California’s Central Valley provides critical habitat for migratory waterbirds, yet only 10% of naturally occurring wetlands remain. Competition for limited water supplies and climate change will impact the long-term viability of these intensively managed habitats.
Forecast the distribution, abundance, and connectivity of surface water and managed wetland habitats, using 5 spatially explicit (270 m2) climate/land use/water prioritization scenarios. Mapping potential future dynamic flooded habitat used by waterbirds and other wetland-dependent wildlife to inform management decisions.
We integrated a climate-driven hydrologic water use model with a spatially explicit land change model, to examine stakeholder-driven scenarios of future land change, climate, and water use and their impacts on future habitat availability.
Declining water availability is the dominant driver of habitat loss across scenarios. The hot/dry scenarios showed the greatest declines in January flooded area by 2101—an important month for overwintering waterbirds. In contrast, higher water supplies in wet climates drive perennial cropland conversion and loss of potential habitat. Potential flooded cropland declined (25 and 33%) under warmer/wetter climate conditions due to this conversion to perennial crops, exposing habitat vulnerability.
Climate-driven loss of water availability had a greater impact on flooded habitat availability than land-use change. When combined, climate change and the conversion of potentially flooded cropland to perennial cropland will threaten future waterbird habitat particularly in January, the peak of the migratory bird season, even when habitat restoration goals are met. Stakeholder-informed scenario analysis can identify target areas for potential habitat change, vulnerability, and conservation.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01398-1","usgsCitation":"Wilson, T., Matchett, E., Byrd, K.B., Conlisk, E., Reiter, M.E., Wallace, C., Flint, L.E., Flint, A.L., and Moritsch, M.M., 2022, Climate and land change impacts on future managed wetland habitat: A case study from California’s Central Valley: Landscape Ecology, v. 37, p. 861-881, https://doi.org/10.1007/s10980-021-01398-1.","productDescription":"21 p.","startPage":"861","endPage":"881","ipdsId":"IP-127595","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":394093,"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 \"features\": [\n {\n \"type\": \"Feature\",\n 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watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent.
In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [r] = 0.517), higher runoff ratios (r = 0.646), and lower amounts (r = −0.637) and proportions (r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology.
Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215126","collaboration":"Prepared in cooperation with DeKalb County Department of Watershed Management","usgsCitation":"Aulenbach, B.T., Kolb, K., Joiner, J.K., and Knaak, A.E., 2022, Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16: U.S. Geological Survey Scientific Investigations Report 2021–5126, 105 p., https://doi.org/10.3133/sir20215126.","productDescription":"Report: xii, 105 p.; Data Release; Database","numberOfPages":"105","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117184","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":393756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M6WCRH","text":"USGS data release","linkHelpText":"Streamwater constituent 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1770 Corporate Drive, Suite 500
Norcross, GA 30093
River waters contain complex chemical mixtures derived from natural and anthropogenic sources. Aquatic organisms are exposed to the entire chemical composition of the water, resulting in potential effects at the organismal through ecosystem level. This study applied a holistic approach to assess landscape, hydrological, chemical, and biological variables. On-site mobile laboratory experiments were conducted to evaluate biological effects of exposure to chemical mixtures in the Shenandoah River Watershed. A suite of 534 inorganic and organic constituents were analyzed, of which 273 were detected. A watershed-scale accumulated wastewater model was developed to predict environmental concentrations of chemicals derived from wastewater treatment plants (WWTPs) to assess potential aquatic organism exposure for all stream reaches in the watershed. Measured and modeled concentrations generally were within a factor of 2. Ecotoxicological effects from exposure to individual components of the chemical mixture were evaluated using risk quotients (RQs) based on measured or predicted environmental concentrations and no effect concentrations or chronic toxicity threshold values. Seventy-two percent of the compounds had RQ values <0.1, indicating limited risk from individual chemicals. However, when individual RQs were aggregated into a risk index, most stream reaches receiving WWTP effluent posed potential risk to aquatic organisms from exposure to complex chemical mixtures.
Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgaging stations and other historical information provides an informative means of estimating flood flow frequency. Flood flow frequency is defined by values or quantiles of discharge for selected annual exceedance probabilities (AEPs) (England and others, 2018). The annual peak discharge data as part of systematic operation of a streamgaging station provides the foundation for a detailed analysis of peak discharge, but additional historical information pertaining to peak discharges also can be used. An annual peak discharge is defined as the maximum instantaneous discharge for a streamgaging station for a given water year, and annual peak discharge data for USGS streamgaging stations can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2018). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of the multi-layered analysis, InFRM team members from the USGS analyzed annual peak discharge records for the 15 USGS streamgaging stations (gages) shown on Figure A.1. Information on the period of record data for those USGS gages are listed in Table A.1.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 64 p.","productDescription":"64 p.","ipdsId":"IP-101867","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835668,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262186,"text":"70262186 - 2022 - Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","interactions":[],"lastModifiedDate":"2025-01-15T17:52:54.665605","indexId":"70262186","displayToPublicDate":"2022-01-01T11:45:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","docAbstract":"Brook Trout Salvelinus fontinalis have faced significant declines throughout their native range and have been stocked in Midwestern waters since the late 1800s to offset such losses. Several studies have investigated the genetic effects of these stockings, but these efforts have been confined to relatively small spatial scales. In this study, we compiled 8,454 Brook Trout microsatellite genotypes from 188 wild Midwestern populations and 26 hatchery strains to provide novel insights of broadscale population structure, regional patterns of genetic diversity, and estimates of hatchery introgression for inland Wisconsin populations. Our results indicate high levels of differentiation among our study populations, a lack of hydrological population structuring, lower estimates of genetic diversity in the Driftless Area, and that hatchery introgression has been largely confined to regions of inland Wisconsin that have been heavily affected by anthropogenic disturbances (i.e., the Driftless Area). We also provide evidence that populations may be able to purge hatchery‐derived alleles, discuss possible mechanisms behind this phenomenon, and consider their relevance to accurate estimation of hatchery introgression. Collectively, these results summarize the genetic effects of over a century of anthropogenic disturbance on native Brook Trout populations and emphasize the importance of integrating historical data into contemporary genetic research of intensively managed species.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10333","usgsCitation":"Bradley Erdman, Matthew G. Mitro, Joanna D.T. Griffin, David Rowe, Kazyak, D.C., Keith Turnquist, Michael Siepker, Loren Miller, Stott, W., Hughes, M., Sloss, B., Kinnison, M.T., and Larson, W., 2022, Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics: Transactions of the American Fisheries Society, v. 151, no. 1, p. 81-99, https://doi.org/10.1002/tafs.10333.","productDescription":"19 p.","startPage":"81","endPage":"99","ipdsId":"IP-132403","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.62198592078825,\n 47.17494781150509\n ],\n [\n -95.43596059373883,\n 47.28169312893286\n ],\n [\n -95.46739097764518,\n 42.15136704567587\n ],\n [\n -86.50823833580571,\n 42.15136704567587\n ],\n [\n -86.62198592078825,\n 47.17494781150509\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley Erdman","contributorId":348382,"corporation":false,"usgs":false,"family":"Bradley Erdman","affiliations":[{"id":83360,"text":"University of Maine School of Biology and Ecology","active":true,"usgs":false}],"preferred":false,"id":923419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matthew G. 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Griffin","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David Rowe","contributorId":348385,"corporation":false,"usgs":false,"family":"David Rowe","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923422,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":923423,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keith Turnquist","contributorId":348386,"corporation":false,"usgs":false,"family":"Keith Turnquist","affiliations":[{"id":83363,"text":"College of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923424,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michael Siepker","contributorId":348387,"corporation":false,"usgs":false,"family":"Michael Siepker","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923425,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loren Miller","contributorId":348388,"corporation":false,"usgs":false,"family":"Loren Miller","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923426,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923427,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hughes, Michael","contributorId":348579,"corporation":false,"usgs":false,"family":"Hughes","given":"Michael","affiliations":[],"preferred":false,"id":923611,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sloss, Brian","contributorId":191462,"corporation":false,"usgs":false,"family":"Sloss","given":"Brian","affiliations":[],"preferred":false,"id":923612,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kinnison, Michael T.","contributorId":169617,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923613,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923418,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70228833,"text":"70228833 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-03-27T15:12:27.207997","indexId":"70228833","displayToPublicDate":"2022-01-01T10:07:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Neches River Basin.
The RiverWare model described in this chapter presents development of the Neches River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer streamgage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Neches River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate model performance since the Salt Water Barrier (SWB) construction was completed in 2005. The time window simulation run is for water year (WY) 2005 – WY 2018. This time window also captures the time when Hurricane Harvey occurred (late August of 2017). Each simulated water year was inspected individually to better validate the results.
After calibration, a general run for January 01, 1929 through WY 2018 was made. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results. More emphasis was put on B.A. Steinhagen’s operations because the dam captures two major rivers (i.e. the Angelina and the Neches Rivers). Results were inspected closely for B.A. Steinhagen’s pool and releases, the simulated discharges at the Neches at Evadale gage, and the simulated discharges at the SWB at Beaumont, Texas.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 66 p.","productDescription":"66 p.","ipdsId":"IP-113418","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396305,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835669,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237286,"text":"70237286 - 2022 - Review of “Lake hydrology: An introduction to lake mass balance”","interactions":[],"lastModifiedDate":"2022-10-06T14:18:51.055642","indexId":"70237286","displayToPublicDate":"2022-01-01T09:16:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Review of “Lake hydrology: An introduction to lake mass balance”","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13145","usgsCitation":"Rosenberry, D., 2022, Review of “Lake hydrology: An introduction to lake mass balance”: Groundwater, v. 60, no. 1, p. 25-26, https://doi.org/10.1111/gwat.13145.","productDescription":"2 p.","startPage":"25","endPage":"26","ipdsId":"IP-133031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":853982,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227325,"text":"70227325 - 2022 - Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional","interactions":[],"lastModifiedDate":"2022-03-28T16:37:58.702915","indexId":"70227325","displayToPublicDate":"2021-12-29T07:02:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional","docAbstract":"Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of ‘Plant Functional Types’ (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, i.e. to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages (‘Lineage Functional Types’) will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.
Considering reservoirs as linear fragments in a basin's river network could improve understanding, predictability, and management efficiency. We looked for general cascading spatial patterns across five categories of reservoir attributes: land cover, morphology and hydrology, fish habitat, fish assemblages, and fisheries. Attributes were pulled from various databases for large reservoirs (>100 ha) located in the United States. 16 widely distributed river basins, each including a minimum of 15 large reservoirs, were selected for analysis. Using analysis of covariance with basin as the class variable, we tested each attribute as a linear function of catchment area, which is an index of reservoir position in the basin. The majority of reservoir attributes displayed log-linear patterns as catchment area increased, indicating that reservoirs act as members of a larger network just as river reaches do. Several patterns were detected including attributes with no apparent lengthwise arrangement along the basin; cascading spatial patterns in which attributes increase or decrease from upstream to downstream within a basin; and attributes that increase with catchment area in some basins, decrease in others, or may simply remain constant throughout the basin. We conclude that each pattern may have different implications for management, and that the effectiveness with which most management activities influence reservoirs is likely to increase or decrease along river basins.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR029910","usgsCitation":"Faucheux, N., Sample, A., Aldridge, C., Norris, D., Owens, C., Starnes, V.R., VanderBloemen, S., and Miranda, L.E., 2022, Reservoir attributes display cascading spatial patterns along river basins: Water Resources Research, v. 58, no. 1, e2021WR029910, 14 p., https://doi.org/10.1029/2021WR029910.","productDescription":"e2021WR029910, 14 p.","ipdsId":"IP-119850","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Faucheux, N.M.","contributorId":341806,"corporation":false,"usgs":false,"family":"Faucheux","given":"N.M.","affiliations":[{"id":81792,"text":"Mississippi State Uni","active":true,"usgs":false}],"preferred":false,"id":908915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, A.R.","contributorId":341807,"corporation":false,"usgs":false,"family":"Sample","given":"A.R.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, C.A.","contributorId":275883,"corporation":false,"usgs":false,"family":"Aldridge","given":"C.A.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, D.M.","contributorId":341780,"corporation":false,"usgs":false,"family":"Norris","given":"D.M.","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":908918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, C.","contributorId":341808,"corporation":false,"usgs":false,"family":"Owens","given":"C.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starnes, Victoria R.","contributorId":343988,"corporation":false,"usgs":false,"family":"Starnes","given":"Victoria","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"VanderBloemen, S.","contributorId":341810,"corporation":false,"usgs":false,"family":"VanderBloemen","given":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908921,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908922,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227357,"text":"70227357 - 2022 - Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","interactions":[],"lastModifiedDate":"2022-05-13T14:36:19.096668","indexId":"70227357","displayToPublicDate":"2021-12-24T07:09:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","docAbstract":"Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
Estuarine food webs are complex, as marine, freshwater, and terrestrial inputs combine and contribute variable amounts of organic material. Seasonal fluctuations in precipitation amplify the dynamism inherent to estuarine food webs, particularly in lagoonal estuaries, which can be seasonally closed and disconnected from the ocean in low-runoff periods (bar-built lagoons). Despite their abundance along coastlines in Mediterranean climates, the organic matter sources fueling bar-built lagoon food webs are poorly understood, particularly with respect to seasonal hydrologic variability, episodic marine connections, and internal nutrient cycling. In this study, we evaluate the food web of a bar-built lagoon with respect to seasonal differences in lagoon water quality, the sources of organic matter which support consumers, and the trophic ecology of resident fishes. Observed water quality conditions reflected biogeochemical processes associated with salinity-driven stratification and high lagoon residence times and were associated with strong seasonal differences in the contribution of different organic matter sources to lagoon consumers. A variety of organic matter sources supported consumers; marine inputs were important to lagoon food webs in spring when the lagoon was open, while summer food webs were largely driven by phytoplankton which was likely fueled by internal nutrient cycling. Fish diets were largely comprised of crustaceans and fish eggs, with clearly defined trophic niches in spring but high overlap in summer. This study demonstrates that the seasonal changes in bar-built lagoon food webs are largely dependent on ocean connectivity and internal cycling within the lagoon, rather than watershed processes as is typical for many estuaries.
Triple oxygen isotope (∆17O with δ18O) signals of H2O and O2 found in sulfate of oxidative weathering origin offer promising constraints on modern and ancient weathering, hydrology, atmospheric gas concentrations, and bioproductivity. However, interpretations of the sulfate-water-O2 system rely on assuming fixed oxygen-isotope fractionations between sulfate and water, which, contrastingly, are shown to vary widely in sign and amplitude. Instead, here we anchor sulfate-water-O2 triple oxygen isotope systematics on the homogeneous composition of atmospheric O2 with empirical constraints and modeling. Our resulting framework does not require a priori assumptions of the O2- versus H2O‑oxygen ratio in sulfate and accounts for the signals of mass-dependent and mass-independent fractionation in the ∆17O and δ18O of sulfate's O2‑oxygen source. Within this framework, new ∆17O measurements of sulfate constrain ~2.3 Ga Paleoproterozoic gross primary productivity to between 6 and 160 times present-day levels, with important implications for the biological carbon cycle response to high CO2 concentrations prevalent on the early Earth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120678","usgsCitation":"Killingsworth, B.A., Cartigny, P., Hayles, J.A., Thomazo, C., Sansjofre, P., Pasquier, V., Lalonde, S.V., and Philippot, P., 2022, Towards a holistic sulfate-water-O2 triple oxygen isotope systematics: Chemical Geology, v. 588, 120678, 13 p., https://doi.org/10.1016/j.chemgeo.2021.120678.","productDescription":"120678, 13 p.","ipdsId":"IP-130808","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120678","text":"Publisher Index Page"},{"id":394018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"588","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Killingsworth, Bryan Alan 0000-0001-6067-8604","orcid":"https://orcid.org/0000-0001-6067-8604","contributorId":270978,"corporation":false,"usgs":true,"family":"Killingsworth","given":"Bryan","email":"","middleInitial":"Alan","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":830272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cartigny, Pierre","contributorId":270979,"corporation":false,"usgs":false,"family":"Cartigny","given":"Pierre","email":"","affiliations":[{"id":56238,"text":"Institut de Physique du Globe de Paris, Sorbonne-Paris Cité, UMR 7154, CNRS-Université Paris Diderot","active":true,"usgs":false}],"preferred":false,"id":830273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayles, Justin A.","contributorId":270977,"corporation":false,"usgs":false,"family":"Hayles","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":56237,"text":"Jacobs-JETS, Astromaterials Research and Exploration Science, Johnson Space Center National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":830274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomazo, Christophe","contributorId":270980,"corporation":false,"usgs":false,"family":"Thomazo","given":"Christophe","email":"","affiliations":[{"id":56239,"text":"UMR CNRS/uB 6282 Laboratoire Biogéosciences, Université de Bourgogne Franche-Comté and Institut Universitaire de France","active":true,"usgs":false}],"preferred":false,"id":830275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sansjofre, Pierre","contributorId":270981,"corporation":false,"usgs":false,"family":"Sansjofre","given":"Pierre","email":"","affiliations":[{"id":56240,"text":"CNRS-UMR6538 Laboratoire Géosciences Océan, European Institute for Marine Studies, Université de Bretagne Occidentale","active":true,"usgs":false}],"preferred":false,"id":830276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pasquier, Virgil","contributorId":270982,"corporation":false,"usgs":false,"family":"Pasquier","given":"Virgil","email":"","affiliations":[{"id":56241,"text":"Department of Earth and Planetary Sciences, Weizmann Institute of Science","active":true,"usgs":false}],"preferred":false,"id":830277,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lalonde, Stefan V.","contributorId":196839,"corporation":false,"usgs":false,"family":"Lalonde","given":"Stefan","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":830278,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Philippot, Pascal","contributorId":270983,"corporation":false,"usgs":false,"family":"Philippot","given":"Pascal","email":"","affiliations":[{"id":56242,"text":"Géosciences Montpellier, CNRS-UMR 5243, Université de Montpellier and Institut de Physique du Globe de Paris, Sorbonne-Paris Cité, UMR 7154, CNRS-Université Paris Diderot","active":true,"usgs":false}],"preferred":false,"id":830279,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70226846,"text":"70226846 - 2022 - What determines the effectiveness of Pinyon-Juniper clearing treatments? Evidence from the remote sensing archive and counter-factual scenarios","interactions":[],"lastModifiedDate":"2021-12-15T12:43:50.648799","indexId":"70226846","displayToPublicDate":"2021-12-08T06:41:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"What determines the effectiveness of Pinyon-Juniper clearing treatments? Evidence from the remote sensing archive and counter-factual scenarios","docAbstract":"In the intermountain western US, expansion of Pinyon (Pinus edulis) and Juniper (Juniperus spp.) woodlands (PJ) into grasslands and shrublands is a pervasive phenomenon, and an example of the global trend towards enhanced woody growth in drylands. Due to the perceived impacts of these expansions on ecosystem services related to biodiversity, hydrology, soil stability, fire prevention, and livestock forage, mechanical and chemical PJ reduction treatments have been a long-standing practice in the region. More recently, PJ reduction practices have come under enhanced public scrutiny, due to potential impacts on PJ-dependent wildlife, risk of erosion due to soil disturbance, and cost effectiveness due to variable rates of long-term success. Moreover, there is growing interest in understanding the biotic, abiotic, and management conditions under which PJ reduction treatments are effective. Here, we evaluated PJ reduction treatment outcomes leveraging large, curated databases of land treatments, new remotely sensed fractional cover time-series products, gridded climate and soils data, and analytical approaches adopted from the econometric literature. From 302 treatment events and 1569 distinct treatment polygons we found evidence that treatments reduced tree cover and largely increased shrub and perennial herbaceous cover for 10 or more years. However, treatments were also associated with increases in annual, likely non-native, herbaceous cover. Importantly, we noted treatment outcomes varied by landscape context, with some soil and geomorphic settings exhibiting consistent returns to pre-treatment conditions within 10–15 years, and others exhibiting more persistent changes in functional type composition. Despite the overall trends we observed, there was considerable unexplained variability in outcomes from treatment to treatment, highlighting the need for caution and attention to local geomorphic and biological context in planning future treatments.
Hydrology and salinity regimes of many impounded wetlands are manipulated to provide seasonal habitats for migratory waterfowl, with little-known consequences for ecosystem structure and function. Managed hydrology can alter ecosystems by directly changing soil properties and processes and by influencing plant community dynamics. Additionally, management history may influence ecosystem response to disturbance, including fires. To better understand how wetland management regime influences ecosystem response to disturbance, we quantified elevation, soil nitrogen concentrations and process rates, and plant community structure and diversity in a natural experiment following the 2018 Branscombe Fire. We measured paired burned-unburned patches in both tidally-influenced and managed, seasonally-impounded wetlands in Suisun Marsh, California, USA. Unburned ecosystem structure and nutrient cycling differed by wetland management history; unburned impounded wetlands were ∼1 m lower in elevation and plant community composition was dominated by succulents whereas the unburned tidal wetland was dominated by graminoids. Unburned impounded wetland soil nitrogen cycling (potential nitrification and denitrification) rates were <28% of those measured in unburned tidal wetland soils and soil extractable nitrate, ammonium, and dissolved inorganic phosphorus concentrations were also substantially lower in unburned impounded than unburned tidal wetlands. Despite these differences in pre-disturbance (i.e., unburned) conditions, all soil processes recovered to baseline levels within 6 months after surface fire, and we found no evidence of plant community change 1 year after fire in either wetland management type. Overall, water management history exerted stronger control on ecosystem processes and structure than surface fire disturbance. Low extractable soil nitrate and potential denitrification rates may indicate limitation of soil nitrogen removal in impounded wetlands, with implications for downstream environmental quality and eutrophication across managed landscapes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.114153","usgsCitation":"Jones, S., Schutte, C.A., Roberts, B., and Thorne, K., 2022, Seasonal impoundment management reduces nitrogen cycling but not resilience to surface fire in a tidal wetland: Journal of Environmental Management, v. 303, 114153, 11 p., https://doi.org/10.1016/j.jenvman.2021.114153.","productDescription":"114153, 11 p.","ipdsId":"IP-134289","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449468,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2021.114153","text":"Publisher Index Page"},{"id":436034,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DWBSQT","text":"USGS data release","linkHelpText":"Soil, Plant, and Elevation Characteristics of Tidal and Managed Impounded Wetlands in Suisun Marsh, California, USA (2018-2019)"},{"id":414698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.01718926989348,\n 38.18457183754458\n ],\n [\n -122.01718926989348,\n 38.080375985659515\n ],\n [\n -121.8946168874855,\n 38.080375985659515\n ],\n [\n -121.8946168874855,\n 38.18457183754458\n ],\n [\n -122.01718926989348,\n 38.18457183754458\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"303","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schutte, Charles A","contributorId":303410,"corporation":false,"usgs":false,"family":"Schutte","given":"Charles","email":"","middleInitial":"A","affiliations":[{"id":65797,"text":"Louisiana Universities Marine Consortium, Chauvin, LA; Rowan University (present)","active":true,"usgs":false}],"preferred":false,"id":867526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, Brian J","contributorId":146207,"corporation":false,"usgs":false,"family":"Roberts","given":"Brian J","affiliations":[{"id":16627,"text":"Louisiana Universities Marine Consortium (LUMCON)","active":true,"usgs":false}],"preferred":false,"id":867527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867528,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231551,"text":"70231551 - 2022 - Aquatic vegetation dynamics in the Upper Mississippi River over 2 decades spanning vegetation recovery","interactions":[],"lastModifiedDate":"2022-05-13T11:47:38.827389","indexId":"70231551","displayToPublicDate":"2021-11-18T06:43:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic vegetation dynamics in the Upper Mississippi River over 2 decades spanning vegetation recovery","docAbstract":"Macrophytes have recovered in rivers across the world, but long-term data and studies are lacking regarding community assembly and diversity changes coincident with macrophyte recovery. We investigated patterns of aquatic vegetation species composition and diversity in thousands of sites in the Upper Mississippi River, USA, spanning 21 y of monitoring and a period of vegetation recovery. We analyzed site-level compositional dissimilarity and environmental associations using non-metric multidimensional scaling, compared stability of lake-level assemblages over time with convex hulls, and assessed shared trends in assemblage dissimilarity at the pool scale using dynamic factor analysis. Site-level differences in aquatic vegetation assemblage structure were associated with water depth and substrate, and a gradient of species abundance and diversity was apparent. A common trend in assemblage dissimilarity over time and across contiguous floodplain lakes indicate that assemblage composition changed and diversity increased with considerable synchrony within the past 21 y. Shared trends across the 400-km study reach are indicative of 1 or more widespread, common drivers; however, neither hydrologic extremes nor turbidity explained vegetation assemblage patterns. Following several years of strong changes in composition and increased diversity, the vegetation assemblage displayed signs of increasing stability in some pools but not others. Further research is needed to identify drivers and mechanisms of aquatic vegetation assemblage expansion, assembly, and resilience, all of which will be applicable to the recovery of aquatic vegetation in floodplain systems worldwide.
Trees in the urban right-of-way areas have increasingly been considered part of a suite of green infrastructure practices used to manage stormwater runoff. A paired-catchment experimental design (with street tree removal as the treatment) was used to assess how street trees affect major hydrologic fluxes in a typical residential stormwater collection and conveyance network. The treatment consisted of removing 29 green ash (Fraxinus pennsylvanica) and two Norway maple (Acer platanoides) street trees from a medium-density residential area. Tree removal resulted in an estimated 198 m3 increase in surface runoff volume compared to the control catchment over the course of the study. This increase accounted for 4% of the total measured runoff after trees were removed. Despite significant changes to runoff volume (p ≤ 0.10), peak discharge was generally not affected by tree removal. On a per-tree basis, 66 L of rainfall per m2 of canopy was lost that would have otherwise been intercepted and stored. Runoff volume reduction benefit was estimated at 6376 L per tree. These values experimentally document per-capita retention services rendered by trees over a growing season with 42 storm events. These values are within the range reported by previous studies, which largely relied on simulation. This study provides catchment scale evidence that reducing stormwater runoff is one of many ecosystem services provided by street trees. This study quantifies these services, based on site conditions and a mix of deciduous species, and serves to improve our ability to account for this important yet otherwise poorly constrained hydrologic service. Engineers, city planners, urban foresters, and others involved with the management of urban stormwater can use this information to better understand tradeoffs involved in using green infrastructure to reduce urban runoff burden.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.151296","usgsCitation":"Selbig, W.R., Loheid, S., Schuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R.J., and Nowak, D., 2022, Quantifying the stormwater runoff volume reduction benefits of urban street tree canopy: Science of the Total Environment, v. 806, no. 3, 151296, 9 p., https://doi.org/10.1016/j.scitotenv.2021.151296.","productDescription":"151296, 9 p.","ipdsId":"IP-131647","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436043,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJHBVW","text":"USGS data release","linkHelpText":"Storm event data in the control and test catchments during the calibration and treatment phase of a urban tree canopy study in Fond du Lac, Wisconsin, from May 2018 through September 2020: U.S. Geological Survey data release"},{"id":392624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.516845703125,\n 43.70163689691259\n ],\n [\n -88.34930419921875,\n 43.70163689691259\n ],\n [\n -88.34930419921875,\n 43.85235516793534\n ],\n [\n -88.516845703125,\n 43.85235516793534\n ],\n [\n -88.516845703125,\n 43.70163689691259\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"806","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loheid, Steven P. II 0000-0003-1897-0163","orcid":"https://orcid.org/0000-0003-1897-0163","contributorId":269846,"corporation":false,"usgs":false,"family":"Loheid","given":"Steven P.","suffix":"II","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":828022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schuster, William","contributorId":117899,"corporation":false,"usgs":true,"family":"Schuster","given":"William","email":"","affiliations":[],"preferred":false,"id":828040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scharenbroch, Bryant C. 0000-0002-9342-7550","orcid":"https://orcid.org/0000-0002-9342-7550","contributorId":269849,"corporation":false,"usgs":false,"family":"Scharenbroch","given":"Bryant","email":"","middleInitial":"C.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":828041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coville, Robert C. 0000-0002-6895-2564","orcid":"https://orcid.org/0000-0002-6895-2564","contributorId":269851,"corporation":false,"usgs":false,"family":"Coville","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":828042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kruegler, James 0000-0002-2671-0807","orcid":"https://orcid.org/0000-0002-2671-0807","contributorId":269853,"corporation":false,"usgs":false,"family":"Kruegler","given":"James","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":828043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Avery, William 0000-0002-2651-9906","orcid":"https://orcid.org/0000-0002-2651-9906","contributorId":269858,"corporation":false,"usgs":false,"family":"Avery","given":"William","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":828044,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haefner, Ralph J. 0000-0002-4363-9010 rhaefner@usgs.gov","orcid":"https://orcid.org/0000-0002-4363-9010","contributorId":1793,"corporation":false,"usgs":true,"family":"Haefner","given":"Ralph","email":"rhaefner@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828045,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nowak, David 0000-0002-2043-0062","orcid":"https://orcid.org/0000-0002-2043-0062","contributorId":269856,"corporation":false,"usgs":false,"family":"Nowak","given":"David","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":828046,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229388,"text":"70229388 - 2022 - Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors","interactions":[],"lastModifiedDate":"2022-03-04T17:22:20.925497","indexId":"70229388","displayToPublicDate":"2021-10-25T11:12:36","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors","docAbstract":"The advent of Low Power Wide Area Networks (LPWAN) has improved the feasibility of wireless sensor networks for environmental sensing across wide areas. We have built EnviSense, an ultra-low power environmental sensing system, and deployed over a dozen of them across two locations in Northern California for hydrological monitoring applications with the U.S. Geological Survey (USGS). This paper details our experiences with the design and implementation of this system across two years, including six months of continuous measurement in the field. We describe the lessons learned for deployment planning, remote device management and programming, and system co-design with a domain-expert from the USGS.
","largerWorkTitle":"LP-IoT '21: Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things","language":"English","publisher":"Association of Computing Machinery","doi":"10.1145/3477085.3478988","usgsCitation":"Grimsley, R., Marineau, M.D., and Iannucci, R.A., 2022, Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors, in LP-IoT '21: Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things, p. 1-7, https://doi.org/10.1145/3477085.3478988.","productDescription":"7 p.","startPage":"1","endPage":"7","ipdsId":"IP-130093","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449595,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1145/3477085.3478988","text":"Publisher Index Page"},{"id":396759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Beale Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.42982482910158,\n 39.066380823434486\n ],\n [\n -121.36528015136717,\n 39.06718050308463\n ],\n [\n -121.31996154785158,\n 39.087169549791966\n ],\n [\n -121.31927490234376,\n 39.13325601865834\n ],\n [\n -121.34056091308594,\n 39.14949897356036\n ],\n [\n -121.3985824584961,\n 39.176650950983294\n ],\n [\n -121.47239685058592,\n 39.16227768020765\n ],\n [\n -121.48097991943358,\n 39.14630393428414\n ],\n [\n -121.4813232421875,\n 39.12633165289992\n ],\n [\n -121.42982482910158,\n 39.066380823434486\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Grimsley, Reese 0000-0002-3458-0707","orcid":"https://orcid.org/0000-0002-3458-0707","contributorId":287982,"corporation":false,"usgs":false,"family":"Grimsley","given":"Reese","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":837249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iannucci, Robert A.","contributorId":202339,"corporation":false,"usgs":false,"family":"Iannucci","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":36393,"text":"Carnegie Mellon University - Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":837251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228320,"text":"70228320 - 2022 - Carbon flux, storage, and wildlife co-benefits in a restoring estuary","interactions":[],"lastModifiedDate":"2022-02-08T16:14:45.51807","indexId":"70228320","displayToPublicDate":"2021-10-15T10:04:24","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Carbon flux, storage, and wildlife co-benefits in a restoring estuary","docAbstract":"Tidal marsh restorations may result in transitional mudflat habitats depending on hydrological and geomorphological conditions. Compared to tidal marsh, mudflats are thought to have limited value for carbon sequestration, carbon storage, and foraging benefits for salmon. We evaluated greenhouse gas exchange, sediment carbon storage, and invertebrate production at restoration and reference tidal marsh sites within the Nisqually River Delta, Puget Sound, Washington. Within the first seven years, the restoration site was a sparsely vegetated mudflat that didn't sequester atmospheric CO2; but, had sediment carbon accumulation rates similar to the reference site due to allochthonous carbon subsidies from nearby mature marshes. Compared to other estuarine habitat types, the tidal marsh supported the greatest production of energy-rich insect prey for juvenile salmon; yet, the restoration site produced similar, and at times elevated, invertebrate prey resources and prey energy compared to the reference site. As a result, salmon that foraged within the restoration site gained measurable benefits as indicated by their bioenergetic growth potential. The restoration site received carbon subsidies from the broader estuarine landscape for both sediment carbon accumulation and invertebrate prey production. These findings demonstrate the importance of habitat connectivity and show how blue carbon and wildlife co-benefits are closely intertwined.
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2022 - Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","interactions":[],"lastModifiedDate":"2022-03-28T16:34:32.669545","indexId":"70227179","displayToPublicDate":"2021-10-03T10:21:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","title":"Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","docAbstract":"Broad-scale patterns of genetic diversity for Brook Trout remain poorly understood across their endemic range in the eastern United States. We characterized variation at 12 microsatellite loci in 22,020 Brook Trout among 836 populations from Georgia, USA to Quebec, Canada to the western Great Lakes region. Within-population diversity was typically lower in the southern Appalachians relative to the mid-Atlantic and northeastern regions. Effective population sizes in the southern Appalachians were often very small, with many estimates less than 30 individuals. The population genetics of Brook Trout in the southern Appalachians are far more complex than a conventionally held simple “northern” versus “southern” dichotomy would suggest. Contemporary population genetic variation was consistent with geographic expansion of Brook Trout from Mississippian, mid-Atlantic, and Acadian glacial refuges, as well as differentiation among drainages within these broader clades. Genetic variation was pronounced among drainages (57.4% of overall variation occurred among Hydrologic Unit Code (HUC)10 or larger units) but was considerable even at fine spatial scales (13% of variation occurred among collections within HUC12 drainage units). Remarkably, 87.2% of individuals were correctly assigned to their collection of origin. While comparisons with fish from existing major hatcheries showed impacts of stocking in some populations, genetic introgression did not overwhelm the signal of broad-scale patterns of population genetic structure. Although our results reveal deep genetic structure in Brook Trout over broad spatial extents, fine-scale population structuring is prevalent across the southern Appalachians. Our findings highlight the distinctiveness and vulnerability of many Brook Trout populations in the southern Appalachian Mountains and have important implications for wild Brook Trout management. To facilitate application of our findings by conservation practitioners, we provide an interactive online visualization tool to allow our results to be explored at management relevant scales.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10337","usgsCitation":"Kazyak, D., Lubinski, B.A., Kulp, M.A., Pregler, K., Whiteley, A.R., Hallerman, E.M., Coombs, J.A., Kanno, Y., Rash, J., Morgan II, R., Habera, J., Henegar, J., Weathers, T., Sell, M.T., Rabern, A., Rankin, D., and King, T., 2022, Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains: Transactions of the American Fisheries Society, v. 151, no. 2, p. 127-149, https://doi.org/10.1002/tafs.10337.","productDescription":"23 p.","startPage":"127","endPage":"149","ipdsId":"IP-126747","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449683,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/tafs.10337","text":"External Repository"},{"id":393864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia, West Virginia","otherGeospatial":"southern Appalachian Mountians","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.375732421875,\n 36.30627216957992\n ],\n [\n -79.38720703125,\n 36.30627216957992\n ],\n [\n -79.38720703125,\n 38.66835610151506\n ],\n [\n -81.375732421875,\n 38.66835610151506\n ],\n [\n -81.375732421875,\n 36.30627216957992\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":829942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pregler, K. 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Casey","contributorId":270747,"corporation":false,"usgs":false,"family":"Weathers","given":"T. Casey","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":829952,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sell, Matthew T.","contributorId":261510,"corporation":false,"usgs":false,"family":"Sell","given":"Matthew","email":"","middleInitial":"T.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829953,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rabern, Anthony","contributorId":270748,"corporation":false,"usgs":false,"family":"Rabern","given":"Anthony","email":"","affiliations":[{"id":56207,"text":"GA Dept Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829954,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rankin, Dan","contributorId":270749,"corporation":false,"usgs":false,"family":"Rankin","given":"Dan","email":"","affiliations":[{"id":56208,"text":"SC Dept Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829955,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"King, Tim L.","contributorId":236903,"corporation":false,"usgs":false,"family":"King","given":"Tim L.","affiliations":[],"preferred":false,"id":829956,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70229087,"text":"70229087 - 2022 - Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data","interactions":[],"lastModifiedDate":"2022-02-28T14:44:11.931261","indexId":"70229087","displayToPublicDate":"2021-10-03T08:37:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data","docAbstract":"Submerged aquatic vegetation (SAV) creates highly productive habitats in coastal areas, providing support for many important species of fish and wildlife. Despite the importance and documented loss of SAV across fresh to marine habitats globally, we lack consistent baseline data on estuarine SAV resources, particularly in the northern Gulf of Mexico (NGOM) estuaries. To understand SAV distribution in the NGOM, SAV biomass and species identity were collected at 384 sites inter-annually (June–September; 2013–2015) from Mobile Bay, Alabama, to San Antonio Bay, Texas, USA. Coastwide, SAV distribution and biomass were consistent across years, covering an estimated 87,000 ha, and supporting approximately 16 ± 1% total cover with an average biomass of 24.5 ± 1.9 g m−2. Differences in hydrology (i.e., precipitation, freshwater input, water depth) and exposure (i.e., wave and wind energy) manifested in unique SAV assemblages and biomass distributions across the region (i.e., Coastal Mississippi-Alabama, Mississippi River Coastal Wetlands, Chenier Plain, Texas Mid-Coast) and estuarine gradient (i.e., marsh zones defined as fresh, intermediate, brackish, saline). Descriptive cluster analyses identified indicator SAV species, known as medoid observations that represented combined salinity, turbidity, and depth conditions unique to different region and marsh zone combinations. While the presence of SAV is often used as an indicator of ecological health, identifying a medoid-based SAV indicator species in aquatic habitats can be used to describe estuarine conditions in more detail and develop aquatic habitat zones. Exploration and the use of this type of field data could be developed as a means to track, manage, and define aquatic habitats across regional and estuarine gradients and further develop ecosystem-based assessment and restoration activities. Identifying aquatic zones through a representative medoid associates SAV species with locations defined by both long-term salinity and salinity variability, water depth, and exposure, which is a powerful potential tool for managers and restoration decision-makers.
","language":"English","publisher":"Springer Link","doi":"10.1007/s12237-021-00958-7","usgsCitation":"DeMarco, K., Hillmann, E., Nyman, J.A., Couvillion, B., and La Peyre, M., 2022, Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data: Estuaries and Coasts, v. 45, p. 148-167, https://doi.org/10.1007/s12237-021-00958-7.","productDescription":"20 p.","startPage":"148","endPage":"167","ipdsId":"IP-121908","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":500008,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/598","text":"External Repository"},{"id":396544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.822265625,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 26.60817437403311\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2021-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"DeMarco, K. 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However, dams block migrations and alter the hydrologic and thermal regimes influencing movement behaviour of freshwater fishes. In North America, many recovering southern Lake Sturgeon populations occur in rivers with hydroelectric dams, but few studies have examined the impact of hydrologic alteration on their seasonal movements. We conducted a 3-year telemetry study of 96 adult and subadult Lake Sturgeon to compare their migratory responses to temperature and hydrology in adjacent regulated and unregulated tributaries of the Missouri River. Many other populations of Lake Sturgeon use tributaries primarily for spring spawning; however, in our study, Lake Sturgeon used Missouri River tributaries during 78% of the year. Differences in river size, hydrologic and thermal regimes in the regulated Osage River may have contributed to the greater year-round residency, later initiation, more frequent directional changes and longer duration of spring migrations compared to the unregulated Gasconade River. Lake Sturgeon made spring upstream migrations at temperatures of 13–19°C and elevated discharges in both rivers. However, Osage River migrants responded less to changes in discharge or temperature during spring migrations, especially those that overwintered at upstream locations. Fall tributary migrations occurred in the Osage River at rising or high discharges but were uncommon in the Gasconade River. Our identification of the influences of abiotic variables on the timing, duration and extent of Lake Sturgeon seasonal migrations can help guide management of habitat and hydrology in regulated rivers to recover migratory fishes globally.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2362","usgsCitation":"Moore, M., Paukert, C.P., Brooke, B., and Moore, T., 2022, Lake sturgeon seasonal movements in regulated and unregulated Missouri River tributaries: Ecohydrology, v. 15, no. 1, e2362, 17 p., https://doi.org/10.1002/eco.2362.","productDescription":"e2362, 17 p.","ipdsId":"IP-130803","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, M.J.","contributorId":341714,"corporation":false,"usgs":false,"family":"Moore","given":"M.J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooke, B.","contributorId":341723,"corporation":false,"usgs":false,"family":"Brooke","given":"B.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, T.","contributorId":257287,"corporation":false,"usgs":false,"family":"Moore","given":"T.","affiliations":[],"preferred":false,"id":908886,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225169,"text":"70225169 - 2022 - A stable isotope record of late Quaternary hydrologic change in the northwestern Brooks Range, Alaska (eastern Beringia)","interactions":[],"lastModifiedDate":"2023-03-24T17:01:40.985278","indexId":"70225169","displayToPublicDate":"2021-09-21T07:54:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2437,"text":"Journal of Quaternary Science","active":true,"publicationSubtype":{"id":10}},"title":"A stable isotope record of late Quaternary hydrologic change in the northwestern Brooks Range, Alaska (eastern Beringia)","docAbstract":"A submillennial-resolution record of lake water oxygen isotope composition (δ18O) from chironomid head capsules is presented from Burial Lake, northwest Alaska. The record spans the Last Glacial Maximum (LGM; ~20–16k cal a bp) to the present and shows a series of large lake δ18O shifts (~5‰). Relatively low δ18O values occurred during a period covering the LGM, when the lake was a shallow, closed-basin pond. Higher values characterize deglaciation (~16–11.5k cal a bp) when the lake was still closed but lake levels were higher. A rapid decline between ~11 and 10.5k cal a bp indicates that lake levels rose to overflowing. Lake δ18O values are interpreted to reflect the combined effects of changes in lake hydrology, growing season temperature and meteoric source water as well as large-scale environmental changes impacting this site, including opening of the Bering Strait and shifts in atmospheric circulation patterns related to ice-sheet dynamics. The results indicate significant shifts in precipitation minus evaporation across the late Pleistocene to early Holocene transition, which are consistent with temporal patterns of vegetation change and paludification. This study provides new perspectives on the paleohydrology of eastern Beringia concomitant with human migration and major turnover in megafaunal assemblages.
Vernal pools of the northeastern United States provide important breeding habitat for amphibians but may be sensitive to droughts and climate change. These seasonal wetlands typically fill by early spring and dry by mid-to-late summer. Because climate change may produce earlier and stronger growing-season evapotranspiration combined with increasing droughts and shifts in precipitation timing, management concerns include the possibility that some pools will increasingly become dry earlier in the year, potentially interfering with amphibian life-cycle completion. In this context, a subset of pools that continue to provide wetland habitat later into the year under relatively dry conditions might function as ecohydrologic refugia, potentially supporting species persistence even as summer conditions become warmer and droughts more frequent. We used approximately 3,000 field observations of inundation from 449 pools to train machine-learning models that predict the likelihood of pool inundation based on pool size, day of the year, climate conditions, short-term weather patterns, and soil, geologic, and landcover attributes. Models were then used to generate predictions of pool wetness across five seasonal time points, three short-term weather scenarios, and four sets of downscaled climate projections. Model outputs are available through a website allowing users to choose the inundation thresholds, time points, weather scenarios, and future climate projections most relevant to their management needs. Together with long-term monitoring of individual pools at the site scale, this regional-scale study can support amphibian conservation by helping to identify which pools may be most likely to function as ecohydrologic refugia from droughts and climate change.