Information on velocity fields in rivers is critical for designing infrastructure, modeling contaminant transport, and assessing habitat. Although non-contact approaches to measuring flow velocity are well established, these methods assume a stationary imaging platform. This study eliminates this constraint by introducing a framework for moving aircraft river velocimetry (MARV). The workflow takes as input images acquired from an airplane and involves orthorectification, frame overlap analysis, image enhancement, particle image velocimetry (PIV), and aggregation of the resulting velocity vectors onto a prediction grid. We also use new metrics to quantify the agreement between image-derived and field-measured velocity vectors in terms of both orientation and magnitude. The potential of MARV was evaluated using data from two Alaskan rivers: a large, highly turbid channel and its smaller, clearer tributary. Sediment boil vortices on the mainstem provided natural features trackable via PIV and estimated velocities corresponded closely with field measurements (R2 up to 0.911). We compared an exhaustive approach that evaluates overlap for all frame combinations to a simpler rolling window implementation and found that the more efficient algorithm did not compromise accuracy. Sensitivity analysis suggested that the method was robust to window parameterization. Comparing PIV output from different flying heights and imaging systems indicated that larger pixels led to higher accuracy and that a more advanced dual-camera system provided superior performance. Results from the tributary were less encouraging, presumably due to a lack of trackable features in visible images. Testing across a range of rivers is needed to assess the generality of MARV.
Globally, Marine Protected Areas are an important tool in the conservation of large marine vertebrates. Recent studies have highlighted the use of protected areas by imperiled green turtles (Chelonia mydas) in the southern Gulf of Mexico. To identify and characterize inter-nesting, migratory, and foraging areas for green turtles that nest in the northern Gulf of Mexico, we deployed 14 satellite tags on 13 individual green turtles after nesting in Northwest Florida. We used switching state-space modeling to highlight turtle use in the Florida Keys National Marine Sanctuary and in habitat outside of protected areas such as near Cape Sable, Florida and off the Yucatán Peninsula, Mexico. Turtles were tracked for 21–217 days and migrated for a mean of 22 days. Five individuals used stopover sites during migration; these sites were in areas of dense seagrass habitat, often within boundaries of existing Aquatic Preserves. Turtles established mean foraging home ranges of 118.0 km2 (50% kernel density estimate) with foraging centroids that were 0.33–7.3 km apart. The area off Cape Sable, Florida, which lies outside of currently protected area boundaries, appears to be a hotspot for green turtles that nest throughout the Gulf of Mexico. While protected areas in the Gulf of Mexico are used by this subset of nesting green turtles, several key sites remain unprotected. These findings are relevant when considering expansion of currently protected areas and in defining critical habitat for this species.
In the United States and globally, contaminant exposure in unregulated private-well point-of-use tapwater (TW) is a recognized public-health data gap and an obstacle to both risk-management and homeowner decision making. To help address the lack of data on broad contaminant exposures in private-well TW from hydrologically-vulnerable (alluvial, karst) aquifers in agriculturally-intensive landscapes, samples were collected in 2018–2019 from 47 northeast Iowa farms and analyzed for 35 inorganics, 437 unique organics, 5 in vitro bioassays, and 11 microbial assays. Twenty-six inorganics and 51 organics, dominated by pesticides and related transformation products (35 herbicide-, 5 insecticide-, and 2 fungicide-related), were observed in TW. Heterotrophic bacteria detections were near ubiquitous (94 % of the samples), with detection of total coliform bacteria in 28 % of the samples and growth on at least one putative-pathogen selective media across all TW samples. Health-based hazard index screening levels were exceeded frequently in private-well TW and attributed primarily to inorganics (nitrate, uranium). Results support incorporation of residential treatment systems to protect against contaminant exposure and the need for increased monitoring of rural private-well homes. Continued assessment of unmonitored and unregulated private-supply TW is needed to model contaminant exposures and human-health risks.
In the Greater Yellowstone Ecosystem, counts of female grizzly bears (Ursus arctos) with cubs-of-the-year (females with cubs) from systematic aerial surveys and opportunistic ground sightings are combined with demographic data to derive annual population estimates. We addressed 2 limitations to the monitoring approach. As part of a rule set, a conservative distance of >30 km currently is used as a threshold to assign sightings to unique females with cubs, resulting in underestimation bias. Using telemetry locations of females with cubs collected during 1997–2019, we created 1,000 data sets for each of 5 levels of simulated number of females with cubs, simulated sightings by selecting among these locations, and evaluated the classification performance of alternative distance criteria (12–30 km). Under all scenarios, 12–16-km criteria maximized classification performance and minimized estimation bias; the 16-km criterion was optimal for current conditions and sampling efforts. Our second objective was to test generalized additive models (GAMs) as a flexible trend analysis technique. We simulated 1,000 time series for each of 10 scenarios (10, 15, and 20% decline over periods of 5, 10, and 15 yrs, plus stability), applied GAMs, and assessed metrics associated with the posterior distribution of the instantaneous rate of change. We detected declines among >99.6% of replicates under the 15 and 20% decline scenarios and in 84.7–94.7% of replicates under the 10% decline scenario. From decline onset to first detection, periods ranged from 3.7 (20% decline over 5 yrs) to 11.1 (10% decline over 15 yrs), with 3.9–8.8 years mean duration of detection events. The GAM approach allows detection of directional changes in population trend, including early warning metrics, and stabilization after such changes. Retrospective application of the 16-km distance criterion and GAMs resulted in higher population estimates and growth rates than are reported using current methods.
","language":"English","publisher":"International Association for Bear Research and Management","doi":"10.2192/URSUS-D-22-00002.2","usgsCitation":"van Manen, F.T., Ebinger, M., Costello, C., Bjornlie, D., Clapp, J., Thompson, D., Haroldson, M.A., Frey, K.L., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2023, Enhancements to population monitoring of Yellowstone grizzly bears: Ursus, v. 33, e17, 19 p., https://doi.org/10.2192/URSUS-D-22-00002.2.","productDescription":"e17, 19 p.","ipdsId":"IP-137270","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444815,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-22-00002.2","text":"Publisher Index Page"},{"id":411958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":861651,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilmot, 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":861652,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":861653,"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":861654,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hnilicka, Pat","contributorId":289731,"corporation":false,"usgs":false,"family":"Hnilicka","given":"Pat","affiliations":[],"preferred":false,"id":861655,"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":861656,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70239810,"text":"70239810 - 2023 - Watershed- and reach-scale drivers of phosphorus retention and release by streambed sediment in a western Lake Erie watershed during summer","interactions":[],"lastModifiedDate":"2023-01-20T13:09:14.687072","indexId":"70239810","displayToPublicDate":"2023-01-16T07:06:55","publicationYear":"2023","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":"Watershed- and reach-scale drivers of phosphorus retention and release by streambed sediment in a western Lake Erie watershed during summer","docAbstract":"Reducing phosphorus (P) concentrations in aquatic ecosystems, is necessary to improve water quality and reduce the occurrence of harmful cyanobacterial algal blooms. Managing P reduction requires information on the role rivers play in P transport from land to downstream water bodies, but we have a poor understanding of when and where river systems are P sources or sinks. During the summers of 2019 and 2021, we sampled streambed sediment at 78 sites throughout the Maumee River network (a major source of P loads to Lake Erie) focusing on the zero equilibrium P concentration (EPC0), the soluble reactive phosphorus (SRP) concentration at which sediment neither sorbs nor desorbs P. We used structural equation modeling to identify direct and indirect drivers of EPC0. Stream sediment was a P sink at 40 % and 67 % of sites in 2019 and 2021, respectively. During both years, spatial variation in EPC0 was shaped by stream water SRP concentrations, sediment P saturation, and sediment physicochemical characteristics. In turn, SRP concentrations and sediment P saturation (PSR) were influenced by agricultural land use and stream size. Effect of stream size differed among years with stream size having a greater effect on SRP in 2019 and on PSR in 2021. Streambed sediment is currently a net P sink across the sites sampled in the Maumee River network during summer, but sediment at these locations, especially sites in headwater streams, may become a P source if stream water SRP concentrations decrease. Our results improve the understanding of watershed- and reach-scale controls on EPC0 but also indicate the need for further research on how changes in SRP concentration as a result of conservation management implementation influences the role of streambed sediment in P transport to Lake Erie.
In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail (Anas acuta), Green-winged Teal (Anas crecca), and Northern Shoveler (Anas clypeata), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4367","usgsCitation":"Conlisk, E.E., Byrd, K.B., Matchett, E., Lorenz, A., Casazza, M.L., Golet, G.H., Reynolds, M.D., Sesser, K.A., and Reiter, M.E., 2023, Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California: Ecosphere, v. 14, e4367, 19 p., https://doi.org/10.1002/ecs2.4367.","productDescription":"e4367, 19 p.","ipdsId":"IP-144890","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4367","text":"Publisher Index Page"},{"id":412024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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E.","contributorId":301022,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":861775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matchett, Elliott 0000-0001-5095-2884 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A.","contributorId":215294,"corporation":false,"usgs":false,"family":"Sesser","given":"Kristin","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":861782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":861783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239935,"text":"70239935 - 2023 - Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","interactions":[],"lastModifiedDate":"2023-03-28T14:38:56.423278","indexId":"70239935","displayToPublicDate":"2023-01-15T07:09:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","docAbstract":"dictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest-site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest-site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.
Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.
As a part of the Texas Water Development Board groundwater availability modeling program, the U.S. Geological Survey developed the Gulf Coast Land Subsidence and Groundwater-Flow model (hereinafter, the “GULF model”) and ensemble to simulate groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system (the study area) in Texas from predevelopment (1897) through 2018. Since the publication of a previous groundwater model for the greater Houston area in 2012, there have been changes to the distribution of groundwater withdrawals and advances in modeling tools. To reflect these changes and to simulate more recent conditions, the GULF model was developed in cooperation with the Harris-Galveston and Fort Bend Subsidence Districts to provide an updated Groundwater Availability Model.
Since the early 1900s, most of the groundwater withdrawals in the study area have been from three of the hydrogeologic units that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers and, more recently, from the Catahoula confining unit. Withdrawals from these hydrogeologic units are used for municipal supply, commercial and industrial use, and irrigation purposes. Withdrawals of large quantities of groundwater in the greater Houston area have caused widespread groundwater-level declines in the Chicot, Evangeline, and Jasper aquifers of more than 300 feet (ft). Early development of the aquifer system, which began before 1900, resulted in nearly 50 percent of the eventual historical groundwater-level minimums having been reached as early as 1946 in some areas. These groundwater-level declines led to more than 9 ft of land-surface subsidence—historically in central and southeastern Harris County and Galveston County, but more recently in northern, northwestern, and western Harris County, Montgomery County, and northern Fort Bend County—from depressurization and compaction of clay and silt layers interbedded in the aquifer sediments.
In a generalized conceptual model of the Gulf Coast aquifer system, water enters the groundwater system in topographically high outcrops of the hydrogeologic units in the northwestern part of the aquifer system. Groundwater that does not discharge to streams flows to intermediate and deep zones of the aquifer system southeastward of the outcrop areas where it is discharged by wells and by upward leakage in topographically low areas near the coast. The uppermost parts of the aquifer system, which include outcrop areas, are under water-table (unconfined) conditions where the groundwater is not confined under pressure. As depth increases in the aquifer system and interbedded clay and silt layers accumulate, water-table conditions evolve into confined conditions where the groundwater is under pressure.
Groundwater flow and land-surface subsidence in the GULF model and ensemble were simulated by using MODFLOW 6 with the Skeletal Storage, Compaction, and Subsidence package. The model consists of six layers, one for each of the five hydrogeologic units in the northern part of the Gulf Coast aquifer system and a surficial top layer that includes part of each hydrogeologic unit. Transient groundwater flow was simulated during 1897–2018 by using a combination of multiyear, annual, and monthly stress periods. An initial steady-state stress period was configured to represent predevelopment mean annual inflows and outflows. The subsidence package used in the GULF model and ensemble uses a head-based subsidence formulation that simulates the delayed drainage response from clay and silt sediment to changes in groundwater levels.
The GULF model and ensemble were history matched to groundwater-level observations at selected wells, land-surface subsidence at benchmarks, aquifer compaction at borehole extensometers, and vertical displacement from Global Positioning System stations. A Bayesian framework was used to represent uncertainty in modeled parameters and simulated outputs of interest. History matching and uncertainty quantification were performed by using a Monte Carlo approach enabled through iterative ensemble smoother software to produce an ensemble of models fit to historical data. The iterative ensemble smoother substantially reduced the computational demand of parameter estimation by approximating the first-order relation between model inputs and outputs, thereby allowing 183,207 adjustable parameters to be used for history matching at a relatively low computational and time cost.
The history-matched parameter values are within the ranges of previously published values and agree with the current understanding of the spatial and temporal patterns of parameter uncertainty for the Gulf Coast aquifer system. A good agreement between the observed (or estimated) and simulated groundwater levels, land-surface subsidence, compaction, and vertical displacement was obtained across the modeled area based on qualitative and quantitative comparisons. Ensemble mean annual groundwater-flow rates to the Chicot, Evangeline, Jasper aquifers and Catahoula confining unit were 0.0–0.49 inch (in.), 0.09–0.33 in., 0.01–0.07 in., and 0.01–0.05 in., respectively. GULF model mean annual groundwater-flow rates to the Chicot, Evangeline, and Jasper aquifers and Catahoula confining unit were 0.31 in., 0.19 in., 0.03 in., and 0.03 in., respectively.
The GULF-model-simulated recharge to the outcrop area was the largest inflow (75 percent), and recharge to other areas was 25 percent of the model inflow. The simulated outflows included (1) net surface-water/groundwater exchange with study area streams (50 percent), (2) groundwater use (49 percent), and (3) net surface-water/groundwater exchange with the Gulf of Mexico (1 percent). The sum of the simulated values of the outflows (1,041,973 acre-feet per year [acre-ft/yr]) and the elastic expansion of the fine-grained sediment and numerical solver error (339 acre-ft/yr) minus the inflows (654,172 acre-ft/yr) represents the reduction of storage from the Gulf Coast aquifer system (388,140 acre-ft/yr). Most of the storage depletion is caused by the long-term groundwater-level declines that have resulted primarily in inelastic compaction.
The GULF model was used to estimate Jasper aquifer compaction at selected benchmarks in Montgomery County and northern Harris County, which are the primary locations of Jasper aquifer groundwater use. Simulated Jasper aquifer compaction in northern Harris County was between 0.2 and 0.5 ft, or between about 5 and 16 percent of simulated subsidence at the benchmark locations. Simulated Jasper aquifer compaction in Montgomery County was between 0.8 and 1.2 ft, or between about 33 and 57 percent of simulated subsidence at the benchmark locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1877","issn":"ISSN 2330-7102","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District and the Fort Bend Subsidence District","usgsCitation":"Ellis, J.H., Knight, J.E., White, J.T., Sneed, M., Hughes, J.D., Ramage, J.K., Braun, C.L., Teeple, A., Foster, L., Rendon, S.H., and Brandt, J., 2023, Hydrogeology, land-surface subsidence, and documentation of the Gulf Coast Land Subsidence and Groundwater-Flow (GULF) model, southeast Texas, 1897–2018 (ver. 1.1, November 2023): U.S. Geological Survey Professional Paper 1877, 425 p., https://doi.org/10.3133/pp1877.","productDescription":"Report: xx, 425 p., 8 Appendixes; Data Release","numberOfPages":"450","onlineOnly":"Y","ipdsId":"IP-127938","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":500160,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114230.htm","linkFileType":{"id":5,"text":"html"}},{"id":422702,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/pp1877/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":411889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM8A1P","text":"USGS Data Release","linkHelpText":"MODFLOW 6 model and ensemble used in the simulation of groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system, 1897–2018"},{"id":422705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/pp1877/coverthb2.jpg"},{"id":411888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/pp1877/pp1877.pdf","text":"Report","size":"184 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.54245107965883,\n 31.199747848944256\n ],\n [\n -96.33297842340923,\n 30.997489619299927\n ],\n [\n -96.79440420465926,\n 30.136679255787612\n ],\n [\n -96.02536123590899,\n 28.551820525825022\n ],\n [\n -95.36068838434645,\n 28.86498475853952\n ],\n [\n -94.72348135309639,\n 29.28746086219381\n ],\n [\n -94.65207022028405,\n 29.402380282489133\n ],\n [\n -94.23458975153387,\n 29.574516044800063\n ],\n [\n -93.82809561090883,\n 29.670020494605353\n ],\n [\n -93.89401357965892,\n 29.803574466610613\n ],\n [\n -93.6907665093464,\n 30.05113045792723\n ],\n [\n -93.67428701715903,\n 30.307554456695556\n ],\n [\n -93.6687938530968,\n 30.563309394138372\n ],\n [\n -93.49301260309677,\n 30.841976559030968\n ],\n [\n -93.4765331109094,\n 31.077503645282718\n ],\n [\n -93.54245107965883,\n 31.199747848944256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 13, 2023; Version 1.1: November 28, 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
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Groundwater is the primary source of drinking water in the Coachella Valley in the desert region of southern California. Although most people in Coachella Valley are served by public drinking-water systems, about 20,000 people rely on private domestic or small-system wells (referred to herein as domestic wells). Recently, the U.S. Geological Survey (USGS) found that 39 percent of the groundwater resources used by domestic wells in Coachella Valley contained arsenic, fluoride, or both constituents at concentrations greater than the maximum contaminant levels established for public drinking-water systems. Uranium, chromium, nitrate, and perchlorate were detected at moderate concentrations below maximum contaminant levels. Elevated (above background) perchlorate concentrations in some areas indicate that domestic wells may receive recharge from Colorado River water used for irrigation or aquifer replenishment. Moderate total dissolved solids (TDS) concentrations throughout the study area and the co-occurrence of high concentrations of TDS and perchlorate indicates that Colorado River water is a source of recharge in the southeastern Indio groundwater subbasin. Four volatile organic compounds were detected at low concentrations, and pesticides and per- and polyfluoroalkyl substances were not detected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221122","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Soldavini, A.L., Harkness, J.S., Levy, Z.F., and Fram, M.S., 2023, Quality of groundwater used for domestic drinking-water supply in the Coachella Valley, 2020: U.S. Geological Survey Open-File Report 2022-1122, 6 p., https://doi.org/10.3133/ofr20221122.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127493","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411823,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UYXI95","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater-quality data in the Coachella Valley Domestic Supply Aquifer Study Unit, 2020: Results from the California GAMA Priority Basin 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U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
Unit Chief State Water Resources Control Board Division of Water Quality
P.O. Box 2231, Sacramento, CA 95812
Telephone number: (916) 341-5779
Snow Lake, a glacially dammed lake on the Snow Glacier near Seward, Alaska, drains rapidly every 14 months–3 years, causing flooding along the Snow River. Highway, railroad, and utility infrastructure on the lower Snow River floodplain is vulnerable to flood damage. Historical hydrology, geomorphology, and two-dimensional hydraulic and sediment transport modeling were used to assess the flood risks from Snow Lake outburst floods. Floods have become more frequent, peaked more rapidly, and have had generally higher peaks over the last 20 years as the Snow Glacier has thinned, translating to a greater potential for flood damage. Rapidly shifting channel locations and the occasional introduction of large volumes of debris to the river also threaten infrastructure on the floodplain and in the channel. An assessment of the historical channel planform between 1951 and 2019 showed that there have been more and less stable segments along the lower Snow River and that channel migration has generally been toward the east. An analysis of floodplain elevations using 2008 light detection and ranging (lidar) showed that the main channel is relatively high compared to floodplain channels that carry floodwaters along the railroad grade, so that once the main channel banks are overtopped water rapidly disperses throughout the floodplain. A two-dimensional flow and sediment transport model was developed, and its simulation results were compared to three past outburst floods from 2007, 2017, and 2019. Despite the complex floodplain and channel geometry, coarse resolution of the mesh, and sediment input data, the model successfully simulated areas of observed scour along the railroad grade and at the guidebank to the highway bridge. The modeled water-surface elevations generally replicated peak elevations recorded at a streamgage in the middle of the model domain and at pressure transducers installed on the floodplain and main channel, although there were discrepancies on the rising limb and some locations had a poorer fit than others. A model of a hypothetical check flood, approximately 150 percent of the largest recorded outburst flood, was developed to provide hydraulic variables to use when planning for infrastructure upgrades.
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Biodiversity conservation under a changing climate is a challenging endeavor. Landscapes are shifting as a result of climate change and sea level rise but plant communities in particular may not keep up with the pace of change. Predictive ecological models can help decision makers understand how species are likely to respond to change and then adjust management actions to align with desired future conditions. Florida’s Everglades is a wetland ecosystem that is host to many species, including a large number of endangered and endemic species. Everglades ecosystem restoration has been ongoing for decades, but consideration of sea level rise impacts in restoration planning is more recent. Incorporating potential impacts from sea level rise into restoration planning should benefit species and their coastal habitats, most notably at the southern Florida peninsula. The endangered Cape Sable seaside sparrow (Ammospiza maritima mirabilis) occurs in marl prairie habitat at the southern end of the Everglades. The locations of three of its six subpopulations are proximate to the coast. We used a spatially explicit predictive model, EverSparrow, to estimate probability of sparrow presence considering both hydrologic change from restoration and sea level rise. We found that the probability of sparrow presence decreased with increasing sea level rise. Within approximately 50 years, probability of presence significantly decreased for all three coastal subpopulation areas, with areas above 40% probability increasingly limited. Given the exceptionally low dispersal ability of this species and the geographic restrictions for habitat expansion, our results highlight the importance of freshwater flow into the southern Everglades marl prairie for habitat conservation.
The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N:P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N:P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.
","language":"English","publisher":"Springer","doi":"10.1007/s11783-023-1676-2","usgsCitation":"Liang, Z., Xu, Y., Zhao, G., Lu, W., Fu, Z., Wang, S., and Wagner, T., 2023, Approaching the upper boundary of driver-response relationships: Identifying factors using a novel framework integrating quantile regression with interpretable machine learning: Frontiers of Environmental Science & Engineering, v. 17, 76, https://doi.org/10.1007/s11783-023-1676-2.","productDescription":"76","ipdsId":"IP-137079","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2023-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":347986,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Yaoyang","contributorId":347987,"corporation":false,"usgs":false,"family":"Xu","given":"Yaoyang","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":922792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, Gang","contributorId":347988,"corporation":false,"usgs":false,"family":"Zhao","given":"Gang","affiliations":[{"id":30217,"text":"Carnegie Institution for Science","active":true,"usgs":false}],"preferred":false,"id":922793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lu, Wentao","contributorId":347989,"corporation":false,"usgs":false,"family":"Lu","given":"Wentao","affiliations":[{"id":83276,"text":"Institute of Strategic Planning","active":true,"usgs":false}],"preferred":false,"id":922794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fu, Zhenghui","contributorId":347990,"corporation":false,"usgs":false,"family":"Fu","given":"Zhenghui","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Shuhang","contributorId":347991,"corporation":false,"usgs":false,"family":"Wang","given":"Shuhang","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922797,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261552,"text":"70261552 - 2023 - Searching for the Achilles heel(s) for maintaining invertebrate biodiversity across complexes of depressional wetlands","interactions":[],"lastModifiedDate":"2024-12-16T15:26:09.876503","indexId":"70261552","displayToPublicDate":"2023-01-11T09:10:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2142,"text":"Journal for Nature Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Searching for the Achilles heel(s) for maintaining invertebrate biodiversity across complexes of depressional wetlands","docAbstract":"Wetlands are among the most threatened ecosystems worldwide due to climate change and land-use conversion. Regional biodiversity of temporary wetlands is dependent on the existence of habitat complexes with variable hydroperiods. Because temperature and rainfall regimes are predicted to shift globally, together with land-use patterns, different scenarios of wetland loss are expected in the future. To understand how wetland biodiversity might change in the future, it is important to evaluate how the loss of particular hydroperiods will affect overall diversity in a region. Using invertebrate datasets from five wetland complexes distributed across South and North America, we calculated beta diversity metrics for each region. Then we contrasted those metrics to simulations of sequential deletions of subsets (30%) of the long-, moderate- and short-hydroperiod wetlands to assess which wetland class would most affect invertebrate beta diversity in each region. Deletions of the short-hydroperiod wetlands led to the most significant decline in beta diversity. However, deletion effects of different wetland classes varied across study regions, with a negative correlation existing between deletions of the long- and short-hydroperiod wetlands on invertebrate beta diversity. Our simulations indicate that loss of short-hydroperiod wetlands will have the most significant effects on invertebrate beta diversity, but loss of long-hydroperiod wetlands will also be important. Thus, wetlands from both hydroperiod extremes should be considered when assessing potential biodiversity declines associated with habitat loss.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jnc.2023.126332","usgsCitation":"Pires, M.M., Garcia, P.E., Maltchik, L., Stenert, C., Epele, L.B., McLean, K., Kneitel, J., Racey, S., and Batzer, D., 2023, Searching for the Achilles heel(s) for maintaining invertebrate biodiversity across complexes of depressional wetlands: Journal for Nature Conservation, v. 72, 126332, 8 p., https://doi.org/10.1016/j.jnc.2023.126332.","productDescription":"126332, 8 p.","ipdsId":"IP-144845","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jnc.2023.126332","text":"Publisher Index Page"},{"id":465145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pires, Mateus M.","contributorId":347168,"corporation":false,"usgs":false,"family":"Pires","given":"Mateus","email":"","middleInitial":"M.","affiliations":[{"id":83092,"text":"Programa de Pós-Graduação em Biologia de Ambientes Aquáticos Continentais, Universidade Federal do Rio Grande (FURG), Rio Grande, RS, Brazil","active":true,"usgs":false}],"preferred":false,"id":921003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Patricia E.","contributorId":347170,"corporation":false,"usgs":false,"family":"Garcia","given":"Patricia","email":"","middleInitial":"E.","affiliations":[{"id":83093,"text":"Grupo de Ecología de Sistemas Acuáticos a Escala de Paisaje (GESAP), INIBIOMA-CONICET Universidad Nacional del Comahue, Bariloche, Argentina","active":true,"usgs":false}],"preferred":false,"id":921005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maltchik, Leonardo","contributorId":347171,"corporation":false,"usgs":false,"family":"Maltchik","given":"Leonardo","affiliations":[{"id":83092,"text":"Programa de Pós-Graduação em Biologia de Ambientes Aquáticos Continentais, Universidade Federal do Rio Grande (FURG), Rio Grande, RS, Brazil","active":true,"usgs":false}],"preferred":false,"id":921006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stenert, Cristina","contributorId":347172,"corporation":false,"usgs":false,"family":"Stenert","given":"Cristina","email":"","affiliations":[{"id":83092,"text":"Programa de Pós-Graduação em Biologia de Ambientes Aquáticos Continentais, Universidade Federal do Rio Grande (FURG), Rio Grande, RS, Brazil","active":true,"usgs":false}],"preferred":false,"id":921007,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Epele, Luis B.","contributorId":347173,"corporation":false,"usgs":false,"family":"Epele","given":"Luis","email":"","middleInitial":"B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":921008,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":921009,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kneitel, Jamie M.","contributorId":347174,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie M.","affiliations":[{"id":83096,"text":"Department of Biological Sciences, California State University, 6000 J St, Sacramento, CA 95819, USA","active":true,"usgs":false}],"preferred":false,"id":921010,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Racey, Sophie","contributorId":347169,"corporation":false,"usgs":false,"family":"Racey","given":"Sophie","email":"","affiliations":[{"id":57293,"text":"Department of Entomology, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":921004,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Batzer, Darold P.","contributorId":347175,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":83097,"text":"Department of Entomology, 120 Cedar St, University of Georgia, Athens, GA 30605, USA","active":true,"usgs":false}],"preferred":false,"id":921011,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239405,"text":"70239405 - 2023 - An aridity threshold model of fire sizes and annual area burned in extensively forested ecoregions of the western USA","interactions":[],"lastModifiedDate":"2023-01-12T13:17:30.735297","indexId":"70239405","displayToPublicDate":"2023-01-11T07:15:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"An aridity threshold model of fire sizes and annual area burned in extensively forested ecoregions of the western USA","docAbstract":"Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability. Identifying the climatic conditions that trigger extensive fire years at regional scales can enable development of area burned models that are both spatially and temporally robust, which is crucial for understanding the impacts of past and future climate change. We identified region-specific thresholds in fire-season aridity that distinguish years with limited, moderate, and extensive area burned for 11 extensively forested ecoregions in the western United States. We developed a new area burned model using these relationships and demonstrate its application in the Southern Rocky Mountains using climate projections from five global climate models (GCMs) that bracket the range of projected changes in aridity. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. We project dramatic increases in area burned after 2020 under most GCMs and after 2060 with all GCMs as the frequency of extensive fire years increases. Our adaptable model can readily incorporate new observations (e.g., extreme fire years) to directly address the non-stationarity of fire-climate relationships as climatic conditions diverge from past observations. Our aridity threshold fire model provides a simple yet spatially robust approach to project regional changes in area burned with broad applicability to ecosystem and vegetation simulation models.
We study stress‐loading mechanisms for the California faults used in rupture forecasts. Stress accumulation drives earthquakes, and that accumulation mechanism governs recurrence. Most moment release in California occurs because of relative motion between the Pacific plate and the Sierra Nevada block; we calculate relative motion directions at fault centers and compare with fault displacement directions. Dot products between these vectors reveal that some displacement directions are poorly aligned with plate motions. We displace a 3D finite‐element model according to relative motions and resolve stress tensors onto defined fault surfaces, which reveal that poorly aligned faults receive no tectonic loading. Because these faults are known to be active, we search for other loading mechanisms. We find that nearly all faults with no tectonic loading show increase in stress caused by slip on the San Andreas fault, according to an elastic dislocation model. Globally, faults that receive a sudden stress change respond with triggered earthquakes that obey an Omori law rate decay with time. We therefore term this class of faults as “aftershock faults.” These faults release ∼4% of the moment release in California, have ∼0.1%–5% probability of M 6.7 earthquakes in 30 yr, and have a 0.001%–1% 30 yr M 7.7 probability range.
Wind-abraded rocks and aeolian bedforms have been observed at the Mars 2020 Perseverance landing site, providing evidence for recent and older wind directions. This study reports orientations of aeolian features measured in Perseverance images to infer formative wind directions. It compares these measurements with orbital observations, climate model predictions, and wind data acquired by the Mars Environmental Dynamics Analyzer. Three-dimensional orientations of flute textures on rocks, regolith wind tails extending from behind obstacles, and other aeolian features were measured using Digital Terrain Models (DTMs) derived from Mastcam-Z and navigation camera (Navcam) stereo images. Orientations of rock flutes measured in images acquired through Sol (martian day) 400 yielded a mean azimuth of 94° ± 7° (wind from the west). However, similar measurements of regolith wind tails indicate that recent sand-driving winds have been blowing from the east-southeast, nearly the opposite direction (mean azimuth = 285° ± 15°). Atmospheric modeling generally predicts net annual sand transport from the east-southeast at present, consistent with Perseverance regolith wind tail and orbital observations. The orientation of ventifact flutes thus suggests that they were formed under a different climate regime. Differences in orientations of recent and paleo-wind indicators have been noted at other Mars landing sites and may result from major orbital/axial changes that can cause significant changes in atmospheric circulation. Orientation differences between modern and older wind direction indicators at Jezero are useful clues to the climate history of the region.
Wetlands are the largest natural source of methane (CH4) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4, but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model—iPEACE—reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.
The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans, whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens. The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.
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We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9711","usgsCitation":"Poutanen, J., Fuller, A.K., Pusenius, J., Royle, A., Wikström, M., and Brommer, J., 2023, Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland: Ecology and Evolution, v. 13, no. 1, e9711, 14 p., https://doi.org/10.1002/ece3.9711.","productDescription":"e9711, 14 p.","ipdsId":"IP-117427","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467128,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9711","text":"Publisher Index Page"},{"id":467013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Finland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 22.745787010636263,\n 60.89878772519478\n ],\n [\n 22.745787010636263,\n 60.81018338906668\n ],\n [\n 22.92321705027345,\n 60.81018338906668\n ],\n [\n 22.92321705027345,\n 60.89878772519478\n ],\n [\n 22.745787010636263,\n 60.89878772519478\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Poutanen, Jenni","contributorId":348818,"corporation":false,"usgs":false,"family":"Poutanen","given":"Jenni","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":923803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pusenius, Jyrki","contributorId":348819,"corporation":false,"usgs":false,"family":"Pusenius","given":"Jyrki","affiliations":[{"id":40380,"text":"Natural Resources Institute Finland","active":true,"usgs":false}],"preferred":false,"id":923805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. 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Alignment of cameras within paired camera stations (hereafter, stations) could affect species detection due to issues with image exposure. We quantified effects of 3 camera models and alignment (staggered, offset by a perpendicular distance of 4.6 m, and aligned, directly facing one another) on camera performance in a station design. Mean exposure events (flash from one camera overexposes or underexposes pictures) at aligned stations was 3.93 (SE = 1.01; n = 40), whereas no exposure events were documented at staggered (n = 36) stations. Overall frequency of exposure events of mammal images at aligned cameras was 44% (68 exposure events/153 images). On average, 8% (range 0−35%) of mammal images from aligned stations were exposure events. We detected no difference (P = 0.88) in exposure events among paired camera models. Further, we detected no overall differences (P ≥ 0.07) in paired camera performance (i.e., number of mammal images over survey interval) between aligned or staggered stations, though reliability (i.e., percentage of camera stations that lasted entire survey interval) varied (P ≤ 0.001) between model types. Research deploying 2 cameras within a camera station framework can eliminate exposure events by using a staggered camera alignment without affecting the number of usable mammal photos. Rigorous field testing prior to deployment of stations is warranted to optimize reliability. One of our low-cost models performed as well as a more expensive model within our paired camera stations at collecting mammal images, and thus could be incorporated into study designs without compromising quality of camera photo data. We suggest a pilot study before large-scale deployment to evaluate reliability and performance of cameras, particularly when deploying multiple models.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1422","usgsCitation":"Swearingen, T., Klaver, R.W., Anderson, C.R., and Jacques, C., 2023, Influence of camera model and alignment on the performance of paired camera stations: Wildlife Society Bulletin, v. 47, no. 2, e1422, 11 p., https://doi.org/10.1002/wsb.1422.","productDescription":"e1422, 11 p.","ipdsId":"IP-117689","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1422","text":"Publisher Index Page"},{"id":465997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Alice L. Kibble Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.43161311292845,\n 40.36213807902013\n ],\n [\n -91.43161311292845,\n 40.359129920123905\n ],\n [\n -91.4282069310208,\n 40.359129920123905\n ],\n [\n -91.4282069310208,\n 40.36213807902013\n ],\n [\n -91.43161311292845,\n 40.36213807902013\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Swearingen, Tim","contributorId":348115,"corporation":false,"usgs":false,"family":"Swearingen","given":"Tim","affiliations":[{"id":49637,"text":"Western Illinois University","active":true,"usgs":false}],"preferred":false,"id":922951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Charles R. Jr.","contributorId":75042,"corporation":false,"usgs":true,"family":"Anderson","given":"Charles","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":922996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacques, Christopher N.","contributorId":348116,"corporation":false,"usgs":false,"family":"Jacques","given":"Christopher N.","affiliations":[{"id":49637,"text":"Western Illinois University","active":true,"usgs":false}],"preferred":false,"id":922953,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242151,"text":"70242151 - 2023 - Sound-side inundation and seaward erosion of a barrier island during hurricane landfall","interactions":[],"lastModifiedDate":"2023-04-10T11:46:38.89231","indexId":"70242151","displayToPublicDate":"2023-01-10T06:40:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13797,"text":"JGR - Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Sound-side inundation and seaward erosion of a barrier island during hurricane landfall","docAbstract":"Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier-island evolution emphasize ocean-side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36-km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm-surge model results and observations of high-water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co-located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post-storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within 2 months. This event represents a volumetric setback in the overwash/rollover behavior required for barrier transgression, but the new ponds and lowland habitats may provide beneficial habit for endangered species and will likely persist for years.