Stormwater best management practices (BMPs) are engineered structures that attempt to mitigate the impacts of stormwater, which can include nitrogen inputs from the surrounding drainage area. The goal of this study was to assess bacterial community composition in different types of stormwater BMP soils to establish whether a particular BMP type harbors more denitrification potential. Soil sampling took place over the summer of 2015 following precipitation events. Soils were sampled from four bioretention facilities, four dry ponds, four surface sand filters, and one dry swale. 16S rRNA gene analysis of extracted DNA and RNA amplicons indicated high bacterial diversity in the soils of all BMP types sampled. An abundance of denitrifiers was also indicated in the extracted DNA using presence/absence of nirS, nirK, and nosZ denitrification genes. BMP soil bacterial communities were impacted by the surrounding soil physiochemistry. Based on the identification of a metabolically-active community of denitrifiers, this study has indicated that denitrification could potentially occur under appropriate conditions in all types of BMP sampled, including surface sand filters that are often viewed as providing low potential for denitrification. The carbon content of incoming stormwater could be providing bacterial communities with denitrification conditions. The findings of this study are especially relevant for land managers in watersheds with legacy nitrogen from former agricultural land use.
Hybridization is a natural process at species-range boundaries that may variably promote the speciation process or break down species barriers but minimally will influence management outcomes of distinct populations. White-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) have broad and overlapping distributions in North America and a recognized capacity for interspecific hybridization. In response to contemporary environmental change to any of one or multiple still-unknown factors, mule deer range is contracting westward accompanied by a westward expansion of white-tailed deer, leading to increasing interactions, opportunities for gene flow, and associated conservation implications. To quantify genetic diversity, phylogenomic structure, and dynamics of hybridization in sympatric populations of white-tailed and mule deer, we used mitochondrial cytochrome b data coupled with SNP loci discovered with double-digest restriction site-associated DNA sequencing. We recovered 25,018 SNPs across 92 deer samples from both species, collected from two regions of western Kansas. Eight individuals with unambiguous external morphology representing both species were of hybrid origin (8.7%), and represented the product of multi-generational backcrossing. Mitochondrial data showed both ancient and recent directional discordance with morphological species assignments, reflecting a legacy of mule deer males mating with white-tailed deer females. Mule deer had lower genetic diversity than white-tailed deer, and both mitochondrial and nuclear data suggest contemporary mule deer effective population decline. Landscape genetic analyses show relative isolation between the two study regions for white-tailed deer, but greater connectivity among mule deer, with predominant movement from north to south. Collectively, our results suggest a long history of gene flow between these species in the Great Plains and hint at evolutionary processes that purge incompatible functional genomic elements as a result of hybridization. Surviving hybrids evidently may be reproductive, but with unknown consequences for the future integrity of these species, population trajectories, or relative susceptibility to emerging pathogens.
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\"}}]}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Combe, Fraser J.","contributorId":288530,"corporation":false,"usgs":false,"family":"Combe","given":"Fraser","email":"","middleInitial":"J.","affiliations":[{"id":33062,"text":"Division of Biology, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":838057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaster, Levi","contributorId":288531,"corporation":false,"usgs":false,"family":"Jaster","given":"Levi","email":"","affiliations":[{"id":61790,"text":"Kansas Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":838058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ricketts, Andrew","contributorId":288532,"corporation":false,"usgs":false,"family":"Ricketts","given":"Andrew","email":"","affiliations":[{"id":61791,"text":"Wildlife and Outdoor Enterprise Management, Department of Horticulture and Natural Sciences","active":true,"usgs":false}],"preferred":false,"id":838059,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838056,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hope, Andrew G.","contributorId":288533,"corporation":false,"usgs":false,"family":"Hope","given":"Andrew G.","affiliations":[{"id":33062,"text":"Division of Biology, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":838060,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229712,"text":"70229712 - 2022 - Warming conditions boost reproductive output for a northern gopher tortoise population","interactions":[],"lastModifiedDate":"2022-03-16T15:47:19.149065","indexId":"70229712","displayToPublicDate":"2021-12-02T11:45:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Warming conditions boost reproductive output for a northern gopher tortoise population","docAbstract":"The effects of climate change on at-risk species will depend on how life history processes respond to climate and whether the seasonal timing of local climate changes overlaps with species-specific windows of climate sensitivity. For long-lived, iteroparous species like gopher tortoises Gopherus polyphemus, climate likely has a greater influence on reproduction than on adult survival. Our objective was to estimate the timing, magnitude, and direction of climate-driven effects on gopher tortoise reproductive output using a 25 yr dataset collected in southeastern Georgia, USA, near the northern edge of the species’ range. We assessed the timing of climate effects on reproductive output (both probability of reproduction and clutch size) by fitting models with climate covariates (maximum temperature, precipitation, and temperature range) summarized at all possible time intervals (in 1 mo increments) within the 24 mo period prior to the summer census date. We then fit a final model of reproductive output as a function of the identified climate variables and time windows using a Bayesian mixture model. Probability of reproduction was positively correlated with the prior year’s April-May maximum temperature, and clutch size was positively correlated with the prior year’s June maximum temperature. April-May and June maximum temperatures have increased over the past 3 decades at the study site, which likely led to an increase in clutch size of approximately 1 egg (15% increase over a mean of 6.5 eggs). However, the net effect of climate change on gopher tortoise population dynamics will depend on whether there are opposing or reinforcing climate responses for other demographic rates.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01155","usgsCitation":"Hunter, E.A., Loope, K., Drake, K.K., Hanley, K., Jones, D.N., Shoemaker, K., and Rostal, D., 2022, Warming conditions boost reproductive output for a northern gopher tortoise population: Endangered Species Research, v. 46, p. 215-226, https://doi.org/10.3354/esr01155.","productDescription":"12 p.","startPage":"215","endPage":"226","ipdsId":"IP-132399","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449478,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01155","text":"Publisher Index Page"},{"id":397162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Fort Stewart Army Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.683349609375,\n 32.11747489684617\n ],\n [\n -81.727294921875,\n 32.10816944421472\n ],\n [\n -81.78909301757812,\n 32.12910537866883\n ],\n [\n -81.81243896484375,\n 32.10816944421472\n ],\n [\n -81.82891845703125,\n 32.10467965495091\n ],\n [\n -81.85638427734375,\n 32.051152857201714\n ],\n [\n -81.85089111328125,\n 32.0232133942454\n ],\n [\n -81.86187744140625,\n 31.991771310172094\n ],\n [\n -81.89071655273438,\n 31.949831760406877\n ],\n [\n -81.88522338867188,\n 31.91953017247695\n ],\n [\n -81.63116455078124,\n 31.84373252620705\n ],\n [\n -81.62017822265625,\n 31.85773063158148\n ],\n [\n -81.60781860351561,\n 31.85889704445453\n ],\n [\n -81.57073974609375,\n 31.87522527511162\n ],\n [\n -81.55838012695312,\n 31.865895211796346\n ],\n [\n -81.36474609375,\n 31.950997006605856\n ],\n [\n -81.3372802734375,\n 31.948666499428395\n ],\n [\n -81.30020141601562,\n 32.001088607540446\n ],\n [\n -81.41006469726562,\n 32.09769967633269\n ],\n [\n -81.46774291992186,\n 32.10002639514208\n ],\n [\n -81.683349609375,\n 32.11747489684617\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loope, Kevin J.","contributorId":288536,"corporation":false,"usgs":false,"family":"Loope","given":"Kevin J.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":838062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, K. Kristina 0000-0003-0711-7634 kdrake@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-7634","contributorId":3799,"corporation":false,"usgs":true,"family":"Drake","given":"K.","email":"kdrake@usgs.gov","middleInitial":"Kristina","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanley, Kaitlyn","contributorId":97416,"corporation":false,"usgs":true,"family":"Hanley","given":"Kaitlyn","affiliations":[],"preferred":false,"id":838064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Douglas N. Jr.","contributorId":288539,"corporation":false,"usgs":false,"family":"Jones","given":"Douglas","suffix":"Jr.","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":838065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":838066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rostal, David C.","contributorId":288543,"corporation":false,"usgs":false,"family":"Rostal","given":"David C.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":838067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255072,"text":"70255072 - 2022 - An introduction to decision science for conservation","interactions":[],"lastModifiedDate":"2024-06-12T16:17:16.570459","indexId":"70255072","displayToPublicDate":"2021-12-02T10:51:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"An introduction to decision science for conservation","docAbstract":"Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13868","usgsCitation":"Hemming, V., Camaclang, A.E., Adams, M., Burgman, M., Carbeck, K., Carwardine, J., Chades, I., Chalifour, L., Converse, S.J., Davidson, L., Garrard, G.E., Finn, R., Fleri, J.R., Huard, J., Mayfield, H., McDonald Madden, E., Naujokaitis-Lewis, I., Possingham, H.P., Rumpff, L., Runge, M.C., Stewart, D., Tulloch, V.J., Walshe, T., and Martin, T.G., 2022, An introduction to decision science for conservation: Conservation Biology, v. 36, no. 1, e13868, 16 p., https://doi.org/10.1111/cobi.13868.","productDescription":"e13868, 16 p.","ipdsId":"IP-126293","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449481,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9302662","text":"External 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mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903574,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Stewart, Daniel","contributorId":338761,"corporation":false,"usgs":false,"family":"Stewart","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":903575,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Tulloch, Vivitskaia J. 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However, the degree to which decadal-scale glacier retreat is associated with systematic or substantial changes in overall coarse sediment export, with the potential to impact downstream river dynamics, remains poorly understood. Here, we use repeat topographic surveys to assess geomorphic change in four partly-glaciated basins on a stratovolcano (Mount Rainier) in Washington State at roughly decadal intervals from 1960 to 2017. The proglacial extents of the four basins show distinct geomorphic trajectories, ranging from substantial and sustained net erosion to relatively inactive with net deposition. These different trajectories correspond to differences in initial (1960) valley floor gradients, and can be effectively understood as valley floor grade adjustments. Significant erosion was most often accomplished by debris flows triggered by extreme rainfall or glacial outburst floods, though a single rockfall mobilized more material than all other events combined. Year-to-year runoff events had little measurable geomorphic impact. Exported material tended to accumulate in broad deposits within several kilometers of source areas and largely remained there through the end of the study period. Over 10- to 100-year timescales, we nd that the impact of glacier retreat on coarse sediment yield may then vary substantially according to the geometry and storage trends of the newly-exposed valley floor; the timing of that response may also be dictated, and potentially obscured, by stochastic and/or extreme events.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5274","usgsCitation":"Anderson, S.W., and Shean, D., 2022, Spatial and temporal controls on proglacial erosion rates: A comparison of four basins on Mount Rainier, 1960 to 2017: Earth Surface Processes and Landforms, v. 47, no. 2, p. 596-617, https://doi.org/10.1002/esp.5274.","productDescription":"22 p.","startPage":"596","endPage":"617","ipdsId":"IP-119858","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436036,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9056ZNG","text":"USGS data release","linkHelpText":"Supporting Datasets for Proglacial Topographic Change Analyses on Mount Rainier, 1960 to 2017"},{"id":392384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85073852539064,\n 46.78971755817767\n ],\n [\n -121.6351318359375,\n 46.78971755817767\n ],\n [\n -121.6351318359375,\n 46.930572093016316\n ],\n [\n -121.85073852539064,\n 46.930572093016316\n ],\n [\n -121.85073852539064,\n 46.78971755817767\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shean, David 0000-0003-3840-3860","orcid":"https://orcid.org/0000-0003-3840-3860","contributorId":269624,"corporation":false,"usgs":false,"family":"Shean","given":"David","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":827602,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226653,"text":"70226653 - 2022 - Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services","interactions":[],"lastModifiedDate":"2021-12-02T16:19:39.846812","indexId":"70226653","displayToPublicDate":"2021-12-02T10:16:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services","docAbstract":"Social Values for Ecosystem Services (SolVES) version 4.0 is a fully open-source, GIS-based tool designed to aid in the creation of quantitative, spatially explicit models of the nonmonetary values attributed to cultural ecosystem services, such as aesthetics and recreation, specifically to facilitate their incorporation into larger ecosystem service assessments. Newly redeveloped for QGIS, SolVES can be applied in a wide variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban settings worldwide. Redeveloping SolVES for an open-source platform was intended to expand its user base by eliminating the cost of proprietary GIS software licenses and to remove the impact of proprietary software changes on SolVES development. Providing additional options would enable users to delineate relevant stakeholder groups to better assess how differing preferences impact the intensity and spatial distribution of perceived social values.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105259","usgsCitation":"Sherrouse, B.C., Semmens, D., and Ancona, Z.H., 2022, Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services: Environmental Modelling & Software, v. 148, p. 1-16, https://doi.org/10.1016/j.envsoft.2021.105259.","productDescription":"105259, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-127459","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":449484,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105259","text":"Publisher Index Page"},{"id":392382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827600,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230805,"text":"70230805 - 2022 - Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system","interactions":[],"lastModifiedDate":"2022-04-26T14:48:25.006386","indexId":"70230805","displayToPublicDate":"2021-12-02T09:25:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system","docAbstract":"Water resource managers are faced with difficult decisions on how to satisfy human water needs while maintaining or restoring riverine ecosystems. Decision sciences have developed approaches and tools that can be used to break down difficult water management decisions into their component parts. An essential aspect of these approaches is the use of quantitative models to evaluate alternative management strategies. Here, we describe four integrated decision support models for evaluating the effect of flows on two life history stages of Chinook salmon (Oncorhynchus tshawytscha) and Steelhead (O. mykiss). We then use constrained nonlinear optimization to identify optimal flow regimes for the water year type with the least available water. These flow regimes were then used by managers to develop candidate minimum flow strategies that were evaluated using forward simulation and sensitivity analyses. We found that optimal flow regimes differed markedly from existing regulations and varied among species and life history stages. However, evaluation of tradeoffs among the four competing objectives indicated relatively minimal losses for most objectives when the optimal flows were based on equally weighting the objectives. Sensitivity analysis indicated that water temperature was the primary driver of estimated outcomes and suggested that managers consider alternative means of managing temperatures. Decision sciences have created multiple analytical tools and approaches that simplify complex problems, such as water resource management, and we believe that water resource management would benefit from their increased use.
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Quantile regression is applied to the annual peak streamflow distributions at 2683 sites in the contiguous United States to test for trends in the 10th quantile (floods with a 0.9 annual exceedance probability), the 50th quantile (median annual flood), and 90th quantile (floods with a 0.1 annual exceedance probability). Trends are most common (36% of sites) for the median annual flood (50th quantile) and often coherent with trends in both frequent small floods (10th quantile) and infrequent large floods (90th quantile). Changes in the at-site variance of annual peak streamflow, indicated by convergence (decreasing variance) or divergence (increasing variance) of the 10th and 90th quantiles over time, are primarily in response to reservoir operation or urban development rather than climate. An analysis of synthetic series generated from nonstationary distributions demonstrates that quantile regression and standard trend tests used in flood frequency analysis have limited power and high rates of false negatives (>70%) when a test has a significance of p = 0.05. Quantile regression and tests with lower significance complement standard trend testing to inform flood risk management.
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These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. Lakes are warming, resulting in the loss of many native fish. In order to manage economically valuable fisheries and other ecosystem services provided by lakes, it is important for managers to have access to accurate estimates of water temperature to better understand past change and to plan for potential future further warming. These data are invaluable for making decisions such as whether to continue stocking plans as usual in certain lakes or how to set specific harvest limits. 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Dakota\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854190,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237342,"text":"70237342 - 2022 - Physics-guided machine learning from simulation data: An application in modeling lake and river systems","interactions":[],"lastModifiedDate":"2022-10-11T16:20:24.97395","indexId":"70237342","displayToPublicDate":"2021-12-01T11:12:09","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Physics-guided machine learning from simulation data: An application in modeling lake and river systems","docAbstract":"This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, these models often produce biased simulations due to inaccurate parameterizations or approximations used to represent the true physics. In this paper, we aim to build a new data-driven framework to monitor dynamical systems by extracting general scientific knowledge embodied in simulation data generated by the physics-based model. To handle the bias in simulation data caused by imperfect parameterization, we propose to extract general physical relations jointly from multiple sets of simulations generated by a physics-based model under different physical parameters. In particular, we develop a spatio-temporal network architecture that uses its gating variables to capture the variation of physical parameters. We initialize this model using a pre-training strategy that helps discover common physical patterns shared by different sets of simulation data. Then we fine-tune it using limited observation data via a contrastive learning process. By leveraging the complementary strength of machine learning and domain knowledge, our method has been shown to produce accurate predictions, use less training samples and generalize to out-of-sample scenarios. We further show that the method can provide insights about the variation of physical parameters over space and time in two domain applications: predicting temperature in streams and predicting temperature in lakes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IEEE International Conference on Data Mining","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"IEEE International Conference on Data Mining","conferenceDate":"December 7-10, 2021","conferenceLocation":"Auckland, New Zealand","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/ICDM51629.2021.00037","usgsCitation":"Jia, X., Xie, Y., Li, S., Chen, S., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., and Read, J., 2022, Physics-guided machine learning from simulation data: An application in modeling lake and river systems, in IEEE International Conference on Data Mining, Auckland, New Zealand, December 7-10, 2021, p. 270-279, https://doi.org/10.1109/ICDM51629.2021.00037.","productDescription":"10 p.","startPage":"270","endPage":"279","ipdsId":"IP-126776","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":854197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Sheng","contributorId":297449,"corporation":false,"usgs":false,"family":"Li","given":"Sheng","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":854199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854202,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854201,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854204,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229837,"text":"70229837 - 2022 - Diadophis punctatus (Ring-necked snake)","interactions":[],"lastModifiedDate":"2022-07-22T15:34:15.200685","indexId":"70229837","displayToPublicDate":"2021-12-01T10:26:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Diadophis punctatus (Ring-necked snake)","title":"Diadophis punctatus (Ring-necked snake)","docAbstract":"No abstract available.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Kidder, R., Glorioso, B., and Gray, K.D., 2022, Diadophis punctatus (Ring-necked snake): Herpetological Review, v. 52, no. 4.","productDescription":"1 p.","startPage":"798","ipdsId":"IP-132431","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":404347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397338,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"United States","state":"Louisiana","county":"St. Mary Parish","otherGeospatial":"Bayou Teche National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.54563903808594,\n 29.76199310960153\n ],\n [\n -91.49894714355469,\n 29.76199310960153\n ],\n [\n -91.49894714355469,\n 29.798346993042582\n ],\n [\n -91.54563903808594,\n 29.798346993042582\n ],\n [\n -91.54563903808594,\n 29.76199310960153\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kidder, Raymond P 0000-0002-7102-788X","orcid":"https://orcid.org/0000-0002-7102-788X","contributorId":288962,"corporation":false,"usgs":false,"family":"Kidder","given":"Raymond P","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":838508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glorioso, Brad 0000-0002-5400-7414","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":219360,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Katie D","contributorId":288963,"corporation":false,"usgs":false,"family":"Gray","given":"Katie","email":"","middleInitial":"D","affiliations":[{"id":61912,"text":"Nicholls State University","active":true,"usgs":false}],"preferred":false,"id":838510,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236035,"text":"70236035 - 2022 - Data resources for NGA-subduction project","interactions":[],"lastModifiedDate":"2024-02-28T16:15:16.891191","indexId":"70236035","displayToPublicDate":"2021-12-01T10:09:11","publicationYear":"2022","noYear":false,"publicationType":{"id":26,"text":"Extramural-Authored Publication Paper"},"publicationSubtype":{"id":31,"text":"Extramural-Authored Publication"},"title":"Data resources for NGA-subduction project","docAbstract":"A relational database was developed over a five-year period to support ground motion model (GMM) development for the Next Generation Attenuation-Subduction (NGA-Sub) project. The relational database has components that interact according to a database schema, including a source and path component used to describe attributes of seismic sources in global subduction regions and to compute source-to-site distances, a site component that describes attributes of sites where recordings have been made, and a ground motion component.
The source component of the database has information for 1880 earthquakes, mainly from the following regions: the Pacific Northwest region of North America, Alaska and the Aleutian Islands, Japan, Taiwan, New Zealand, South America, Central America, and Mexico. Of the 1880 earthquakes, 88 have finite fault models (FFMs) from the literature that were systematically reviewed, distilled to one more rectangular shapes, and trimmed according to procedures based on percentage of total slip. For earthquakes without FFMs, a simulation routine is used to represent finite fault effects required for distance calculations. This simulation routine was adjusted and made more uniform in its application than in prior NGA projects. All earthquakes are classified as interface, intraslab, shallow crustal, or outer rise, using uniform protocols developed for this project. All earthquakes are also assigned class designations adapted from a prior NGA project for active regions, that allows foreshock, mainshock, and aftershock events to be distinguished.
The site component of the database is described in a companion paper (Ahdi et al. 2020 [1]).
The ground motion component of the database consists of median – and maximum – horizontal component peak parameters (peak ground acceleration, PGA and peak ground velocity, PGV) and pseudo-spectral accelerations (PSa) at 111 oscillator periods and 11 damping ratios. Response spectra were also computed for the vertical component. Fourier amplitude spectra (FAS) and duration metrics were also computed. The ground motion recordings were obtained from collaborating organizations world-wide as uncorrected (Vol 1) digital recordings, that were corrected (componentspecific low – and high – pass filters and baseline correction, as needed) following Pacific Earthquake Engineering Research Center (PEER)/NGA protocols.
The relational database operates on each of these (and other) database components to dynamically draw relevant parameters into a single file, known as a flatfile, that is used by researchers engaged in GMM development. The flatfiles used in model development are being published with the NGA-Sub GMMs as products of the NGA-Sub project.
","conferenceTitle":"The 17th World Conference on Earthquake Engineering","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association of Earthquake Engineering","usgsCitation":"Contreras, V., Mazzoni, S., Kishida, T., Ahdi, S., Darragh, R.B., Youngs, R., Chiou, B., Kuehn, N., Wooddell, K., Bozorgnia, Y., and Stewart, J.P., 2022, Data resources for NGA-subduction project, 12 p.","productDescription":"12 p.","ipdsId":"IP-130876","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":426071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426069,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"}],"noUsgsAuthors":true,"publicationStatus":"PW","contributors":{"authors":[{"text":"Contreras, V.","contributorId":295706,"corporation":false,"usgs":false,"family":"Contreras","given":"V.","email":"","affiliations":[{"id":63911,"text":"University of California, Los Angeles, USA","active":true,"usgs":false}],"preferred":false,"id":849767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mazzoni, S.","contributorId":270337,"corporation":false,"usgs":false,"family":"Mazzoni","given":"S.","affiliations":[{"id":56148,"text":"University of California, Los Angeles, CA 90095","active":true,"usgs":false}],"preferred":false,"id":849765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kishida, T.","contributorId":203476,"corporation":false,"usgs":false,"family":"Kishida","given":"T.","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":849768,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ahdi, S.K.","contributorId":334403,"corporation":false,"usgs":false,"family":"Ahdi","given":"S.K.","affiliations":[{"id":80128,"text":"Exponent Failure Analysis, Los Angeles, CA","active":true,"usgs":false}],"preferred":false,"id":849764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Darragh, Robert B.","contributorId":25188,"corporation":false,"usgs":false,"family":"Darragh","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":895558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Youngs, R.R.","contributorId":75312,"corporation":false,"usgs":true,"family":"Youngs","given":"R.R.","email":"","affiliations":[],"preferred":false,"id":895559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chiou, B.S.J.","contributorId":74119,"corporation":false,"usgs":true,"family":"Chiou","given":"B.S.J.","email":"","affiliations":[],"preferred":false,"id":895560,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kuehn, N.","contributorId":334404,"corporation":false,"usgs":false,"family":"Kuehn","given":"N.","affiliations":[],"preferred":false,"id":895561,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wooddell, Kathryn","contributorId":47674,"corporation":false,"usgs":false,"family":"Wooddell","given":"Kathryn","email":"","affiliations":[{"id":13174,"text":"Pacific Gas & Electric","active":true,"usgs":false}],"preferred":false,"id":895562,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bozorgnia, Y.","contributorId":51427,"corporation":false,"usgs":true,"family":"Bozorgnia","given":"Y.","affiliations":[],"preferred":false,"id":895563,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":849773,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70229457,"text":"70229457 - 2022 - Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks","interactions":[],"lastModifiedDate":"2022-03-09T15:49:19.092846","indexId":"70229457","displayToPublicDate":"2021-12-01T09:44:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks","docAbstract":"Environmental management often requires making decisions despite system uncertainty. One such example is mudflat mediation in flood control reservoirs. Reservoir mudflats limit development of diverse fish assemblages due to the lack of structural habitat provided by plants. Seeding mudflats with agricultural plants may mimic floodplain wetlands once inundated and provide fish habitat and achieve habitat management objectives. However, planting success is uncertain because of unpredictable water level fluctuations that affect plant survival and growth. Decision support tools can account for uncertainty that influences decision outcomes and reduce the risk in reservoir mudflat planting decisions. We used Bayesian decision networks and sensitivity analyses to quantify uncertainty surrounding mudflat plantings as supplemental fish habitat in four northwest Mississippi reservoirs. When averaged across all uncertainty, planting was the optimal decision only in Enid Lake. Response profiles indicated planting decisions depended on elevation contours within Enid, Sardis, and Grenada reservoirs. No planting was optimal at all elevations for Arkabutla Lake. These results provide a quantified basis for establishing best management practices and identify key system states that influence decision outcomes. The process used in this study to evaluate planting decisions can be applied to any reservoir by modifying reservoir dependent inputs to evaluate planting decisions to provide supplemental fish habitat.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.114139","usgsCitation":"Norris, D.M., Colvin, M.E., Miranda, L.E., and Lashley, M.A., 2022, Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks: Journal of Environmental Management, v. 304, 114139, 12 p., https://doi.org/10.1016/j.jenvman.2021.114139.","productDescription":"114139, 12 p.","ipdsId":"IP-125224","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.15380859375,\n 33.43144133557529\n ],\n [\n -88.681640625,\n 33.43144133557529\n ],\n [\n -88.681640625,\n 34.96699890670367\n ],\n [\n -90.15380859375,\n 34.96699890670367\n ],\n [\n -90.15380859375,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"304","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norris, D. M.","contributorId":271192,"corporation":false,"usgs":false,"family":"Norris","given":"D.","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, M. E.","contributorId":275884,"corporation":false,"usgs":false,"family":"Colvin","given":"M.","email":"","middleInitial":"E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":837531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lashley, M. A.","contributorId":278603,"corporation":false,"usgs":false,"family":"Lashley","given":"M.","email":"","middleInitial":"A.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837532,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236428,"text":"70236428 - 2022 - Multiple lines of evidence for identifying potential hazards to fish from contaminants of emerging concern in Great Lakes tributaries","interactions":[],"lastModifiedDate":"2022-09-07T12:05:20.977639","indexId":"70236428","displayToPublicDate":"2021-11-30T07:00:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Multiple lines of evidence for identifying potential hazards to fish from contaminants of emerging concern in Great Lakes tributaries","docAbstract":"Contaminants of emerging concern (CECs; e.g., pharmaceuticals, flame retardants, pesticides, and industrial chemicals) are omnipresent throughout tributaries to the Great Lakes. Furthermore, CECs are often present at concentrations that are potentially hazardous to aquatic species. Since 2010, we characterized the presence of CECs at 309 sites within 47 Great Lakes tributaries and characterized responses of fathead minnow (Pimephales promelas) exposed to river water at a subset of 26 sites within four tributaries. Our work resulted in three independent lines of evidence related to the potential hazards of CEC exposure to fish. First, vulnerability (where vulnerability refers to likelihood) of surface waters to CEC presence was predicted using select watershed characteristics. Second, hazard to fish (where hazard means the potential for adverse biological responses) was predicted using screening values for a subset of CECs. Third, biological responses of fathead minnow exposed to river water in streamside exposures were measured. We assessed the congruence of these three lines of evidence for identifying sites with elevated hazards to CEC exposure. Predicted vulnerability and hazards agreed at 66% of all sites. Where the two indices did not agree, vulnerability often underestimated predicted hazard. When compared with measured biological responses from streamside exposures, predicted hazards agreed for 42% of samples. Furthermore, when predicted hazards for specific effect categories were compared with similar measured biomarkers, 26% and 46% of samples agreed for reproductive and physiological effect categories, respectively. Overall, vulnerability and hazard predictions tended to overestimate the measured biological responses, providing a protective estimate of the potential hazards of CEC exposure to fish. When used together, these three approaches can help resource managers prioritize management activities in minimizing hazards of CEC exposure and can be used by researchers to prioritize studies focused on understanding the hazards of CEC exposure to fish. Integr Environ Assess Manag 2022;18:1246–1259. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by US Government employees and their work is in the public domain in the USA.
We identify aspects of finite‐source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite‐fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D‐source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line‐source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D‐source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground‐motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.
Extensive urbanization in coastal southern California has reduced natural habitat in this biodiversity hotspot. To better conserve ecological communities, state and federal agencies, along with local jurisdictions and private stakeholders, developed regional conservation plans for southern California. Although many protected areas exist within this region, the patchwork nature of these protected areas might not provide good coverage for species that require multiple habitat components, such as amphibians with complex life histories. Because of declines in the past century, the status of the western spadefoot (Spea hammondii) in southern California is of concern to state and federal wildlife agencies. Species distribution models (SDMs) can aid in determining the conservation status of imperiled species by projecting where suitable habitat remains and how much is protected from further development. We built SDMs that integrated site-occupancy data from systematic pitfall trapping surveys and presence-only data from biodiversity databases and citizen science platforms to project the current distribution of western spadefoots in southern California. Western spadefoot occurrence was positively related to the cover of grassland or shrub/scrub and the % sand in the soil within a 1000 m buffer, and was negatively related to slope, elevation, and distance to ephemeral streams or vernal pools. Most of the remaining unprotected habitat for western spadefoots is in the southern half of its historical range in western San Diego and Riverside counties. A few large tracts of spadefoot habitat exist on U.S. Department of Defense lands and smaller tracts remain on ecological reserves owned by state and local government agencies. Only small patches of habitat remain in the northern half of this clade’s historical range in Ventura, Orange, Los Angeles, and San Bernardino counties. Existing regional conservation plans provide ostensible spatial coverage of the majority of extant habitat for western spadefoots in southern California, but most of the habitat within the jurisdiction of these plans lacks formal protection, exposing this species to further declines as urbanization continues in the 21st century.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01944","usgsCitation":"Rose, J.P., Halstead, B., Packard, R.H., and Fisher, R., 2022, Projecting the remaining habitat for the western spadefoot (Spea hammondii) in heavily urbanized southern California: Global Ecology and Conservation, v. 33, e01944, 16 p., https://doi.org/10.1016/j.gecco.2021.e01944.","productDescription":"e01944, 16 p.","ipdsId":"IP-123687","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01944","text":"Publisher Index Page"},{"id":486326,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YYKW1H","text":"USGS data release","linkHelpText":"Code to fit integrated Species Distribution Models to occurrence data for the Western Spadefoot (Spea hammondii) in Southern California."},{"id":414543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"southern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.03709900369654,\n 32.618965807566894\n ],\n [\n -116.03709900369654,\n 34.57584559465637\n ],\n [\n -118.93425527477612,\n 34.57584559465637\n ],\n [\n -118.93425527477612,\n 32.618965807566894\n ],\n [\n -116.03709900369654,\n 32.618965807566894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Packard, Robert H.","contributorId":303286,"corporation":false,"usgs":false,"family":"Packard","given":"Robert","email":"","middleInitial":"H.","affiliations":[{"id":65748,"text":"Western Riverside County Multiple Species Habitat Conservation Plan","active":true,"usgs":false}],"preferred":false,"id":867028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867029,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226723,"text":"70226723 - 2022 - The silence of the clams: Forestry registered pesticides as multiple stressors on soft-shell clams","interactions":[],"lastModifiedDate":"2022-03-15T16:17:45.230156","indexId":"70226723","displayToPublicDate":"2021-11-29T06:41:43","publicationYear":"2022","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":"The silence of the clams: Forestry registered pesticides as multiple stressors on soft-shell clams","docAbstract":"Contaminants are ubiquitous in the environment, often reaching aquatic systems. Combinations of forestry use pesticides have been detected in both water and aquatic organism tissue samples in coastal systems. Yet, most toxicological studies focus on the effects of these pesticides individually, at high doses, and over acute time periods, which, while key for establishing toxicity and safe limits, are rarely environmentally realistic. We examined chronic (90 days) exposure by the soft-shell clam, Mya arenaria, to environmentally relevant concentrations of four pesticides registered for use in forestry (atrazine, 5 μg/L; hexazinone, 0.3 μg/L; indaziflam, 5 μg/L; and bifenthrin, 1.5 μg/g organic carbon (OC)). Pesticides were tested individually and in combination, except bifenthrin, which was tested only in combination with the other three. We measured shell growth and condition index every 30 days, as well as feeding rates, mortality, and chemical concentrations in tissue from a subset of clams at the end of the experiment to measure contaminant uptake. Indaziflam caused a high mortality rate (max. 36%), followed by atrazine (max. 27%), both individually as well as in combination with other pesticides. Additionally, indaziflam concentrations in tissue (61.70–152.56 ng/g) were higher than those of atrazine (26.48–48.56 ng/g), despite equal dosing concentrations, indicating higher tissue accumulation. Furthermore, clams exposed to indaziflam and hexazinone experienced reduced condition index and clearance rates individually and in combination with other compounds; however, the two combined did not result in significant mortality. These two compounds, even at environmentally relevant concentrations, affected a non-target organism and, in the case of the herbicide indaziflam, accumulated in clam tissue and appeared more toxic than other tested pesticides. These findings underscore the need for more comprehensive studies combining multiple compounds at relevant concentrations to understand their impacts on aquatic ecosystems.
Populations of Brook Trout Salvelinus fontinalis exhibit large variation in annual recruitment (abundance of young of the year [age 0]), which is likely a product of density-dependent and density-independent factors. Quantifying the importance of each of these mechanisms in regulating Brook Trout recruitment would be valuable to managers that are responsible for the conservation of this iconic species throughout its native range. We analyzed a time series of age-0 and adult Brook Trout density data collected from 10 streams in the Sinnemahoning Creek watershed, north-central Pennsylvania (2010–2019), using Bayesian hierarchical modeling to partition the density-dependent effects of adult density and the density-independent effects of elevated streamflow on Brook Trout recruitment. Multiple models were examined, and the top-ranked model showed that Brook Trout recruitment followed a Ricker stock–recruitment relationship, with annual recruitment negatively influenced by maximum streamflow during the spring season (March–April). This model will be useful in predicting future variation in Brook Trout recruitment under climate change scenarios in which the frequency and intensity of high-flow events are expected to increase.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10347","usgsCitation":"Sweka, J., and Wagner, T., 2022, Influence of seasonal extreme flows on Brook Trout recruitment: North American Journal of Fisheries Management, v. 151, no. 2, p. 231-244, https://doi.org/10.1002/tafs.10347.","productDescription":"14 p.","startPage":"231","endPage":"244","ipdsId":"IP-130483","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Sinnemahoning Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.64013671875,\n 41.22824901518529\n ],\n [\n -77.67333984375,\n 41.22824901518529\n ],\n [\n -77.67333984375,\n 41.96765920367816\n ],\n [\n -78.64013671875,\n 41.96765920367816\n ],\n [\n -78.64013671875,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweka, John A.","contributorId":288581,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":838107,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240333,"text":"70240333 - 2022 - Accuracy and precision of otolith-derived age Interpretations for known-age lake trout","interactions":[],"lastModifiedDate":"2023-02-06T13:21:49.565349","indexId":"70240333","displayToPublicDate":"2021-11-27T07:18:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy and precision of otolith-derived age Interpretations for known-age lake trout","docAbstract":"Catch-at-age data are used to inform important management decisions for recovering populations of Lake Trout Salvelinus namaycush. Age data for Lake Trout are commonly derived from interpretation of annual growth marks (annuli) on the fish’s otoliths. Due to the tendency for annuli to vary in appearance and the subjectivity that is inherent to any age interpretation method, it is important that the common sources of interpretation error be well understood for any aging method used to inform management plans. In this study, coded wire tags were used to establish true ages for 153 Lake Trout to measure the precision and accuracy of age interpretations made from transverse-sectioned otoliths and to identify sources of potential age interpretation error that researchers and managers may encounter when using this method. Precision of age interpretations, as measured by average coefficient of variation, ranged from 7.9% to 9.2%. Accuracy of age interpretations varied among readers, with exact matches ranging from 41.8% to 53.6% and accuracy within ±1 year ranging from 81.0% to 83.0%. Age interpretation errors were more likely to be overestimates of true age for Lake Trout under age 7 and underestimates for Lake Trout over age 13. However, only reader 1 exhibited significant systematic bias in their age interpretations. Poor clarity of the first annuli, growth checks resembling annuli, and faintness of narrow annuli near otolith margins in older fish were identified as likely sources of interpretation error in this study. A digital reference collection of known-age Lake Trout otoliths is provided as supplemental material in the online version of this article. This collection can be used for training new readers, measuring the accuracy of age interpretations, and monitoring for aging bias by anyone using otoliths to obtain age data for Lake Trout.
Crude oil is known to induce developmental defects in teleost fish exposed during early life stages (ELSs). While most studies in recent years have focused on cardiac endpoints, evidence from whole-animal transcriptomic analyses and studies with individual polycyclic aromatic hydrocarbons (PAHs) indicate that the developing kidney (i.e., pronephros) is also at risk. Considering the role of the pronephros in osmoregulation, and the common observance of edema in oil-exposed ELS fish, surprisingly little is known regarding the effects of oil exposure on pronephros development and function. Using zebrafish (Danio rerio) ELSs, we assessed the transcriptional and morphological responses to two dilutions of high-energy water accommodated fractions (HEWAF) of oil from the Deepwater Horizon oil spill using a combination of qPCR and whole-mount in situ hybridization (WM-ISH) of candidate genes involved in pronephros development and function, and immunohistochemistry (WM-IHC). To assess potential functional impacts on the pronephros, three 24 h osmotic challenges (2 hypo-osmotic, 1 near iso‐osmotic) were implemented at two developmental time points (48 and 96 h post fertilization; hpf) following exposure to HEWAF. Changes in transcript expression level and location specific to different regions of the pronephros were observed by qPCR and WM-ISH. Further, pronephros morphology was altered in crude oil exposed larvae, characterized by failed glomerulus and neck segment formation, and straightening of the pronephric tubules. The osmotic challenges at 96 hpf greatly exacerbated edema in both HEWAF-exposed groups regardless of osmolarity. By contrast, larvae at 48 hpf exhibited no edema prior to the osmotic challenge, but previous HEWAF exposure elicited a concentration-response increase in edema at hypo-osmotic conditions that appeared to have been largely alleviated under near iso‐osmotic conditions. In summary, ELS HEWAF exposure impaired proper pronephros development in zebrafish, which coupled with cardiotoxic effects, most likely reduced or inhibited pronephros fluid clearance capacity and increased edema formation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.151988","usgsCitation":"Bonatesta, F., Emadi, C., Price, E.R., Wang, Y., Greer, J.B., Xu, E.G., Schlenk, D., Grosell, M., and Mager, E.M., 2022, The developing zebrafish kidney is impaired by Deepwater Horizon crude oil early-life stage exposure: A molecular to whole-organism perspective: Science of the Total Environment, v. 808, 151988, 15 p., https://doi.org/10.1016/j.scitotenv.2021.151988.","productDescription":"151988, 15 p.","ipdsId":"IP-134626","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":449509,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.scitotenv.2021.151988","text":"External Repository"},{"id":396107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"808","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bonatesta, Fabrizio","contributorId":279576,"corporation":false,"usgs":false,"family":"Bonatesta","given":"Fabrizio","email":"","affiliations":[{"id":57294,"text":"Department of Biological Sciences and the Advanced Environmental Research Institute, University of North Texas, Denton, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":835145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Emadi, Cameron","contributorId":279577,"corporation":false,"usgs":false,"family":"Emadi","given":"Cameron","email":"","affiliations":[{"id":57294,"text":"Department of Biological Sciences and the Advanced Environmental Research Institute, University of North Texas, Denton, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":835146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Price, Edwin R.","contributorId":279578,"corporation":false,"usgs":false,"family":"Price","given":"Edwin","email":"","middleInitial":"R.","affiliations":[{"id":57294,"text":"Department of Biological Sciences and the Advanced Environmental Research Institute, University of North Texas, Denton, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":835147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Yadong","contributorId":279579,"corporation":false,"usgs":false,"family":"Wang","given":"Yadong","email":"","affiliations":[{"id":57296,"text":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":835148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Greer, Justin Blaine 0000-0001-6660-9976","orcid":"https://orcid.org/0000-0001-6660-9976","contributorId":265183,"corporation":false,"usgs":true,"family":"Greer","given":"Justin","email":"","middleInitial":"Blaine","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":835149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Elvis Genbo","contributorId":279580,"corporation":false,"usgs":false,"family":"Xu","given":"Elvis","email":"","middleInitial":"Genbo","affiliations":[{"id":57298,"text":"Department of Biology, University of Southern Denmark, Odense, Denmark","active":true,"usgs":false}],"preferred":false,"id":835150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schlenk, Daniel","contributorId":221106,"corporation":false,"usgs":false,"family":"Schlenk","given":"Daniel","email":"","affiliations":[{"id":12655,"text":"University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":835151,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grosell, Martin","contributorId":279581,"corporation":false,"usgs":false,"family":"Grosell","given":"Martin","email":"","affiliations":[{"id":57299,"text":"Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":835152,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mager, Edward M.","contributorId":279582,"corporation":false,"usgs":false,"family":"Mager","given":"Edward","email":"","middleInitial":"M.","affiliations":[{"id":57294,"text":"Department of Biological Sciences and the Advanced Environmental Research Institute, University of North Texas, Denton, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":835153,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229223,"text":"70229223 - 2022 - Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community","interactions":[],"lastModifiedDate":"2022-03-03T16:58:26.697189","indexId":"70229223","displayToPublicDate":"2021-11-25T10:46:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community","docAbstract":"Disturbances such as fire provide an opportunity for invasive plant species to exploit newly created niche space. Whether initial invaders facilitate, compete with, or do not affect later invaders is important to determine in communities affected by multiple invaders. This analysis focuses on the newer invaders Taeniatherum caput-medusae (medusahead) and Ventenata dubia (ventenata) in sagebrush-steppe communities previously invaded by Bromus tectorum (cheatgrass), during the first 5 years of recovery after wildfire. We combined probabilistic co-occurrence analysis and Getis-Ord spatial clustering analysis to assess relationships between different exotic annual grass species and native and introduced perennial bunchgrasses, then used Bayesian generalized linear models to determine if and how medusahead and ventenata differed in their environmental relationships and thus invasion niches. Medusahead presence was positively associated with both other exotic annual grasses, but ventenata presence was negatively associated with cheatgrass presence. Medusahead hotspots were more spatially similar to cheatgrass hotspots while ventenata hotspots were unique. Both invaders were negatively related to total perennial bunchgrass cover but disassociations between invaders and different perennial bunchgrasses were species-specific. Medusahead and ventenata occupied different niches; medusahead in low elevation, low precipitation areas and ventenata in higher elevation, higher precipitation areas. Despite seemingly similar ecology and growth requirements among these annual grasses and a tendency to be considered uniformly in both research and management, the species appeared to have different invasion niches.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10530-021-02669-3","usgsCitation":"Applestein, C., and Germino, M., 2022, Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community: Biological Invasions, v. 24, p. 741-759, https://doi.org/10.1007/s10530-021-02669-3.","productDescription":"19 p.","startPage":"741","endPage":"759","ipdsId":"IP-131007","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":436038,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TG16C5","text":"USGS data release","linkHelpText":"Presence and cover of exotic annual and perennial grass species during five years post-fire on the Soda Wildfire"},{"id":396709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Owyhee Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.564453125,\n 42.00848901572399\n ],\n [\n -116.20239257812499,\n 42.00848901572399\n ],\n [\n -116.20239257812499,\n 44.12702800650004\n ],\n [\n -118.564453125,\n 44.12702800650004\n ],\n [\n -118.564453125,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836972,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230210,"text":"70230210 - 2022 - Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","interactions":[],"lastModifiedDate":"2023-06-09T13:55:46.784692","indexId":"70230210","displayToPublicDate":"2021-11-25T10:23:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","title":"Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","docAbstract":"Estimates of parameters that affect population dynamics, including the size at which individuals reproduce, are crucial for efforts aimed at understanding how imperiled species may recover from the numerous threats they face. In this study, we observed loggerhead marine turtles (Caretta caretta) nesting at three sites in the Gulf of Mexico at sizes assumed nonreproductive in this region (≤87 cm curved carapace length-notch [CCL-n]). These smaller individuals ranged from 74.0 to 86.9 cm CCL-n, and the proportion of smaller nesting loggerheads was 0.13 across three study sites: Gulf Shores, AL; Dry Tortugas National Park, Florida (FL); and Everglades National Park (ENP), FL. The greatest proportion of smaller nesters was observed at ENP at 0.24. Tracking data indicated that the smaller nesters migrated shorter distances and swam in shallower waters compared to the larger nesting loggerheads (>87 cm CCL-n) in our dataset. These results provide valuable information on two of the smallest subpopulations of NW Atlantic loggerheads and understudied ENP turtles. Our results have potential applications in the classification and interpretation of stranding limits and bycatch estimates, population modeling (e.g., stage durations and fecundity), and understanding threats and subpopulation recovery efforts for multiple subpopulations of this imperiled species.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.581","usgsCitation":"Benscoter, A., Smith, B., and Hart, K., 2022, Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation: Conservation Science and Practice, v. 4, no. 1, e581, 14 p.; Data Release, https://doi.org/10.1111/csp2.581.","productDescription":"e581, 14 p.; Data Release","ipdsId":"IP-128726","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":449512,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.581","text":"Publisher Index Page"},{"id":398119,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417872,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96S4B8P"}],"country":"United States","state":"Alabama, Florida","city":"Gulf Shores","otherGeospatial":"Dry Tortugas National Park, Gulf of Mexico, Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.02108764648438,\n 30.181934730780572\n ],\n [\n -87.506103515625,\n 30.181934730780572\n ],\n [\n -87.506103515625,\n 30.349176094149833\n ],\n [\n -88.02108764648438,\n 30.349176094149833\n ],\n [\n -88.02108764648438,\n 30.181934730780572\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.4034423828125,\n 25.348990395713393\n ],\n [\n -80.947265625,\n 25.353954558526684\n ],\n [\n -81.3812255859375,\n 25.997549919572112\n ],\n [\n -81.8646240234375,\n 26.017297563851745\n ],\n [\n -81.419677734375,\n 25.502784548755354\n ],\n [\n -81.1505126953125,\n 25.075648445630527\n ],\n [\n -80.738525390625,\n 25.015928763367857\n ],\n [\n -80.3924560546875,\n 25.19002975536265\n ],\n [\n -80.3594970703125,\n 25.304303764403617\n ],\n [\n -80.3814697265625,\n 25.329131707091477\n ],\n [\n -80.4034423828125,\n 25.348990395713393\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.01544189453125,\n 24.540877160098404\n ],\n [\n -82.75314331054688,\n 24.540877160098404\n ],\n [\n -82.75314331054688,\n 24.729369599118222\n ],\n [\n -83.01544189453125,\n 24.729369599118222\n ],\n [\n -83.01544189453125,\n 24.540877160098404\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Benscoter, Allison 0000-0003-4205-3808","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":216194,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":839564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218324,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839565,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227326,"text":"70227326 - 2022 - Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","interactions":[],"lastModifiedDate":"2022-01-10T12:59:36.251299","indexId":"70227326","displayToPublicDate":"2021-11-24T06:56:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","docAbstract":"Factors affecting groundwater quality used for domestic supply within the Marcellus Shale footprint in north-central and north-east Pennsylvania are identified using a combination of spatial, statistical, and geochemical modeling. Untreated groundwater, sampled during 2011–2017 from 472 domestic wells within the study area, exhibited wide ranges in pH (4.5–9.3), total dissolved solids (TDS, 22–1960 mg/L), sodium (0.3–760 mg/L), chloride (0.3–1020 mg/L), bromide (<0.01–8.6 mg/L), and methane (<0.001–77 mg/L). The wells had depths ranging from 10 to 394 m; 69.5 percent were completed in sandstone bedrock, 19.3 percent in shale, 4.2 percent in siltstone, 4 percent in carbonate, and 3 percent in unconsolidated alluvial or glacial deposits. Groundwater quality in the Delaware River watershed, in the eastern part of the study area where Marcellus gas has not been developed, was similar to that in the Susquehanna, Allegheny, and Genesee River watersheds in the western part of the study area where natural gas production from Marcellus Shale has been ongoing since 2008. Most groundwaters were calcium/bicarbonate type with near-neutral pH; approximately 10 percent were sodium/bicarbonate and 1 percent were sodium/chloride types. Sodium-enriched waters, which were mostly from shale and siltstone aquifers, had the greatest frequency of elevated pH (>8.5) and elevated concentrations of TDS (>250 mg/L), bromide (>0.15 mg/L), methane (>7.0 mg/L), and lithium (>60 μg/L). Geochemical models indicate these characteristics could result from progressive mineral dissolution combined with cation exchange, plus mixing with locally important salinity sources, including as much as 0.7 percent Appalachian Basin brine and/or road-deicing salt. Multivariate correlation models suggest the observed variability in methane concentrations may be attributed to several environmental factors, such as geochemical evolution along groundwater flow paths, redox conditions, and/or mixing with saline groundwater or brine. Most samples having elevated methane were from shale aquifers, which were mainly in the Susquehanna River basin and had the greatest density of gas wells compared to other lithologies. Samples having elevated methane were also observed in the Delaware River watershed and other areas outside gas development. Isotopic compositions of methane for a subset of 39 samples (selected because of elevated methane) and relatively high ratios of methane to ethane in those samples indicated methane could be derived from microbial gas mixed with thermogenic gas that may have undergone degradation and/or fractionation during migration. The methods used in this study could be broadly applicable to understanding major factors affecting groundwater quality, particularly for explaining variations in ionic composition with pH and identifying sources of salinity and associated constituents (e.g. sodium, chloride, bromide, lithium, methane) that may have geogenic or anthropogenic origins.
Golden eagles Aquila chrysaetos are a long-lived and wide-ranging species believed to be stable or in slight decline across North America. Golden eagles have an extended subadult stage (4–5 years) that is critical to maintaining recruitment into the breeding population and population viability. Compared to adult golden eagles, the ecology of subadult golden eagles (hereafter, subadults) has received little attention. We investigated patterns of resource selection for subadults in the Great Basin Desert of the western United States during summer and winter, 2013–2019. We monitored 46 subadults with GPS transmitters and related locations (n = 99 037) with predictors hypothesized to influence seasonal patterns of space use with mixed-effects logistic regression. Subadults selected for ridges and upper slopes in summer and winter, but higher elevations in summer. Subadults showed weak selection for lower ridge density in summer, which was likely facilitated by selection for areas with greater thermal wind current potential. In contrast, subadults showed strong selection for higher ridge density in winter. Subadults selected areas further from roads in summer and closer to roads and electrical transmission lines in winter, which may be related to winter scavenging of road-killed ungulates. Resource selection functions suggested subadults selected for shrublands and woodlands in both seasons, but odds ratios revealed that during winter subadults avoided shrublands and increased selection of woodlands relative to summer. Subadults selected for areas with infrequent fires in both seasons; areas with frequent fires were avoided in summer but selected for in winter. Seasonal changes in resource selection suggested that subadults used woodlands more than expected, potentially reflecting spatial partitioning by subadults to lower-quality habitats to minimize competition with breeding adults during winter when energetic demands for thermoregulation were presumably higher and prey more limited.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1002/wlb3.01002","usgsCitation":"Hixson, K., Slater, S., Knight, R., and Lonsinger, R.C., 2022, Seasonal variation in resource selection by subadult golden eagles in the Great Basin Desert: Wildlife Biology, v. 2022, no. 1, e01002, 14 p., https://doi.org/10.1002/wlb3.01002.","productDescription":"e01002, 14 p.","ipdsId":"IP-128701","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449516,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wlb3.01002","text":"Publisher Index Page"},{"id":433455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"U.S. Army's Dugway Proving Ground","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.79823682094569,\n 40.44232739121148\n ],\n [\n -113.79823682094569,\n 39.897870571302406\n ],\n [\n -112.70671299878512,\n 39.897870571302406\n ],\n [\n -112.70671299878512,\n 40.44232739121148\n ],\n [\n -113.79823682094569,\n 40.44232739121148\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2022","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hixson, K.M.","contributorId":341760,"corporation":false,"usgs":false,"family":"Hixson","given":"K.M.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slater, S.J.","contributorId":341761,"corporation":false,"usgs":false,"family":"Slater","given":"S.J.","email":"","affiliations":[{"id":35596,"text":"HawkWatch International","active":true,"usgs":false}],"preferred":false,"id":908870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, R.N.","contributorId":341763,"corporation":false,"usgs":false,"family":"Knight","given":"R.N.","email":"","affiliations":[{"id":81783,"text":"U.S. Army Dugway Proving Ground","active":true,"usgs":false}],"preferred":false,"id":908871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}