The Long Term Resource Monitoring (LTRM) element of the Upper Mississippi River Restoration program employs a harvest method for sampling submersed aquatic vegetation (SAV) whereby a rake is dragged ~1.5 m over the substrate and plant materials are retrieved. “Plant density” (PD) scores indicate SAV abundance and are based on the amount of plant material collected on the teeth of the rake. Standard PD scores are ordered, whole numbers from 0 (no SAV on the rake) to 5 (80-100% of rake teeth full) and are assigned at each subsite for all species combined and for each individual species.
In LTRM monitoring between 1998 and 2018, ~73% of non-zero, all-species-combined PD scores were 1s, and ~89% of individual SAV species were 1s. The preponderance of PD = 1 scores along with the wide range of fresh mass represented by PD = 1 (quantified in Drake and Lund 2020) limits inference about SAV abundance from LTRM monitoring data.
Field personnel noted that small plant fragments comprised a substantial fraction of PD = 1 observations and proposed a modification of the existing LTRM methods where PD = 1 was subdivided to include “trace” scores to represent such small fragments. Trace was defined as PD = 0.08, indicating a maximum of 1 of 13 gaps in the sampling rake filled to the level of an original PD = 1. Amounts of plant material greater than PD = 0.08 and up to the original score of 1 were defined PD = +1. This study used field data collected in 2018 (scoring and fresh weights of scored plant materials) from 136 vegetated sites in Pools 4, 8 and 13 to evaluate the proposed subdivision and to examine among-pool differences in PD data. In the study data, 33% of all-species-combined observations and 69% of species (grouped by morphology) that would previously have received a score of 1 were classified as PD = 0.08. PD scores of 0.08, +1, and 2-3 represented statistically distinct amounts of fresh mass in rake samples. There were systematic differences in the mass of SAV reflected by PD score based on plant morphology and species composition. The mean fresh mass of plant materials assigned a given PD score varied among the three pools, suggesting bias attributable to personnel. To reduce this bias in future data collection efforts, the field crews incorporated a calibration of plant density scores in annual field training. The results presented here describe how including a trace PD score in LTRM data collection improves the description of SAV abundance and consequently estimates of biomass from those PD scores. LTRM vegetation crews have recorded trace scores in annual sampling since 2019 as extra information (i.e. which does not change the LTRM data stream as 0.08 and +1 scores can still be combined for PD=1). Trace data are not currently available to outside users through the LTRM data browser but are available from vegetation component personnel upon request.
","language":"English","publisher":"U.S. Army Corps of Engineers, Upper Mississippi River Restoration Program","usgsCitation":"Drake, D.C., Lund, E., and Bales, K., 2021, Evaluation of a “trace” plant density score in LTRM vegetation monitoring: Long Term Resource Monitoring Technical Report LTRM-2018BI03a, 32 p.","productDescription":"32 p.","ipdsId":"IP-106633","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391319,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2021/drake_a_2021.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Lowenberg, Carol 0000-0002-2961-6808","orcid":"https://orcid.org/0000-0002-2961-6808","contributorId":221012,"corporation":false,"usgs":true,"family":"Lowenberg","given":"Carol","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825693,"contributorType":{"id":2,"text":"Editors"},"rank":0}],"authors":[{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":826584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Kyle","contributorId":267952,"corporation":false,"usgs":false,"family":"Bales","given":"Kyle","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825692,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225695,"text":"70225695 - 2021 - Lagged wetland CH4 flux response in a historically wet year","interactions":[],"lastModifiedDate":"2021-11-03T12:52:20.561946","indexId":"70225695","displayToPublicDate":"2021-10-25T07:51:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Lagged wetland CH4 flux response in a historically wet year","docAbstract":"While a stimulating effect of plant primary productivity on soil carbon dioxide (CO2) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH4) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH4 emissions (FCH4) is complicated by confounding influences of water table level and temperature on CH4 production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH4 at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US-Los) and Allequash Creek (US-ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US-Los being larger in scale and having a more open canopy. Co-located sites with multi-year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH4 without interference due to differing climate. Daily average FCH4 from US-Los reached a maximum of 47.7 ηmol CH4 m−2 s−1 during the study period, while US-ALQ was more than double at 117.9 ηmol CH4 m−2 s−1. The lagged influence of GPP on temperature-normalized FCH4 (Tair-FCH4) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH4 at US-ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH4 due to this study has implications for climate modeling and more accurate carbon budgeting.
NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.
Baseflow is critical to sustaining streamflow in the Upper Colorado River Basin. Therefore, effective water resources management requires estimates of baseflow response to climatic changes. This study provides the first estimates of projected baseflow changes from historical (1984 – 2012) to thirty-year periods centered around 2030, 2050, and 2080 under warm/wet, median, and hot/dry climatic conditions using a hybrid statistical-deterministic baseflow model. Total baseflow supplied to the Lower Colorado River Basin may decline by up to 33%, although this value may increase in the near future by 6% under warm/wet conditions. The percentage of baseflow lost during in-stream transport is projected to increase by 1 - 5% relative to historical conditions. Results highlight that climate driven changes in high elevation hydrology have impacts on basin-wide water availability. Study results have implications for human and ecological water availability in one of the most heavily managed watersheds in the world.
Timber harvesting can influence headwater streams by altering stream productivity, with cascading effects on the food web and predators within, including stream salamanders. Although studies have examined shifts in salamander occupancy or abundance following timber harvest, few examine sublethal effects such as changes in growth and demography. To examine the effect of upland harvesting on growth of the stream-associated Ouachita dusky salamander (Desmognathus brimleyorum), we used capture–mark–recapture over three years at three headwater streams embedded in intensely managed pine forests in west-central Arkansas. The pine stands surrounding two of the streams were harvested, with retention of a 14- and 21-m-wide forested stream buffer on each side of the stream, whereas the third stream was an unharvested control. At the two treatment sites, measurements of newly metamorphosed salamanders were on average 4.0 and 5.7 mm larger post-harvest compared with pre-harvest. We next assessed the influence of timber harvest on growth of post-metamorphic salamanders with a hierarchical von Bertalanffy growth model that included an effect of harvest on growth rate. Using measurements from 839 individual D. brimleyorum recaptured between 1 and 6 times (total captures, n = 1229), we found growth rates to be 40% higher post-harvest. Our study is among the first to examine responses of individual stream salamanders to timber harvesting, and we discuss mechanisms that may be responsible for observed shifts in growth. Our results suggest timber harvest that includes retention of a riparian buffer (i.e., streamside management zone) may have short-term positive effects on juvenile stream salamander growth, potentially offsetting negative sublethal effects associated with harvest.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8238","usgsCitation":"Guzy, J.C., Halstead, B., Halloran, K.M., Homyack, J.A., and Willson, J.D., 2021, Increased growth rates of stream salamanders following forest harvesting: Ecology and Evolution, v. 11, no. 24, p. 17723-17733, https://doi.org/10.1002/ece3.8238.","productDescription":"11 p.","startPage":"17723","endPage":"17733","ipdsId":"IP-127689","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450369,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8238","text":"External Repository"},{"id":397302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Howard County","otherGeospatial":"Ouachita Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.306640625,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 34.384246040152185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","noUsgsAuthors":false,"publicationDate":"2021-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halloran, Kelly M.","contributorId":288948,"corporation":false,"usgs":false,"family":"Halloran","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homyack, Jessica A.","contributorId":288949,"corporation":false,"usgs":false,"family":"Homyack","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":56610,"text":"Weyerhaeuser Company","active":true,"usgs":false}],"preferred":false,"id":838480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willson, John D.","contributorId":288952,"corporation":false,"usgs":false,"family":"Willson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225585,"text":"70225585 - 2021 - Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","interactions":[],"lastModifiedDate":"2021-10-26T14:15:05.326145","indexId":"70225585","displayToPublicDate":"2021-10-23T09:13:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","docAbstract":"Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13214266","usgsCitation":"Fischbach, A., and Douglas, D.C., 2021, Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout: Remote Sensing, v. 13, no. 21, 4266, 19 p., https://doi.org/10.3390/rs13214266.","productDescription":"4266, 19 p.","ipdsId":"IP-131033","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450373,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13214266","text":"Publisher Index Page"},{"id":436135,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2UL7N","text":"USGS data release","linkHelpText":"Walrus Haulout Outlines Apparent from Satellite Imagery Near Point Lay Alaska, Autumn 2018-2020"},{"id":390960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Point Lay haulout area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.19366455078125,\n 69.33674271476097\n ],\n [\n -162.9986572265625,\n 69.6121624754292\n ],\n [\n -162.542724609375,\n 69.96796725849453\n ],\n [\n -162.48504638671875,\n 70.00368818988092\n ],\n [\n -162.73223876953122,\n 70.03372158435194\n ],\n [\n -163.289794921875,\n 69.70286804851057\n ],\n [\n -163.36669921875,\n 69.47778343567616\n ],\n [\n -163.3447265625,\n 69.337711892853\n ],\n [\n -163.19366455078125,\n 69.33674271476097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"21","noUsgsAuthors":false,"publicationDate":"2021-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":825688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":825689,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240302,"text":"70240302 - 2021 - Offspring of translocated individuals drive the successful reintroduction of Columbian Sharp-tailed Grouse in Nevada, USA","interactions":[],"lastModifiedDate":"2023-02-03T16:08:20.075162","indexId":"70240302","displayToPublicDate":"2021-10-22T09:52:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Offspring of translocated individuals drive the successful reintroduction of Columbian Sharp-tailed Grouse in Nevada, USA","docAbstract":"Translocations of North American prairie-grouse (genus Tympanuchus) present a conservation paradox wherein they are performed to augment, restore, or reintroduce populations, but translocated individuals exhibit a diminished ability to contribute to population restoration. For reintroduced populations without immigration, persistence can only be achieved through reproductive contributions by translocated individuals and their progeny. Due to the disruptive nature of translocation (e.g., physiological chronic stress), progeny produced at restoration sites may outperform founder populations in terms of demographics, but this hypothesis has yet to be tested. We reintroduced Columbian Sharp-tailed Grouse (T. phasianellus columbianus; CSTG) to north central Nevada from 2013 to 2017 and used integrated population models (IPMs) to evaluate the process of population establishment and estimate latent contributions of progeny hatched at the restoration site to population rate of change (
Alluvial aquifers in Iowa have more wells with nitrate exceeding drinking-water standards than other aquifers; are susceptible to contamination by organic contaminants; and have high concentrations of naturally occurring iron and manganese in depositional areas that contain abundant organic matter. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, Iowa, studied the Cedar River alluvial aquifer in Linn County, Iowa, from 1990 to 2019 to understand the effect of municipal pumping on spatial and temporal hydrologic and water-quality variability. The Cedar River alluvial aquifer is the source of water for the city of Cedar Rapids, Iowa. Withdrawal of large quantities of water for municipal and industrial supply has altered the normal flow of water in the alluvial aquifer. Pumping induces flow from the Cedar River and the underlying bedrock aquifer into the alluvial aquifer.
Water quality in the alluvial aquifer varies along the Cedar River. Changes in nitrate, ammonia, manganese, and iron in the alluvial aquifer are seen as the upstream free-flowing reach of the Cedar River transitions to a partially regulated downstream reach, likely because of differences in reduction-oxidation conditions in the aquifer, which are controlled by infiltration from the Cedar River under normal conditions and when wells are being pumped. Nitrate, normally found in oxygenated environments, had the highest concentrations in the most upstream wells in the Seminole well field and the lowest concentrations in the most downstream wells in the East well field. In contrast, ammonia, manganese, and iron, normally found in greatest abundance in anoxic (reducing) conditions, had the greatest concentrations in the most downstream wells. Additionally, dissolved nitrate plus nitrite nitrogen concentrations in wells were substantially less and manganese concentrations were greater in production wells near backwater wetlands in contrast to wells near the Cedar River.
Temporal variability in water quality in the alluvial aquifer was driven by pumping that increased flow from the Cedar River into the alluvial aquifer and ultimately led to changes in reduction-oxidation conditions of the aquifer. Increasing dissolved nitrate plus nitrite nitrogen concentrations in the Cedar River from 1990 to 2019 were mirrored in the alluvial aquifer. Anoxic conditions are prevalent in the alluvial aquifer next to the Cedar River when the aquifer is not under pumping stress. However, production well pumping caused induced infiltration of oxygenated river water into the aquifer resulting in increased dissolved nitrate plus nitrite nitrogen concentrations and pesticides and decreased naturally occurring dissolved iron and manganese.
Hydrologic and water-quality conditions in the Cedar River alluvial aquifer from 1990 to 2019 provide baseline conditions needed to evaluate the effects of current and future nutrient reduction efforts and land-use changes in the Cedar River Basin on water quality of the Cedar River alluvial aquifer and its source water, the Cedar River. This summary and analysis provide information that can assist the City of Cedar Rapids Utilities Water Department in managing groundwater resources, and provides information that could be used develop a groundwater-quality model to characterize variability over larger areas of the alluvial aquifer, allowing water providers to plan for future water needs of their users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215110","usgsCitation":"Kalkhoff, S.J., 2021, Hydrologic and water-quality conditions in the Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019: U.S. Geological Survey Scientific Investigations Report 2021–5110, 61 p., https://doi.org/10.3133/sir20215110.","productDescription":"Report: ix, 61 p.; Data Release; Dataset","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-121189","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5110/coverthb.jpg"},{"id":390748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5110/sir20215110.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5110"},{"id":390749,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7VKOU","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic and water quality data from the Cedar River and Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019"},{"id":390750,"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":"Iowa","county":"Linn County","otherGeospatial":"Cedar River Alluvial Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3649,42.2964],[-91.3651,42.2082],[-91.3653,42.1215],[-91.3661,42.0343],[-91.3669,41.948],[-91.3677,41.8603],[-91.4836,41.8608],[-91.5989,41.8612],[-91.716,41.862],[-91.8318,41.8617],[-91.8329,41.9485],[-91.8338,42.0366],[-91.8342,42.1242],[-91.8328,42.2087],[-91.8319,42.2987],[-91.7153,42.2971],[-91.5969,42.2959],[-91.4809,42.296],[-91.3649,42.2964]]]},\"properties\":{\"name\":\"Linn\",\"state\":\"IA\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030H","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J., Anderson, C., Stensaas, G.L., and Kim, M., 2021, System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor (ver. 1.2, December 2024), chap. H of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 20 p., https://doi.org/10.3133/ofr20211030H.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126659","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":433262,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/h/versionHist.txt","text":"Version History","size":"2.07 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390427,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/h/images"},{"id":390426,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.xml","size":"75.7 kB","linkFileType":{"id":8,"text":"xml"}},{"id":390425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–H"},{"id":390424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/h/coverthb4.jpg"},{"id":464526,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030H/full"}],"edition":"Version 1.0: October 21, 2021; Version 1.1: August 29, 2024; Version 1.2: December 2, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many applications in wildlife management require knowledge of the sex of individual animals. The Yuma Ridgway's rail Rallus obsoletus yumanensis is an endangered marsh bird with monomorphic plumage and secretive behaviors, thereby complicating sex determination in field studies. We collected morphometric measurements from 270 adult Yuma Ridgway's rails and quantified the plumage and mandible color of 91 of those individuals throughout their geographic range to evaluate intersexual differences in morphology and coloration. We genetically sexed a subset of adult Yuma Ridgway's rails (n = 101) and used these individuals to determine the optimal combination of measurements (based on discriminant function analyses) to distinguish between sexes. Males averaged significantly larger than females in all measurements, and the optimal discriminant function contained whole leg, culmen, and tail measurements and classified correctly 97.8% (95% CI: 92.5–100.0%) of genetically sexed individuals. We used two additional functions that classified correctly ≥ 95.6% of genetically sexed Yuma Ridgway's rails to assign sex to individuals with missing measurements. These simple models provide managers and researchers with a practical tool to determine the sex of Yuma Ridgway's rails based on morphometric measurements. Although color measurements were not in the most accurate discriminant functions, we quantified subtle intersexual differences in the color of mandibles and greater coverts of Yuma Ridgway's rails. These results document sex-specific patterns in coloration that allow future researchers to test hypotheses to determine the mechanisms underlying sex-based differences in plumage coloration.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-095","usgsCitation":"Conway, C.J., Harrity, E.J., and Michael, L.E., 2021, Sexual dimorphism in morphology and plumage of endangered Yuma Ridgway’s Rails: A model for documenting sex: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 464-474, https://doi.org/10.3996/JFWM-20-095.","productDescription":"11 p.","startPage":"464","endPage":"474","ipdsId":"IP-126314","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-095","text":"Publisher Index Page"},{"id":397003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.8505859375,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 32.38923910985902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrity, E. J.","contributorId":288332,"corporation":false,"usgs":false,"family":"Harrity","given":"E.","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael, L. E.","contributorId":288333,"corporation":false,"usgs":false,"family":"Michael","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225518,"text":"70225518 - 2021 - Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2021-10-20T15:36:49.819571","indexId":"70225518","displayToPublicDate":"2021-10-20T10:26:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","docAbstract":"Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224031,"text":"ofr20211089 - 2021 - Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","interactions":[],"lastModifiedDate":"2021-10-20T14:18:57.158711","indexId":"ofr20211089","displayToPublicDate":"2021-10-20T10:20:00","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":"2021-1089","displayTitle":"Managed Aquifer Recharge Suitability—Regional Screening and Case Studies in Jordan and Lebanon","title":"Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","docAbstract":"The U.S. Geological Survey, at the request of the U.S. Agency for International Development, led a 5-year regional project to develop and apply methods for water availability and suitability mapping for managed aquifer recharge (MAR) in the Middle East and North Africa region. A regional model of surface runoff for the period from 1984 to 2015 was developed to characterize water availability using remote sensing data on climate, vegetation, and topography in Jordan, Lebanon, and surrounding areas. Surface runoff was accumulated to characterize potential streamflow available for MAR and these data were combined with land surface slope to prepare a regional screening map of MAR suitability, illustrating suitability mapping concepts and methods. The application of the methods is demonstrated by the evaluation of water availability and suitability for potential MAR in study areas in Jordan and Lebanon. Locations suitable for MAR are present in both Jordan and Lebanon, but limitations exist in both countries, related primarily to water availability in Jordan and land areas of suitable terrain in Lebanon. An additional feasibility study including field investigations would likely provide decision makers with essential information for further development of the use of MAR in Jordan, Lebanon, and the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211089","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Goode, D.J., ed., 2021, Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon: U.S. Geological Survey Open-File Report 2021–1089, 87 p., https://doi.org/10.3133/ofr20211089.","productDescription":"Report: xi, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124064","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436143,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":390660,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"- Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":389216,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971ZVHF","text":"USGS data release","linkHelpText":"Assembly of satellite-based rainfall datasets in situ data and rainfall climatology contours for the MENA region"},{"id":389217,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TXLT1X","text":"USGS data release","linkHelpText":"Modeling accumulated surface runoff and water availability for aquifer storage and recovery in the MENA region from 1984–2015"},{"id":389215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1089/ofr20211089.pdf","text":"Report","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1089"},{"id":389214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1089/coverthb.jpg"}],"country":"Jordan, Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[35.54567,32.39399],[35.71992,32.70919],[36.83406,32.31294],[38.79234,33.37869],[39.19547,32.16101],[39.00489,32.01022],[37.00217,31.50841],[37.99885,30.5085],[37.66812,30.33867],[37.50358,30.00378],[36.74053,29.86528],[36.50121,29.50525],[36.06894,29.19749],[34.95604,29.35655],[34.9226,29.50133],[35.42092,31.10007],[35.39756,31.48909],[35.54525,31.7825],[35.54567,32.39399]]],[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]]},\"properties\":{\"name\":\"Jordan\"}}]}","contact":"U.S. Geological Survey
Office of International Programs
917 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
directoroip@usgs.gov
Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken (Tympanuchus pallidicinctus), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act) was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictor layers (2016 land cover and cleaned/ updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat which could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0256633","usgsCitation":"Jarnevich, C.S., Belamaric, P.N., Fricke, K., Houts, M., Rossi, L., Beauprez, G.M., Cooper, B., and Martin, R., 2021, Challenges in updating habitat suitability models: An example with the lesser prairie-chicken: PLoSOne, v. 16, no. 9, e0256633, 19 p., https://doi.org/10.1371/journal.pone.0256633.","productDescription":"e0256633, 19 p.","ipdsId":"IP-120427","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256633","text":"Publisher Index Page"},{"id":436145,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MS0QR0","text":"USGS data release","linkHelpText":"Second Iteration of Range Wide Lesser Prairie Chicken Lek Habitat Suitability in 2019, Predicted in Southern Great Plains"},{"id":390673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64453124999999,\n 31.98944183792288\n ],\n [\n -101.2060546875,\n 31.98944183792288\n ],\n [\n -101.22802734375,\n 34.34343606848294\n ],\n [\n -99.97558593749999,\n 34.361576287484176\n ],\n [\n -96.328125,\n 36.98500309285596\n ],\n [\n -96.328125,\n 40.01078714046552\n ],\n [\n -102.10693359375,\n 40.027614437486655\n ],\n [\n -102.12890625,\n 39.62261494094297\n ],\n [\n -104.0625,\n 39.67337039176558\n ],\n [\n -103.11767578124999,\n 37.03763967977139\n ],\n [\n -103.07373046875,\n 36.4566360115962\n ],\n [\n -105.62255859375,\n 34.65128519895413\n ],\n [\n -105.64453124999999,\n 31.98944183792288\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":825400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belamaric, Pairsa Nicole 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":267846,"corporation":false,"usgs":true,"family":"Belamaric","given":"Pairsa","email":"","middleInitial":"Nicole","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":47756,"text":"Student contractor to the U.S. Geological Survey Fort Collins Science Center","active":true,"usgs":false}],"preferred":true,"id":825401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Kent","contributorId":267847,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent","affiliations":[{"id":40289,"text":"Kansas Department of Wildlife, Parks, and Tourism","active":true,"usgs":false}],"preferred":false,"id":825402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houts, Mike","contributorId":267848,"corporation":false,"usgs":false,"family":"Houts","given":"Mike","email":"","affiliations":[{"id":33109,"text":"Kansas Biological Survey, Lawrence, KS","active":true,"usgs":false},{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":825403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rossi, Liza","contributorId":267849,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":825404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beauprez, Grant M.","contributorId":172889,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":825405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Brett","contributorId":267850,"corporation":false,"usgs":false,"family":"Cooper","given":"Brett","email":"","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":825406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":825471,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229154,"text":"70229154 - 2021 - Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","interactions":[],"lastModifiedDate":"2022-03-01T15:14:30.323759","indexId":"70229154","displayToPublicDate":"2021-10-20T09:14:18","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","docAbstract":"A concentrated animal feeding operation (CAFO) established in Newton County, Arkansas, near Big Creek, a tributary of the Buffalo National River, raised concern about potential degradation of water quality in the karst watershed. In this study, isotopic tools were combined with standard geochemical approaches to characterize nutrient sources and dynamics in the Big Creek watershed. An isotopic and geochemical reference database of potential nutrient sources in the Big Creek watershed was constructed based on samples collected from representative potential sources. Nutrient sources and stream samples were analyzed for delta (δ)15N-NO3, δ18O NO3, and a suite of selected dissolved ions. Data provide evidence of modification of potential local nutrient source signatures by nitrification, atmospheric deposition, evaporation, and denitrification. Samples taken from the CAFO waste pond, a septic system, field and parking lot runoff, fertilizer, and hog manure exhibited different δ15N-NO3 and δ18O-NO3 values as compared to stream samples. Stream δ15N-NO3 and δ18O-NO3 values cannot be explained by direct input of any one of these potential sources without modification of the isotopic composition by mixing or fractionation. Big Creek nitrate isotope values (-3.4 per mil [‰] to 6.7‰ δ15N-NO3 and -7.6 to 9.1‰ δ18O-NO3) were similar to values expected from nitrification of nitrogen stored in soils sampled in the watershed (2.8 to 7.6‰ δ15N-NO3 and 3.4 to 4.8‰ δ18O-NO3).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"conferenceTitle":"2020 KIG workshop","conferenceDate":"October 19-20, 2021","conferenceLocation":"Online","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Sokolosky, K., and Hays, P.D., 2021, Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas, in U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021, v. 8, Online, October 19-20, 2021, p. 54-63.","productDescription":"10 p.","startPage":"54","endPage":"63","ipdsId":"IP-117025","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":396600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/sir20205019"},{"id":396602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Big Creek, Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6307373046875,\n 36.50522086338427\n ],\n [\n -94.4549560546875,\n 35.40696093270201\n ],\n [\n -91.7578125,\n 35.420391545750746\n ],\n [\n -91.768798828125,\n 36.50522086338427\n ],\n [\n -94.6307373046875,\n 36.50522086338427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":836807,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Spangler, Lawrence E. 0000-0003-3928-8809 spangler@usgs.gov","orcid":"https://orcid.org/0000-0003-3928-8809","contributorId":973,"corporation":false,"usgs":true,"family":"Spangler","given":"Lawrence","email":"spangler@usgs.gov","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836808,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Sokolosky, Kelly","contributorId":287479,"corporation":false,"usgs":false,"family":"Sokolosky","given":"Kelly","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":836794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hays, Phillip D. 0000-0001-5491-9272 pdhays@usgs.gov","orcid":"https://orcid.org/0000-0001-5491-9272","contributorId":4145,"corporation":false,"usgs":true,"family":"Hays","given":"Phillip","email":"pdhays@usgs.gov","middleInitial":"D.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836795,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100925","usgsCitation":"DeSimone, L.A., and Ransom, K.M., 2021, Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning: Journal of Hydrology: Regional Studies, v. 37, 100925, 20 p., https://doi.org/10.1016/j.ejrh.2021.100925.","productDescription":"100925, 20 p.","ipdsId":"IP-126500","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450397,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100925","text":"Publisher Index Page"},{"id":436146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M64CD1","text":"USGS data release","linkHelpText":"Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA"},{"id":390662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York, North Carolina, Pennsylvania, Virginia","city":"Baltimore, New York, Philadelphia, Richmond, Washington D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.1142578125,\n 41.22824901518529\n ],\n [\n -73.63037109375,\n 40.94671366508002\n ],\n [\n -75.816650390625,\n 39.78321267821705\n ],\n [\n -78.321533203125,\n 38.30718056188316\n ],\n [\n -78.079833984375,\n 35.200744801724014\n ],\n [\n -77.069091796875,\n 35.06597313798418\n ],\n [\n -76.80541992187499,\n 34.94899072578227\n ],\n [\n -76.475830078125,\n 35.092945313732635\n ],\n [\n -76.387939453125,\n 35.34425514918409\n ],\n [\n -76.036376953125,\n 35.29943548054545\n ],\n [\n -75.509033203125,\n 35.84453450421662\n ],\n [\n -75.882568359375,\n 36.43012234551576\n ],\n [\n -75.904541015625,\n 37.055177106660814\n ],\n [\n -75.860595703125,\n 37.28279464911045\n ],\n [\n -75.201416015625,\n 38.07404145941957\n ],\n [\n -74.893798828125,\n 38.496593518947584\n ],\n [\n -75.289306640625,\n 39.16414104768742\n ],\n [\n -74.94873046875,\n 39.138581990583525\n ],\n [\n -75.069580078125,\n 38.976492485539396\n ],\n [\n -74.87182617187499,\n 38.865374851611634\n ],\n [\n -74.124755859375,\n 39.715638134796336\n ],\n [\n -73.883056640625,\n 40.38839687388361\n ],\n [\n -74.058837890625,\n 40.50544628405211\n ],\n [\n -71.74072265625,\n 40.98819156349393\n ],\n [\n -72.1142578125,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226453,"text":"70226453 - 2021 - Incorporation of uncertainty to improve projections of tidal wetland elevation and carbon accumulation with sea-level rise","interactions":[],"lastModifiedDate":"2021-11-18T12:58:34.617176","indexId":"70226453","displayToPublicDate":"2021-10-20T06:56:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Incorporation of uncertainty to improve projections of tidal wetland elevation and carbon accumulation with sea-level rise","docAbstract":"Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. A threshold analysis revealed that all three wetland sites were likely to eventually transition to an unvegetated state with SLR rates above 7 mm/yr. Our results show the utility in incorporating additional estuary-specific parameters to bolster confidence in model projections. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR.
Historically, Cisco Coregonus artedi and Lake Whitefish Coregonus clupeaformis were abundant throughout the Laurentian Great Lakes, but overharvest, habitat degradation, and interactions with exotic species caused most populations to collapse by the mid-1900s. Strict commercial fishery regulations and improved environmental and ecological conditions allowed Cisco to partially recover only in Lake Superior, whereas Lake Whitefish recovered in all the upper Great Lakes (Superior, Michigan, and Huron). The differential responses of Cisco and Lake Whitefish to improved environmental and ecological conditions in lakes Michigan and Huron have led to questions about potential negative interactions between these species. To provide context for fishery managers, we tested for positive and negative correlations between historical (1929–1970) Cisco and Lake Whitefish commercial gill net catch per effort (CPE; kg/km of net) at a variety of spatial scales in Michigan waters of the upper Great Lakes. The three best-fit spatial models—LAKEWIDE, REGIONAL 10, and SIMPLE—all had similar levels of support (scaled second-order Akaike Information Criterion < 3.0), and we used these models to determine whether there was a significant correlation between Cisco and Lake Whitefish CPE (positive and negative). There was either no correlation between Cisco and Lake Whitefish CPE or a positive correlation for most (12 of 13) pairwise (Cisco–Lake Whitefish) comparisons. We identified no strong positive or negative correlations in the lakewide (LAKEWIDE) or reduced (SIMPLE) models. In the regional model (REGIONAL 10), we identified strong and positive correlations between Cisco and Lake Whitefish CPE in two regions (ρ = 0.59–0.71) and a weak negative correlation in one region (ρ = −0.45). Collectively, our findings suggest that Cisco and Lake Whitefish CPE were largely independent of each other; thus, these species likely did not interact to the detriment of one another in Michigan waters of the upper Great Lakes during 1929–1970.
Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.
We aimed to disentangle the patterns of synchronous and variable cone production (i.e. masting) and its relationship to climate in two conifer species native to dry forests of western North America. We used cone abscission scars to reconstruct ca 15 years of recent cone production in Pinus edulis and Pinus ponderosa, and used redundancy analysis to relate time series of annual cone production to climate indices describing the North American monsoon and the El Niño Southern Oscillation (ENSO). We show that the sensitivity to climate and resulting synchrony in cone production varies substantially between species. Cone production among populations of P. edulis was much more spatially synchronous and more closely related to large-scale modes of climate variability than among populations of P. ponderosa. Large-scale synchrony in P. edulis cone production was associated with the North American monsoon and we identified a dipole pattern of regional cone production associated with ENSO phase. In P. ponderosa, these climate indices were not strongly associated with cone production, resulting in asynchronous masting patterns among populations. This study helps frame our understanding of mast seeding as a life-history strategy and has implications for our ability to forecast mast years in these species.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0378","usgsCitation":"Wion, A., Pearse, I., Rodman, K., Veblen, T.T., and Redmond, M., 2021, The effects of ENSO and the North American monsoon on mast seeding in two Rocky Mountain conifer species: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0378.","ipdsId":"IP-126312","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450421,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":391740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wion, Andreas","contributorId":225092,"corporation":false,"usgs":false,"family":"Wion","given":"Andreas","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodman, Kyle C.","contributorId":238090,"corporation":false,"usgs":false,"family":"Rodman","given":"Kyle C.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":826733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Veblen, Thomas T.","contributorId":192285,"corporation":false,"usgs":false,"family":"Veblen","given":"Thomas","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":826734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Redmond, Miranda D.","contributorId":225094,"corporation":false,"usgs":false,"family":"Redmond","given":"Miranda","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226183,"text":"70226183 - 2021 - The ecology and evolution of synchronized reproduction in long-lived plants","interactions":[],"lastModifiedDate":"2021-11-16T12:56:10.688362","indexId":"70226183","displayToPublicDate":"2021-10-18T06:54:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The ecology and evolution of synchronized reproduction in long-lived plants","docAbstract":"Populations of many long-lived plants exhibit spatially synchronized seed production that varies extensively over time, so that seed production in some years is much higher than on average, while in others, it is much lower or absent. This phenomenon termed masting or mast seeding has important consequences for plant reproductive success, ecosystem dynamics and plant–human interactions. Inspired by recent advances in the field, this special issue presents a series of articles that advance the current understanding of the ecology and evolution of masting. To provide a broad overview, we reflect on the state-of-the-art of masting research in terms of underlying proximate mechanisms, ontogeny, adaptations, phylogeny and applications to conservation. While the mechanistic drivers and fitness consequences of masting have received most attention, the evolutionary history, ontogenetic trajectory and applications to plant–human interactions are poorly understood. With increased availability of long-term datasets across broader geographical and taxonomic scales, as well as advances in molecular approaches, we expect that many mysteries of masting will be solved soon. The increased understanding of this global phenomenon will provide the foundation for predictive modelling of seed crops, which will improve our ability to manage forests and agricultural fruit and nut crops in the Anthropocene.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0369","usgsCitation":"Pesendorfer, M.B., Ascoli, D., Bogdziewicz, M., Hacket-Pain, A., Pearse, I., and Vacchiano, G., 2021, The ecology and evolution of synchronized reproduction in long-lived plants: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0369.","ipdsId":"IP-130378","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2020.0369","text":"Publisher Index Page"},{"id":391738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pesendorfer, Mario B.","contributorId":201187,"corporation":false,"usgs":false,"family":"Pesendorfer","given":"Mario","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":826744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ascoli, Davide","contributorId":224289,"corporation":false,"usgs":false,"family":"Ascoli","given":"Davide","email":"","affiliations":[{"id":40848,"text":"University of Torino","active":true,"usgs":false}],"preferred":false,"id":826745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bogdziewicz, Michal","contributorId":256849,"corporation":false,"usgs":false,"family":"Bogdziewicz","given":"Michal","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":826746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hacket-Pain, Andrew","contributorId":224290,"corporation":false,"usgs":false,"family":"Hacket-Pain","given":"Andrew","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":826747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826743,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vacchiano, Giorgio","contributorId":224295,"corporation":false,"usgs":false,"family":"Vacchiano","given":"Giorgio","email":"","affiliations":[{"id":40851,"text":"University of Milan","active":true,"usgs":false}],"preferred":false,"id":826748,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225673,"text":"70225673 - 2021 - Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States","interactions":[],"lastModifiedDate":"2021-11-02T11:54:43.920548","indexId":"70225673","displayToPublicDate":"2021-10-18T06:51:54","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":"Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States","docAbstract":"Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R2 of 0.83) and hold-out (R2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer, and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.
There is evidence that variable and synchronous reproduction in seed plants (masting) correlates to modes of climate variability, e.g. El Niño Southern Oscillation and North Atlantic Oscillation. In this perspective, we explore the breadth of knowledge on how climate modes control reproduction in major masting species throughout Earth's biomes. We posit that intrinsic properties of climate modes (periodicity, persistence and trends) drive interannual and decadal variability of plant reproduction, as well as the spatial extent of its synchrony, aligning multiple proximate causes of masting through space and time. Moreover, climate modes force lagged but in-phase ecological processes that interact synergistically with multiple stages of plant reproductive cycles. This sets up adaptive benefits by increasing offspring fitness through either economies of scale or environmental prediction. Community-wide links between climate modes and masting across plant taxa suggest an evolutionary role of climate variability. We argue that climate modes may ‘bridge’ proximate and ultimate causes of masting selecting for variable and synchronous reproduction. The future of such interaction is uncertain: processes that improve reproductive fitness may remain coupled with climate modes even under changing climates, but chances are that abrupt global warming will affect Earth's climate modes so rapidly as to alter ecological and evolutionary links.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0380","usgsCitation":"Ascoli, D., Hacket-Pain, A., Pearse, I.S., Vacchiano, G., Corti, S., and Davini, P., 2021, Modes of climate variability bridge proximate and evolutionary mechanisms of masting: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0380.","ipdsId":"IP-127671","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450429,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2020.0380","text":"External Repository"},{"id":391736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Ascoli, Davide","contributorId":224289,"corporation":false,"usgs":false,"family":"Ascoli","given":"Davide","email":"","affiliations":[{"id":40848,"text":"University of Torino","active":true,"usgs":false}],"preferred":false,"id":826814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hacket-Pain, Andrew","contributorId":224290,"corporation":false,"usgs":false,"family":"Hacket-Pain","given":"Andrew","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":826815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vacchiano, Giorgio","contributorId":224295,"corporation":false,"usgs":false,"family":"Vacchiano","given":"Giorgio","email":"","affiliations":[{"id":40851,"text":"University of Milan","active":true,"usgs":false}],"preferred":false,"id":826817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corti, Susanna","contributorId":268854,"corporation":false,"usgs":false,"family":"Corti","given":"Susanna","email":"","affiliations":[{"id":55694,"text":"Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche","active":true,"usgs":false}],"preferred":false,"id":826818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davini, Paolo","contributorId":268855,"corporation":false,"usgs":false,"family":"Davini","given":"Paolo","email":"","affiliations":[{"id":55694,"text":"Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche","active":true,"usgs":false}],"preferred":false,"id":826819,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}