Global dryland vegetation communities will likely change as ongoing drought conditions shift regional climates towards a more arid future. Additional aridification of drylands can impact plant and ground cover, biogeochemical cycles, and plant-soil feedbacks, yet how and when these crucial ecosystem components will respond to drought intensification requires further investigation. Using a long-term precipitation reduction experiment (35% reduction) conducted across the Colorado Plateau and spanning ten years into a 20+ year regional megadrought, we explored how vegetation cover, soil conditions, and growing season nitrogen (N) availability are impacted by drying climate conditions. We observed large declines for all dominant plant functional types (C3 and C4 grasses and C3 and C4 shrubs) across measurement period, both in the drought treatment and control plots, likely due to ongoing regional megadrought conditions. In experimental drought plots, we observed less plant cover, less biological soil crust cover, warmer and drier soil conditions, and more soil resin-extractable N compared to the control plots. Observed increases in soil N availability were best explained by a negative correlation with plant cover regardless of treatment, suggesting that declines in vegetation N uptake may be driving increases in available soil N. However, in ecosystems experiencing long-term aridification, increased N availability may ultimately result in N losses if soil moisture is consistently too dry to support plant and microbial N immobilization and ecosystem recovery. These results show dramatic, worrisome declines in plant cover with long-term drought. Additionally, this study highlights that more plant cover losses are possible with further drought intensification, and underscore that, in addition to large drought effects on aboveground communities, drying trends drive significant changes to critical soil resources such as N availability, all of which could have long-term ecosystem impacts for drylands.
Des Moines Water Works (DMWW) is one of the largest water providers in Iowa and as population growth continues, demand for drinking water is increasing. DMWW uses groundwater and surface water as raw water sources to supply the City of Des Moines and surrounding communities. In response to current and future demands, DMWW is in need of a thorough understanding of local groundwater resources, specifically the Des Moines River alluvial aquifer. The Des Moines River alluvial aquifer is hydraulically connected to the Des Moines River and consists of alluvial deposits and glacial outwash sands and gravels. To ensure a sustainable groundwater supply, additional information to better understand and manage groundwater availability within the Des Moines River alluvial aquifer would be beneficial. Beginning in 2018, DMWW partnered with the U.S. Geological Survey to construct a groundwater-flow model to increase understanding of the hydrologic system in the Des Moines area. The model hydrogeologic framework will be enhanced by using multiple geophysical methods of data collection in the Des Moines River, Beaver Creek, and the Des Moines River alluvial aquifer that could provide a better understanding of the geology in the model area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233006","usgsCitation":"Thomas, J.C., Spring, M.A., Gruhn, L.R., and Bristow, E.L., 2023, Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022: U.S. Geological Survey Fact Sheet 2023–3006, 4 p., https://doi.org/10.3133/fs20233006.","productDescription":"Report: 4 p.; 2 Data Releases; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136349","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":414104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3006"},{"id":414105,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.XML","description":"FS 2023–3006"},{"id":414103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3006/coverthb2.jpg"},{"id":414112,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS dataset","linkHelpText":"—National Hydrography Dataset— USGS National Hydrography Dataset Best Resolution for Hydrologic Unit 4 – 2001"},{"id":414109,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":414110,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499566,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114475.htm","linkFileType":{"id":5,"text":"html"}},{"id":414138,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":414113,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3006/images"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Des Moines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.69895287733283,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.66093681949087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. Finally, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.
Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process-guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.
Livestock production is the most widespread land use globally and occurs across a diverse set of ecosystems. Variability in long-term livestock grazing impacts across ecosystems is poorly characterized, particularly at larger spatial scales, despite strong relationships with various ecosystem services related to soil fertility and stabilization and vegetation productivity. Here we examine the effects of grazing on vegetation and the implications for resistance and resilience to global change.
We use six long-term research stations in the western United States, spanning two ecoregions, multiple ecosystems, and 311 total site-years of research. Across these sites we evaluate convergence and divergence of vegetation response to grazing vs grazing removal, focusing on interactions with drivers of global change.
We found that at long time scales (multiple decades), grazing has numerous convergent and divergent effects across ecoregions and ecosystems. Similarity among precipitation patterns and plant traits linked to grazing and production timing were key elements explaining convergence or divergence in long-term patterns of livestock grazing response. Ecosystem differences across western US rangelands are also associated with variable effects of grazing on resistance and resilience to invasive species and climate change.
These results suggest that unique ecosystem or ecoregion responses to future global change may result from complex interactions between grazing and environmental factors, such as precipitation timing and plant traits. Adapting livestock and grazing management to specific ecosystem vegetation and climate variability is needed to manage for the myriad global changes affecting rangeland production and diversity.
","language":"English","publisher":"Wiley","doi":"10.1111/avsc.12719","usgsCitation":"Copeland, S., Hoover, D.L., Augustine, D.J., Bates, J.D., Boyd, C.S., Davies, K.W., Derner, J., Duniway, M.C., Porensky, L., and Vermeire, L.T., 2023, Variable effects of long-term livestock grazing across the western United States suggest diverse approaches are needed to meet global change challenges: Applied Vegetation Science, v. 26, no. 1, e12719, 16 p., https://doi.org/10.1111/avsc.12719.","productDescription":"e12719, 16 p.","ipdsId":"IP-133319","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498865,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/avsc.12719","text":"Publisher Index Page"},{"id":414881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.46681746772508,\n 47.39667163190734\n ],\n [\n -125.58511650460318,\n 47.39667163190734\n ],\n [\n -125.58511650460318,\n 34.02051503113559\n ],\n [\n -105.46681746772508,\n 34.02051503113559\n ],\n [\n -105.46681746772508,\n 47.39667163190734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":867982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoover, David L. dlhoover@usgs.gov","contributorId":245331,"corporation":false,"usgs":false,"family":"Hoover","given":"David","email":"dlhoover@usgs.gov","middleInitial":"L.","affiliations":[{"id":49151,"text":"USDA-ARS Rangeland Resources Research Unit, Crops Research Laboratory, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":867983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":867984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bates, Jonathan D","contributorId":303747,"corporation":false,"usgs":false,"family":"Bates","given":"Jonathan","email":"","middleInitial":"D","affiliations":[{"id":65895,"text":"USDA-Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR","active":true,"usgs":false}],"preferred":false,"id":867985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":867986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davies, Kirk W.","contributorId":255108,"corporation":false,"usgs":false,"family":"Davies","given":"Kirk","email":"","middleInitial":"W.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":867987,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":270261,"corporation":false,"usgs":false,"family":"Derner","given":"Justin D.","affiliations":[{"id":56124,"text":"USDA, Agricultural Research Service, Rangeland Resources and Systems Research Unit","active":true,"usgs":false}],"preferred":false,"id":867988,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":867989,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":867990,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vermeire, Lance T","contributorId":303748,"corporation":false,"usgs":false,"family":"Vermeire","given":"Lance","email":"","middleInitial":"T","affiliations":[{"id":65897,"text":"USDA-Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT","active":true,"usgs":false}],"preferred":false,"id":867991,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243046,"text":"70243046 - 2023 - Plant water-use strategies predict restoration success across degraded drylands","interactions":[],"lastModifiedDate":"2023-06-09T15:23:00.168208","indexId":"70243046","displayToPublicDate":"2023-03-13T07:17:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Plant water-use strategies predict restoration success across degraded drylands","docAbstract":"Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions.
An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km2).
First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy.
Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover.
We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01621-1","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D., Rongsong, L., Requena-Mullor, J.M., and Caughlin, T., 2023, Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching: Landscape Ecology, v. 38, https://doi.org/10.1007/s10980-023-01621-1.","productDescription":"16 p.","startPage":"1306","ipdsId":"IP-144778","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":414693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.76040258225669,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 42.96440805189778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","edition":"1291","noUsgsAuthors":false,"publicationDate":"2023-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii","contributorId":257073,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":867435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":867436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rongsong, Liu","contributorId":303384,"corporation":false,"usgs":false,"family":"Rongsong","given":"Liu","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":867437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867439,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241163,"text":"70241163 - 2023 - Field assessment of Naled and its primary degradation product (dichlorvos) in aquatic ecosystems following aerial ultra-low volume application for mosquito control","interactions":[],"lastModifiedDate":"2023-06-27T16:43:22.597659","indexId":"70241163","displayToPublicDate":"2023-03-13T07:10:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Field assessment of Naled and its primary degradation product (dichlorvos) in aquatic ecosystems following aerial ultra-low volume application for mosquito control","docAbstract":"Naled, an organophosphate insecticide, is applied aerially at ultra-low volumes over aquatic ecosystems near Sacramento, California, USA, during summer months for mosquito control. Two ecosystem types (rice fields and a flowing canal) were sampled in 2020 and 2021. Naled and its primary degradation product (dichlorvos) were measured in water, biofilm, grazer macroinvertebrates, and omnivore/predator macroinvertebrates (predominantly crayfish). Maximum naled and dichlorvos concentrations detected in water samples one day after naled application were 287.3 and 5647.5 ng/L, respectively, which were above the U.S. Environmental Protection Agency’s aquatic life benchmarks for invertebrates. Neither compound was detected in water more than one day after the application. Dichlorvos, but not naled, was detected in composite crayfish samples up to 10 days after the last aerial application. Detections in water from the canal showed that the compounds were transported downstream of the target application area. Factors such as vector control flight paths, dilution, and transport through air and water likely affected concentrations of naled and dichlorvos in water and organisms from these aquatic ecosystems.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-023-00981-8","usgsCitation":"Smith, C., Hladik, M.L., Kuivila, K., and Waite, I.R., 2023, Field assessment of Naled and its primary degradation product (dichlorvos) in aquatic ecosystems following aerial ultra-low volume application for mosquito control: Archives of Environmental Contamination and Toxicology, v. 84, p. 307-317, https://doi.org/10.1007/s00244-023-00981-8.","productDescription":"11 p.","startPage":"307","endPage":"317","ipdsId":"IP-143358","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":444223,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00244-023-00981-8","text":"Publisher Index Page"},{"id":435413,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3DU0U","text":"USGS data release","linkHelpText":"Naled and dichlorvos in water and aquatic organisms from a canal and rice fields near Sacramento, California"},{"id":414087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.18108336553227,\n 39.24258329396358\n ],\n [\n -122.18108336553227,\n 38.67900906553575\n ],\n [\n -121.19273356695606,\n 38.67900906553575\n ],\n [\n -121.19273356695606,\n 39.24258329396358\n ],\n [\n -122.18108336553227,\n 39.24258329396358\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2023-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Cassandra 0000-0003-1088-1772 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-1088-1772","contributorId":193491,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":203857,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuivila, Kathryn 0000-0001-7940-489X","orcid":"https://orcid.org/0000-0001-7940-489X","contributorId":303031,"corporation":false,"usgs":false,"family":"Kuivila","given":"Kathryn","affiliations":[{"id":65617,"text":"Scientist Emeritus","active":true,"usgs":false}],"preferred":false,"id":866322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866323,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248732,"text":"70248732 - 2023 - Migrating ducks and submersed aquatic vegetation respond positively after invasive common carp (Cyprinus carpio) exclusion from a freshwater coastal marsh","interactions":[],"lastModifiedDate":"2023-09-19T12:03:30.238894","indexId":"70248732","displayToPublicDate":"2023-03-13T07:01:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Migrating ducks and submersed aquatic vegetation respond positively after invasive common carp (Cyprinus carpio) exclusion from a freshwater coastal marsh","docAbstract":"Invasive carp can negatively affect waterbirds through habitat degradation, including removal of submersed aquatic vegetation (SAV). At a freshwater coastal marsh of great ecological and cultural significance, we excluded invasive common carp (Cyprinus carpio) with the goal of restoring the marsh to historical conditions to support fall-migrating waterfowl. We used a multi-pronged approach to assess the response of ducks and SAV to carp exclusion by leveraging historical duck and SAV surveys and collecting new data for six years post-exclusion on density and distribution of ducks within the marsh, SAV response, and refueling performance (as indexed by plasma-lipid metabolites) by two species of diving ducks. We found that fall-migrating duck numbers and total SAV extent rebounded to historical levels (1970s). There was a 339% increase in diving duck density and a nearly 400% increase in dabbling duck density between the pre- (i.e., 2000s) and post-exclusion periods. Diving ducks were more likely to be observed associated with SAV within the marsh, whereas dabbling ducks responded to emergent vegetation extent and water levels. Refueling performance was stable post-exclusion, despite increased numbers of ducks using the marsh, indicating that marsh habitat quality was sufficient. Some aspects of the marsh recovery remain in question, including possible shifts in SAV community composition. Overall, the carp exclusion has successfully improved the quality of habitat for migrating ducks.
Ecosystem connectivity tends to increase the resilience and function of ecosystems responding to stressors. Coastal ecosystems sequester disproportionately large amounts of carbon, but rapid exchange of water, nutrients, and sediment makes them vulnerable to sea level rise and coastal erosion. Individual components of the coastal landscape (i.e., marsh, forest, bay) have contrasting responses to sea level rise, making it difficult to forecast the response of the integrated coastal carbon sink. Here we couple a spatially-explicit geomorphic model with a point-based carbon accumulation model, and show that landscape connectivity, in-situ carbon accumulation rates, and the size of the landscape-scale coastal carbon stock all peak at intermediate sea level rise rates despite divergent responses of individual components. Progressive loss of forest biomass under increasing sea level rise leads to a shift from a system dominated by forest biomass carbon towards one dominated by marsh soil carbon that is maintained by substantial recycling of organic carbon between marshes and bays. These results suggest that climate change strengthens connectivity between adjacent coastal ecosystems, but with tradeoffs that include a shift towards more labile carbon, smaller marsh and forest extents, and the accumulation of carbon in portions of the landscape more vulnerable to sea level rise and erosion.
Wildfires pose a risk to water supplies in the western U.S. and many other parts of the world, due to the potential for degradation of water quality. However, a lack of adequate data hinders prediction and assessment of post-wildfire impacts and recovery. The dearth of such data is related to lack of funding for monitoring extreme events and the challenge of measuring the outsized hydrologic and erosive response after wildfire. Assessment and prediction of post-wildfire surface water quality would be strengthened by the strategic monitoring of key parameters, and the selection of sampling locations based on the following criteria: (1) streamgage with pre-wildfire data; (2) ability to install equipment that can measure water quality at high temporal resolution, with a focus on storm sampling; (3) minimum of 10% drainage area burned at moderate to high severity; (4) lack of major water management; (5) high-frequency precipitation; and (6) availability of pre-wildfire water-quality data and (or) water-quality data from a comparable unburned basin. Water-quality data focused on parameters that are critical to human and (or) ecosystem health, relevant to water-treatment processes and drinking-water quality, and (or) inform the role of precipitation and discharge on flow paths and water quality are most useful. We discuss strategic post-wildfire water-quality monitoring and identify opportunities for advancing assessment and prediction. Improved estimates of the magnitude, timing, and duration of post-wildfire effects on water quality would aid the water resources community prepare for and mitigate against impacts to water supplies.
Arctic marine mammals have had little exposure to vessel traffic and potential associated disturbance, but sea ice loss has increased accessibility of Arctic waters to vessels. Vessel disturbance could influence marine mammal population dynamics by altering behavioral activity budgets that affect energy balance, which in turn can affect birth and death rates. As an initial step in studying these linkages, we conducted the first comprehensive analysis to evaluate the effects of vessel exposure on Pacific walrus (Odobenus rosmarus divergens) behaviors. We obtained >120,000 h of location and behavior (foraging, in-water not foraging, and hauled out) data from 218 satellite-tagged walruses and linked them to vessel locations from the marine automatic identification system (AIS). This yielded 206 vessel-exposed walrus telemetry hours for comparison to unexposed hours, which we used to assess if vessel exposure altered walrus behavior. We developed a filter to account for misclassification of vessel exposure of telemetered walruses. Then we tested for an effect of vessel exposure on walrus behaviors using a combination of exact and propensity score-based matching to account for confounding covariates, and we conducted statistical power analyses. We did not detect an effect of vessel exposure on walrus behaviors even when statistical power was high (i.e., for foraging walruses), which may have been due to the sample size-driven need to define vessel presence within a larger than desired distance (15-km measured radius) around a walrus. Although this study did not determine at what distance vessel exposure affects walrus behaviors, it provided an upper bound on the distance at which the vessels encountered may disturb foraging walruses. When more situation-specific information is lacking, this distance could be used as a conservative buffer to maintain between vessels and areas of high use by foraging walruses. Studies on behavioral consequences of closer proximities between walruses and vessels are needed, and our assessments of misclassification rates and statistical power can be used for future studies. We demonstrated that analytical approaches such as matching, which are rarely used in wildlife studies, are particularly useful for testing hypotheses with observational data.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4433","usgsCitation":"Taylor, R.L., Jay, C.V., Beatty, W., Fischbach, A.S., Quakenbush, L.T., and Crawford, J.A., 2023, Exploring effects of vessels on walrus behaviors using telemetry, automatic identification system data and matching: Ecosphere, v. 14, no. 3, e4433, 16 p., https://doi.org/10.1002/ecs2.4433.","productDescription":"e4433, 16 p.","ipdsId":"IP-122838","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":444233,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4433","text":"Publisher Index Page"},{"id":435414,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IO8AZJ","text":"USGS data release","linkHelpText":"Walrus Haulout and In-water Activity Levels Relative to Vessel Interactions in the Chukchi Sea, 2012-2015"},{"id":413040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -170.32245819085716,\n 66.05513807898694\n ],\n [\n -169.7201609109013,\n 65.81337853498471\n ],\n [\n -163.44646744670234,\n 67.26413622283681\n ],\n [\n -163.48928051808477,\n 67.77761301095038\n ],\n [\n -165.89517336815703,\n 68.50009214376516\n ],\n [\n -165.8497260252095,\n 68.66729130495727\n ],\n [\n -163.80598521715572,\n 68.81337251177467\n ],\n [\n -162.7412289106771,\n 69.22689570748591\n ],\n [\n -162.42602442536432,\n 69.65754636971394\n ],\n [\n -161.76725890958286,\n 69.98451068064082\n ],\n [\n -159.03175166715397,\n 70.56710759229969\n ],\n [\n -156.4162777380868,\n 70.98318711582823\n 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]\n}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":866281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":866282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":288790,"corporation":false,"usgs":false,"family":"Beatty","given":"William S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":866283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":866284,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quakenbush, Lori T.","contributorId":47262,"corporation":false,"usgs":true,"family":"Quakenbush","given":"Lori","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":866285,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Justin A.","contributorId":214225,"corporation":false,"usgs":false,"family":"Crawford","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":866286,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241072,"text":"tm1D11 - 2023 - Field techniques for fluorescence measurements targeting dissolved organic matter, hydrocarbons, and wastewater in environmental waters: Principles and guidelines for instrument selection, operation and maintenance, quality assurance, and data reporting","interactions":[],"lastModifiedDate":"2023-04-03T16:43:31.13652","indexId":"tm1D11","displayToPublicDate":"2023-03-13T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1-D11","displayTitle":"Field Techniques for Fluorescence Measurements Targeting Dissolved Organic Matter, Hydrocarbons, and Wastewater in Environmental Waters: Principles and Guidelines for Instrument Selection, Operation and Maintenance, Quality Assurance, and Data Reporting","title":"Field techniques for fluorescence measurements targeting dissolved organic matter, hydrocarbons, and wastewater in environmental waters: Principles and guidelines for instrument selection, operation and maintenance, quality assurance, and data reporting","docAbstract":"The use of field deployable fluorescence sensors by the U.S. Geological Survey has become increasingly common for a wide variety of surface water and groundwater investigations. This report addresses field deployable fluorometers that measure the fluorescence response of various substances in water exposed to incident light generated by the sensor. An introduction to the basic principles of field measurements of fluorescence is provided, as well as technical background information on sensors that target dissolved organic matter, wastewater, and hydrocarbons, including sensor selection, operating principles, key features, and design elements. General deployment, operation and maintenance protocols, quality-assurance techniques, and suggestions for data reporting are presented to facilitate and standardize the collection and accurate communication of data collected by the U.S. Geological Survey across studies, sites, and sensor types. Sensor performance issues and common interferences are also described. An appendix is included to describe sensor calibration criteria and procedures, reporting units, and specific approaches to correct for interferences for fluorescence of dissolved organic matter sensors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D11","usgsCitation":"Booth, A., Fleck, J., Pellerin, B.A., Hansen, A., Etheridge, A., Foster, G.M., Graham, J.L., Bergamaschi, B.A., Carpenter, K.D., Downing, B.D., Rounds, S.A., and Saraceno, J., 2023, Field techniques for fluorescence measurements targeting dissolved organic matter, hydrocarbons, and wastewater in environmental waters: Principles and guidelines for instrument selection, operation and maintenance, quality assurance, and data reporting: U.S. Geological Survey Techniques and Methods, book 1, chap. D11, 41 p., https://doi.org/10.3133/tm1D11.","productDescription":"Report: vi, 41 p.; Data Release","numberOfPages":"52","onlineOnly":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":414608,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm1D11/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413883,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJUCTP","text":"USGS data release","linkHelpText":"Comparisons from an Aqualog fluorometer standardized to quinine sulfate equivalents (QSE) with excitation (ex) and emissions (em) equivalent to fluorescence of dissolved organic Matter (fDOM) sensors from multiple manufacturers"},{"id":413879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d11/tm1d11.pdf","text":"Report","size":"3.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1–D11"},{"id":413880,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/01/d11/tm1d11.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":413882,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/01/d11/images"},{"id":413877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d11/coverthb2.jpg"}],"contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The magnitude and variability of floods have increased for many nontidal streams on Long Island (LI), NY since the mid-20th century. One of the most densely populated regions of the United States, LI has experienced amplified floods in step with increases in impervious land cover, storm, and sanitary sewers that have accompanied urban development. To better understand the drivers of observed flood trends and effects of urbanization, a nonstationary flood frequency analysis is conducted, using historical annual peak flow records from 17 gaged watersheds on LI using conditional moments based on physical covariates from a two-stage sequential robust linear regression procedure. Regression results indicate that urban development and precipitation are significant co-predictors of peak flows for LI watersheds that have undergone rapid development during the available peak flow record. In watersheds with less intense urbanization or that were fully developed before the peak flow record began, precipitation alone was a significant explanatory variable. Long-term baseflow patterns identified using a nonparametric smoother explained some patterns of decreasing peak flows and heteroskedasticity in the peak flow records. Fitting a log-Pearson III distribution with these conditional moments, floods corresponding to a 20% annual exceedance probability (AEP) are up to 80% higher under a nonstationary framework compared with stationary under current watershed conditions, and differ significantly (95% confidence) from stationary estimates for 6 out of 17 watersheds. Larger floods corresponding to 1% AEPs do not differ significantly between nonstationary and stationary estimates at a 95% confidence level. Nonmonotonic trends observed in two watersheds indicate that recent stormwater management practices, such as rerouting stormwater outfalls away from the channel, substantially reduce flood frequency. Reduced nonstationary flood quantile estimates at these two watersheds are 20 to 40% lower than stationary estimates when accounting for changing watershed conditions over time. Across LI, stormwater management and water-table fluctuations have increased peak flow variability, characteristic of a late phase urban adjustment period on LI. Results of this study demonstrate that a nonstationary framework is a necessary step forward toward a regional flood-frequency analysis for LI. This nonstationary framework will allow flood managers to update flood discharge estimates to current conditions that reflect altered relationships between urban cover and climate for more targeted planning of flood control, transportation infrastructure, and management of floodplain ecosystems.
Northern peatlands store globally-important amounts of carbon in the form of partly decomposed plant detritus. Drying associated with climate and land-use change may lead to increased fire frequency and severity in peatlands and the rapid loss of carbon to the atmosphere. However, our understanding of the patterns and drivers of peatland burning on an appropriate decadal to millennial timescale relies heavily on individual site-based reconstructions. For the first time, we synthesise peatland macrocharcoal records from across North America, Europe, and Patagonia to reveal regional variation in peatland burning during the Holocene. We used an existing database of proximal sedimentary charcoal to represent regional burning trends in the wider landscape for each region. Long-term trends in peatland burning appear to be largely climate driven, with human activities likely having an increasing influence in the late Holocene. Warmer conditions during the Holocene Thermal Maximum (∼9–6 cal. ka BP) were associated with greater peatland burning in North America's Atlantic coast, southern Scandinavia and the Baltics, and Patagonia. Since the Little Ice Age, peatland burning has declined across North America and in some areas of Europe. This decline is mirrored by a decrease in wider landscape burning in some, but not all sub-regions, linked to fire-suppression policies, and landscape fragmentation caused by agricultural expansion. Peatlands demonstrate lower susceptibility to burning than the wider landscape in several instances, probably because of autogenic processes that maintain high levels of near-surface wetness even during drought. Nonetheless, widespread drying and degradation of peatlands, particularly in Europe, has likely increased their vulnerability to burning in recent centuries. Consequently, peatland restoration efforts are important to mitigate the risk of peatland fire under a changing climate. Finally, we make recommendations for future research to improve our understanding of the controls on peatland fires.
For more than 25 years, the U.S. Geological Survey (USGS) has used the remote-sensing capabilities of United States National Imagery Systems (USNIS) to obtain high-resolution electro-optical imagery to monitor Earth’s response to global environmental change. A major focus has been monitoring sea ice behavior in the Arctic Ocean and its marginal seas. In 1997 and 1998, under the direction of the Global Fiducials Program (GFP), USNIS imagery was collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) Project. In 1999, collection of USNIS imagery of six static sea ice sites in the Arctic Ocean and its marginal seas began, and the imagery was archived in the USGS-hosted Global Fiducials Library (GFL). The static sites were imaged through 2014, creating time series of geographically referenced images which scientists have used to study seasonal changes in Arctic ice over the same locations for extended time periods. In early 2009, the Central Intelligence Agency’s MEDEA Program requested that the USGS use USNIS imagery to track movements of sea ice floes during an entire Arctic summer (April through September). The goal was to improve researchers’ understanding of seasonal changes in Arctic sea ice. In order to track and repeatedly capture imagery of the same ice as it drifted across the Arctic Ocean, the USGS developed a methodology and a series of protocols to use data from telemetering drift buoys deployed at locations across the Arctic Ocean by the International Arctic Buoy Programme (IABP) to track the drift of targeted ice masses for periods that exceeded a year. Resulting time series of sea ice imagery, captured while monitoring 38 individual buoys, were archived in the GFL. In 2013 and 2014, in support of the Seasonal Ice Zone Reconnaissance Surveys (SIZRS) Program led by the University of Washington, Seattle, Washington, the USGS requested the collection of USNIS imagery of selected sites in the Beaufort and Chukchi Seas located at every degree of latitude between 70º and 80º N. along a north-south transect. This was done to track and understand the interplay among the ice, atmosphere, and ocean and what it contributes to the rapid decline in summer ice extent that has occurred in recent years. Under the auspices of the GFP, thousands of sea ice images have been collected. Many of those that pass a quality and cloud-cover screening are archived in the GFL. Of these, more than 1,750 sea ice images have been publicly released, following an editing and processing procedure that produces high-resolution degraded images, known as “literal imagery-derived products” or LIDPs. These LIDPs have been approved for free, unrestricted public distribution and scientific analysis. The LIDPs can be downloaded from the USGS Global Fiducials Library Data Access Portal (USGS GFLDAP) at https://www.usgs.gov/global-fiducials-library-data-access-portal. In addition, nonliteral imagery-derived products (nonliteral IDPs), such as metadata, maps, charts, and graphs, have also been released.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231008","usgsCitation":"Molnia, B.F., and Wilson, E.M., 2023, Documenting Arctic sea ice dynamics with Global Fiducials Program imagery: U.S. Geological Survey Open-File Report 2023–1008, 32 p., https://doi.org/10.3133/ofr20231008.","productDescription":"viii, 32 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129958","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":499733,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114471.htm","linkFileType":{"id":5,"text":"html"}},{"id":413704,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1008/images/"},{"id":413703,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1008/ofr20231008.XML"},{"id":413702,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1008"},{"id":413701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1008/ofr20231008.pdf","text":"Report","size":"37.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1008"},{"id":413700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1008/coverthb.jpg"}],"contact":"Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Email: cac@usgs.gov
The purpose of this report is to describe the assembly methods for an external acoustic transmitter attachment device that can be used during fish telemetry studies. External attachment is a simple procedure that can limit handling and reduce recovery times on fish. This report provides step-by-step directions to assemble devices; this assembly method can be used for telemetry studies where external transmitter attachment is needed. General guidelines to attach transmitters to fish also are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm8D1","usgsCitation":"Smerud, J.R., Fredricks, K.T., Gaikowski, M.P., and Cupp, A.R., 2023, Assembly methods for an external acoustic transmitter attachment device for fish telemetry studies: U.S. Geological Survey Techniques and Methods, book 8, chap. D1, 7 p., https://doi.org/10.3133/tm8D1.","productDescription":"vi, 7 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-143564","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":413956,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm8D1/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/08/d01/coverthb.jpg"},{"id":413948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/08/d01/tm8d1.pdf","text":"Report","size":"1.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 8–D1"},{"id":413950,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/08/d01/tm8d1.XML","text":"Report"},{"id":413951,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/08/d01/images"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
The utility of a functional immune assay for smallmouth bass (Micropterus dolomieu) lymphocyte mitogenesis was evaluated. Wild populations in the Potomac River have faced disease and mortality with immunosuppression from exposure to chemical contaminants a suspected component. However, a validated set of immune parameters to screen for immunosuppression in wild fish populations is not available. Prior to use in ecotoxicology studies, ancillary factors influencing the mitogenic response need to be understood. The assay was field-tested with fish collected from three sites in West Virginia as part of health assessments occurring in spring (pre-spawn; April–May) and fall (recrudescence; October–November). Anterior kidney leukocytes were exposed to lipopolysaccharide (LPS) from E.coli O111:B4 or mitogen-free media and proliferation was measured using imaging flow cytometry with advanced machine learning to distinguish lymphocytes. An anti-smallmouth bass IgM monoclonal antibody was used to identify IgM+ lymphocytes. Lymphocyte mitogenesis, or proliferative responses, varied by site and season and positively and negatively correlated with factors such as sex, age, tissue parasites, and macrophage aggregates. Background proliferation of IgM− lymphocytes was negatively correlated to LPS-induced proliferation in both seasons at all sites, but only in spring for IgM+ lymphocytes. The results demonstrate that many factors, in addition to chemical contaminants, may influence lymphocyte proliferation.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8030159","usgsCitation":"Smith, C.R., Ottinger, C., Walsh, H.L., Mazik, P.M., and Blazer, V., 2023, Application of a lipopolysaccharide (LPS)-stimulated mitogenesis assay in smallmouth bass (Micropterus dolomieu) to augment wild fish health studies: Fishes, v. 8, no. 3, 159, 18 p., https://doi.org/10.3390/fishes8030159.","productDescription":"159, 18 p.","ipdsId":"IP-138884","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":444243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8030159","text":"Publisher Index Page"},{"id":435416,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FTUPPX","text":"USGS data release","linkHelpText":"Immune Function of Wild Smallmouth Bass Collected from Sites within the Chesapeake Bay Watershed, 2016-2021"},{"id":414615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","otherGeospatial":"Cheat River, South Branch Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.18698999878868,\n 39.52897283606595\n ],\n [\n -80.18698999878868,\n 38.97427210045825\n ],\n [\n -78.74995409613122,\n 38.97427210045825\n ],\n [\n -78.74995409613122,\n 39.52897283606595\n ],\n [\n -80.18698999878868,\n 39.52897283606595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":867150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ottinger, Christopher 0000-0003-2551-1985","orcid":"https://orcid.org/0000-0003-2551-1985","contributorId":205874,"corporation":false,"usgs":true,"family":"Ottinger","given":"Christopher","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":867151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":867152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":867153,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":867154,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241862,"text":"70241862 - 2023 - Algal amendment enhances biogenic methane production from coals of different thermal maturity","interactions":[],"lastModifiedDate":"2023-03-29T12:25:10.724795","indexId":"70241862","displayToPublicDate":"2023-03-10T07:23:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Algal amendment enhances biogenic methane production from coals of different thermal maturity","docAbstract":"The addition of small amounts of algal biomass to stimulate methane production in coal seams is a promising low carbon renewable coalbed methane enhancement technique. However, little is known about how the addition of algal biomass amendment affects methane production from coals of different thermal maturity. Here, we show that biogenic methane can be produced from five coals ranging in rank from lignite to low-volatile bituminous using a coal-derived microbial consortium in batch microcosms with and without algal amendment. The addition of 0.1 g/l algal biomass resulted in maximum methane production rates up to 37 days earlier and decreased the time required to reach maximum methane production by 17–19 days when compared to unamended, analogous microcosms. Cumulative methane production and methane production rate were generally highest in low rank, subbituminous coals, but no clear association between increasing vitrinite reflectance and decreasing methane production could be determined. Microbial community analysis revealed that archaeal populations were correlated with methane production rate (p = 0.01), vitrinite reflectance (p = 0.03), percent volatile matter (p = 0.03), and fixed carbon (p = 0.02), all of which are related to coal rank and composition. Sequences indicative of the acetoclastic methanogenic genus Methanosaeta dominated low rank coal microcosms. Amended treatments that had increased methane production relative to unamended analogs had high relative abundances of the hydrogenotrophic methanogenic genus Methanobacterium and the bacterial family Pseudomonadaceae. These results suggest that algal amendment may shift coal-derived microbial communities towards coal-degrading bacteria and CO2-reducing methanogens. These results have broad implications for understanding subsurface carbon cycling in coal beds and the adoption of low carbon renewable microbially enhanced coalbed methane techniques across a diverse range of coal geology.
The reproductive ecology of geese that breed in the Arctic and subarctic is likely susceptible to the effects of climate change, which is projected to alter the environmental conditions of northern latitudes. Nest survival is an important component of productivity in geese; however, the effects of regional environmental conditions on nest survival are not well understood for some species, including the Emperor Goose (Anser canagicus), a species of conservation concern that is endemic to the Bering Sea region. We estimated nest survival and examined how indices of regional environmental conditions, nest traits (nest age, initiation date, and maximum number of eggs in the nest), and researcher disturbance influenced daily survival probability (DSP) of Emperor Goose nests using hierarchical models and 24 years of nest monitoring data (1994–2017) from the Yukon–Kuskokwim Delta (Y–K Delta) in western Alaska. Our results indicate that overall nest survival was generally high (µ = 0.766, 95% CRI: 0.655–0.849) and ranged from 0.327 (95% CRI: 0.176–0.482) in 2013 to 0.905 (95% CRI: 0.839–0.953) in 1995. We found that DSPs of nests were influenced by nest traits, negatively influenced by major tidal flooding events and by researcher disturbance, but were not influenced by regional indices of spring timing, temperature and precipitation during nesting, or fox and vole abundance on the Y–K Delta. However, the number of nests found each year was negatively related to our index of fox abundance, suggesting nests that failed as a result of fox predation may have never been discovered due to our limited nest-searching efforts during egg laying. Our results suggest that regional environmental variation had minimal influence on the nest survival of Emperor Geese, although major flooding events were important. Nevertheless, we suspect that within-year variation in local weather conditions and local abundance of predators and alternative prey may be important and should be considered in future studies.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duad008","usgsCitation":"Thompson, J.M., Uher-Koch, B.D., Daniels, B.L., Schmutz, J.A., and Sedinger, B.S., 2023, Nest traits and major flooding events influence nest survival of Emperor Geese while regional environmental variation linked to climate does not: Ornithological Applications, v. 125, no. 2, duad008, 14 p., https://doi.org/10.1093/ornithapp/duad008.","productDescription":"duad008, 14 p.","ipdsId":"IP-144477","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":444247,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1093/ornithapp/duad008","text":"Publisher Index Page"},{"id":435417,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9891BFB","text":"USGS data release","linkHelpText":"Emperor Goose (Anser canagicus) Nest Survival Encounter History from the Yukon-Kuskokwim Delta, Alaska, 1994-2017"},{"id":416853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.0853296316241,\n 61.527680543295105\n ],\n [\n -166.0853296316241,\n 60.419454074425744\n ],\n [\n -163.581422052621,\n 60.419454074425744\n ],\n [\n -163.581422052621,\n 61.527680543295105\n ],\n [\n -166.0853296316241,\n 61.527680543295105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Jordan M.","contributorId":303133,"corporation":false,"usgs":false,"family":"Thompson","given":"Jordan","email":"","middleInitial":"M.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":872081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":872082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, Bryan L.","contributorId":304964,"corporation":false,"usgs":false,"family":"Daniels","given":"Bryan","email":"","middleInitial":"L.","affiliations":[{"id":66195,"text":"Yukon Delta National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":872083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmutz, Joel A.","contributorId":304965,"corporation":false,"usgs":false,"family":"Schmutz","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":66196,"text":"Alaska Science Center WTEB (retired)","active":true,"usgs":false}],"preferred":false,"id":872084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sedinger, Benjamin S.","contributorId":304966,"corporation":false,"usgs":false,"family":"Sedinger","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":872085,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241155,"text":"70241155 - 2023 - Wastewater reuse and predicted ecological risk posed by contaminant mixtures in Potomac River watershed streams","interactions":[],"lastModifiedDate":"2023-08-07T17:00:33.651676","indexId":"70241155","displayToPublicDate":"2023-03-10T06:58:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Wastewater reuse and predicted ecological risk posed by contaminant mixtures in Potomac River watershed streams","docAbstract":"A wastewater model was applied to the Potomac River watershed to provide (i) a means to identify streams with a high likelihood of carrying elevated effluent-derived contaminants and (ii) risk assessments to aquatic life and drinking water. The model linked effluent discharges along stream networks, accumulated wastewater, and predicted contaminant loads of municipal wastewater constituents while accounting for instream dilution and attenuation. Simulations using 2016 data suggested that nearly 30% (8281 km) of streams were wastewater impacted. Low- to medium-order streams had the largest range of accumulated wastewater (ACCWW%) values. ACCWW% exceeded a 1% threshold at >39% of drinking-water intakes (varied by temporal condition). Risk assessments of municipal wastewater-contaminant mixtures indicated that 22% (1479 km) of streams impacted by municipal wastewater (5.5% of all reaches modeled) may pose high risk to aquatic organisms under mean-annual conditions, with fish more susceptible to chronic-exposure effects relative to other taxa. Risk varied temporally and by stream order, with the greatest risk occurring in the summer in small streams. These findings suggest that wastewater may be an important factor contributing to environmental degradation in the Potomac River watershed.
Anthropogenically degraded benthic-macroinvertebrate communities (benthos) are one of seven beneficial use impairments (BUIs) in the Niagara River Area of Concern (AOC). Over the last 50 years, upgrades to waste-water treatment, industry closures, and sediment remediations reduced contaminant levels throughout the system. Improvements in benthic communities and sediment toxicity, however, were difficult to assess because there are no comparable reference reaches outside the AOC. A multi-phase study was initiated in 2015 to determine if data from inside the AOC could identify reference conditions, if toxicity and benthic-community data from these sites differed from other AOC sites, and if further remediation efforts were warranted in parts of the AOC. Concentrations or quotients of PAHs, PCBs, dioxins and furans, pesticides, and most metals were below their New York Sediment Class A Guidance Values at 10 sites that were subsequently designated as reference sites. Survival and growth data from Chironomus dilutus and Hyalella azteca bioassays indicated that sediments from only a few individual AOC-impact sites were toxic or significantly different from reference sites, and that mean toxicity at pooled AOC-impact and reference sites did not differ significantly. Similarly, New York Biological Assessment Profile scores and chironomid mentum deformity scores at only a few individual sites differed significantly from corresponding indices at reference sites, but neither metric differed significantly in comparisons between pooled AOC-impact and reference sites. Most analyses indicated that benthic communities were unimpaired and that removal criteria for the benthos BUI were largely met in much of the upper Niagara River AOC.
Brook trout (Salvelinus fontinalis) have been extirpated from many karst-geology streams in West Virginia; however, the causes are not fully understood. Specifically, the impact of calcareous precipitate (marl), which is common in hard-water environments, has not been evaluated as an im- pediment to juvenile survival. Accordingly, two lab-based studies were conducted to determine if brook trout egg and alevin survival is inhibited by marl. In the first study, three aeration treatments were applied to water from a limestone spring source (13–14 C; ~300 mg L–1 hardness), resulting in different pH levels and an increasing degree of marl precipitate. Treatments included raw/untreated (RU; no marl), once-aerated (OA; limited marl), and continuously aerated (CA; significant marl) water. Brook trout eggs obtained from a local hatchery were fertilized and stocked among gravel-filled trays receiving each water type. Mortality occurred faster in CA water where marl coated egg surfaces, but cumulative survival was negligible for all water types. After 53 days, no surviving alevins remained in RU or CA, and 1% survival was observed in OA water. However, extra eggs maintained in a marl-producing system at 8 C without gravel demonstrated >50% survival. A second study was carried out to investigate this discrepancy. Survival was evaluated at three temperatures with and without gravel while producing a thin coating of marl. Increased prevalence of alevin deformities and significantly lower survival were observed at 13.7 C versus 8.1 and 11.2 C, but gravel inclusion did not affect these variables. Potentially harmful effects of marl were observed; however, juvenile brook trout survival was higher during Study 2. This research suggests that brook trout reintroduction efforts in karst-geology streams should be focused on microhabitats with limited marl production and adequate water temperatures for juvenile survival.