The details and mechanisms for Neogene river reorganization in the U.S. Pacific Northwest and northern Rocky Mountains have been debated for over a century with key implications for how tectonic and volcanic systems modulate topographic development. To evaluate paleo-drainage networks, we produced an expansive data set and provenance analysis of detrital zircon U-Pb ages from Miocene to Pleistocene fluvial strata along proposed proto-Snake and Columbia River pathways. Statistical comparisons of Miocene-Pliocene detrital zircon spectra do not support previously hypothesized drainage routes of the Snake River. We use detrital zircon unmixing models to test prior Snake River routes against a newly hypothesized route, in which the Snake River circumnavigated the northern Rocky Mountains and entered the Columbia Basin from the northeast prior to incision of Hells Canyon. Our proposed ancestral Snake River route best matches detrital zircon age spectra throughout the region. Furthermore, this northerly Snake River route satisfies and provides context for shifts in the sedimentology and fish faunal assemblages of the western Snake River Plain and Columbia Basin through Miocene−Pliocene time. We posit that eastward migration of the Yellowstone Hotspot and its effect on thermally induced buoyancy and topographic uplift, coupled with volcanic densification of the eastern Snake River Plain lithosphere, are the primary mechanisms for drainage reorganization and that the eastern and western Snake River Plain were isolated from one another until the early Pliocene. Following this basin integration, the substantial increase in drainage area to the western Snake River Plain likely overtopped a bedrock threshold that previously contained Lake Idaho, which led to incision of Hells Canyon and establishment of the modern Snake and Columbia River drainage network.
Native forests on tropical islands have been displaced by non-native species, leading to calls for their transformation. Simultaneously, there is increasing recognition that tropical forests can help sequester carbon that would otherwise enter the atmosphere. However, it is unclear if native forests sequester more or less carbon than human-altered landscapes. At Palmyra Atoll, efforts are underway to transform the rainforest composition from coconut palm (Cocos nucifera) dominated to native mixed-species. To better understand how this landscape-level change will alter the atoll’s carbon dynamics, we used field sampling, remote sensing, and parameter estimates from the literature to model the total carbon accumulation potential of Palmyra’s forest before and after transformation. The model predicted that replacing the C. nucifera plantation with native species would reduce aboveground biomass from 692.6 to 433.3 Mg C. However, expansion of the native Pisonia grandis and Heliotropium foertherianum forest community projected an increase in soil carbon to at least 13,590.8 Mg C, thereby increasing the atoll’s overall terrestrial carbon storage potential by 11.6%. Nearshore sites adjacent to C. nucifera canopy had a higher dissolved organic carbon (DOC) concentration (110.0 μMC) than sites adjacent to native forest (81.5 μMC), suggesting that, in conjunction with an increase in terrestrial carbon storage, replacing C. nucifera with native forest will reduce the DOC exported from the forest into in nearshore marine habitats. Lower DOC levels have potential benefits for corals and coral dependent communities. For tropical islands like Palmyra, reverting from C. nucifera dominance to native tree dominance could buffer projected climate change impacts by increasing carbon storage and reducing coral disease.
Shark dermal scale (denticle) accumulation in the fossil record can provide information about the abundance and composition of past shark communities. Denticles are shed continuously, such that a single shark leaves a scattered composite of many isolated denticles in sediments. However, the rate of denticle shedding as well as how these rates vary among shark species with different life modes and their consistency over time are unknown, limiting the interpretation of denticle assemblages. To better understand the process of denticle shedding and calibrate the relationship between absolute shark abundance in the environment and denticle deposition in sediments, we captured denticles shed by 2 shark species in a large aquarium over 9 mo. We then simulated how these aquarium-derived shedding rates shape the relationship between shark abundance and denticle accumulation. Bonnethead sharks Sphyrna tiburo, a more active, benthopelagic species with small, thin denticles, shed 3.6 times faster on average than zebra sharks Stegostoma fasciatum, a more sedentary, demersal species with large, robust denticles. This pattern persisted when shedding rates were corrected by estimated denticle quantities, shark space use, and methodological factors (2.2- to 3.8-fold difference). Over the study, bonnethead shark shedding rates declined while zebra shark shedding rates increased slightly. Finally, denticle assemblage composition corresponded with the relative abundance of denticles on the body of each species, consistent with natural shedding rather than selective loss. Overall, we show that shark taxa contribute unevenly to the denticle record, indicating that shedding rate measurements can help inform and constrain ecological interpretations of denticle assemblages.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13936","usgsCitation":"Dillon, E.M., Bagla, A., Plioplys, K.D., McCauley, D., Lafferty, K.D., and O’Dea, A., 2022, Dermal denticle shedding rates vary between two captive shark species: Marine Ecology Progress Series, v. 682, p. 153-167, https://doi.org/10.3354/meps13936.","productDescription":"15 p.","startPage":"153","endPage":"167","ipdsId":"IP-129960","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":413531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"682","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, Erin M.","contributorId":221878,"corporation":false,"usgs":false,"family":"Dillon","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":34029,"text":"U.C. Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagla, Anshika","contributorId":302734,"corporation":false,"usgs":false,"family":"Bagla","given":"Anshika","email":"","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plioplys, Kiera D.","contributorId":302735,"corporation":false,"usgs":false,"family":"Plioplys","given":"Kiera","email":"","middleInitial":"D.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Dea, Aaron","contributorId":302736,"corporation":false,"usgs":false,"family":"O’Dea","given":"Aaron","affiliations":[{"id":65541,"text":"Smithsonian Tropical Research Institute, University of Bologna","active":true,"usgs":false}],"preferred":false,"id":865282,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227645,"text":"70227645 - 2022 - A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","interactions":[],"lastModifiedDate":"2022-01-24T13:07:15.729427","indexId":"70227645","displayToPublicDate":"2022-01-20T07:02:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","docAbstract":"The Biological Condition Gradient (BCG) is a conceptual model used to describe incremental changes in biological condition along a gradient of increasing anthropogenic stress. As coral reefs collapse globally, scientists and managers are focused on how to sustain the crucial structure and functions, and the benefits that healthy coral reef ecosystems provide for many economies and societies. We developed a numeric (quantitative) BGC model for the coral reefs of Puerto Rico and the US Virgin Islands to transparently facilitate ecologically meaningful management decisions regarding these fragile resources. Here, reef conditions range from natural, undisturbed conditions to severely altered or degraded conditions. Numeric decision rules were developed by an expert panel for scleractinian corals and other benthic assemblages using multiple attributes to apply in shallow-water tropical fore reefs with depths <30 m. The numeric model employed decision rules based on metrics (e.g., % live coral cover, coral species richness, pollution-sensitive coral species, unproductive and sediment substrates, % cover by Orbicella spp.) used to assess coral reef condition. Model confirmation showed the numeric BCG model predicted the panel’s median site ratings for 84% of the sites used to calibrate the model and 89% of independent validation sites. The numeric BCG model is suitable for adaptive management applications and supports bioassessment and criteria development. It is a robust assessment tool that could be used to establish ecosystem condition that would aid resource managers in evaluating and communicating current or changing conditions, protect water and habitat quality in areas of high biological integrity, or develop restoration goals with stakeholders and other public beneficiaries.
The Yucaipa groundwater subbasin (referred to in this report as the Yucaipa subbasin) is located about 75 miles (mi) east of of Los Angeles and about 12 mi southeast of the City of San Bernardino. In the Yucaipa subbasin, as in much of southern California, limited annual rainfall and large water demands can strain existing water supplies; therefore, understanding local surface water and groundwater conditions is essential for managing these resources. To better understand the hydrogeology and water resources in the Yucaipa subbasin, especially groundwater, the San Bernardino Valley Municipal Water District and the U.S. Geological Survey initiated a cooperative study to evaluate the hydrogeologic system of the Yucaipa subbasin and the encompassing Yucaipa Valley watershed. Previous studies of the area provided information on general geologic and hydrologic conditions, but this study provides the first comprehensive definition of the hydrogeology of the subsurface throughout the entire subbasin.
The Yucaipa subbasin is located between the northwest trending San Andreas fault zone and San Jacinto fault. Several northeast-trending dip-slip faults dissect the Yucaipa subbasin, providing the mechanism for structural relief within the sediment-filled subbasin and between the subbasin and surrounding mountains and highlands. Several of these dip-slip faults have been previously identified as potential barriers to groundwater flow. This report provides a synthesis of previous studies and a discussion of the geologic interpretations that were used as the foundation for hydrogeologic classification of the Yucaipa subbasin. Notably, this report (1) adopts the recently named and classified sedimentary deposits of Live Oak Canyon geologic formation and extends the mapped distribution of the formation into the Yucaipa subbasin, and (2) adopts the interpretation that activity along the Banning fault predates the deposition of most basin-fill sedimentary materials in the Yucaipa subbasin.
Four hydrogeologic units were classified in the Yucaipa subbasin: (1) crystalline basement, (2) consolidated sedimentary materials, (3) unconsolidated sediment, and (4) surficial materials. The crystalline basement unit forms the bottom boundary of the aquifer system, and the three other units comprise the basin-fill aquifer system. The four hydrogeologic units vary in extent, thickness, and structural relief across the subbasin, with the unconsolidated sediment unit serving as the primary aquifer unit. A three-dimensional hydrogeologic framework model was developed for the Yucaipa subbasin and surrounding area to characterize the thickness, extent, and hydrogeologic variability of the aquifer system. Geologic maps, borehole geophysical logs, drillers’ lithology logs, and depth-to-basement gravity data were used to map and interpolate the subsurface extent and structure of the hydrogeologic units within the subbasin. Faults and structures of geologic and (or) hydrogeologic importance were included in the model for future evaluation of their potential effects on groundwater flow. The resulting hydrogeologic framework is consistent with existing geologic concepts and the tectonic and structural history of the Yucaipa subbasin and surrounding area. The framework is also suitable for use in basin-scale hydrogeologic investigations.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nrl.northumbria.ac.uk/id/eprint/48317/1/STOTEN_Epele_Manuscript.pdf","text":"External Repository"},{"id":396114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"820","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Epele, Luis B.","contributorId":279551,"corporation":false,"usgs":false,"family":"Epele","given":"Luis B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grech, Marta G.","contributorId":279552,"corporation":false,"usgs":false,"family":"Grech","given":"Marta","email":"","middleInitial":"G.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams-Subiza, Emilio A. 0000-0001-9480-527X","orcid":"https://orcid.org/0000-0001-9480-527X","contributorId":279553,"corporation":false,"usgs":false,"family":"Williams-Subiza","given":"Emilio","email":"","middleInitial":"A.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stenert, Cristina","contributorId":279554,"corporation":false,"usgs":false,"family":"Stenert","given":"Cristina","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greig, Hamish S.","contributorId":279555,"corporation":false,"usgs":false,"family":"Greig","given":"Hamish S.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maltchik, Leonardo 0000-0002-5321-7524","orcid":"https://orcid.org/0000-0002-5321-7524","contributorId":279556,"corporation":false,"usgs":false,"family":"Maltchik","given":"Leonardo","email":"","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835125,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pires, Mateus M. 0000-0002-5728-8733","orcid":"https://orcid.org/0000-0002-5728-8733","contributorId":279557,"corporation":false,"usgs":false,"family":"Pires","given":"Mateus","email":"","middleInitial":"M.","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835126,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bird, Matthew S.","contributorId":279558,"corporation":false,"usgs":false,"family":"Bird","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":57281,"text":"Department of Zoology, University of Johannesburg, Auckland Park 2006, South Africa","active":true,"usgs":false}],"preferred":false,"id":835127,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Boissezon, Aurelie","contributorId":279559,"corporation":false,"usgs":false,"family":"Boissezon","given":"Aurelie","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835128,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Boix, Dani 0000-0001-5468-2236","orcid":"https://orcid.org/0000-0001-5468-2236","contributorId":279560,"corporation":false,"usgs":false,"family":"Boix","given":"Dani","email":"","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835129,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Demierre, Eliane","contributorId":279561,"corporation":false,"usgs":false,"family":"Demierre","given":"Eliane","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835130,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"García, Patricia E.","contributorId":279562,"corporation":false,"usgs":false,"family":"García","given":"Patricia E.","affiliations":[{"id":57284,"text":"Grupo de Ecología de Sistemas Acuáticos a escala de Paisaje (GESAP) INIBIOMA, Universidad Nacional del Comahue, CONICET, Quintral 1250, San Carlos de Bariloche (8400), Argentina","active":true,"usgs":false}],"preferred":false,"id":835131,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gascón, Stephanie","contributorId":279563,"corporation":false,"usgs":false,"family":"Gascón","given":"Stephanie","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835132,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jeffries, Michael","contributorId":279564,"corporation":false,"usgs":false,"family":"Jeffries","given":"Michael","email":"","affiliations":[{"id":57285,"text":"Department of Geography & Environmental Sciences, Northumbria University, Newcastle upon Tune, NE1 8ST, UK","active":true,"usgs":false}],"preferred":false,"id":835133,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kneitel, Jamie M. 0000-0002-7841-1198","orcid":"https://orcid.org/0000-0002-7841-1198","contributorId":279565,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie","email":"","middleInitial":"M.","affiliations":[{"id":57286,"text":"Department of Biological Sciences, California State University-Sacramento, Sacramento, CA 95819-6077, USA","active":true,"usgs":false}],"preferred":false,"id":835134,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Loskutov, Olga","contributorId":279566,"corporation":false,"usgs":false,"family":"Loskutov","given":"Olga","email":"","affiliations":[{"id":57287,"text":"Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences, 28 Kommunisticheskaya Street, 167982 Syktyvkar, Russia","active":true,"usgs":false}],"preferred":false,"id":835135,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Manzo, Luz M.","contributorId":279567,"corporation":false,"usgs":false,"family":"Manzo","given":"Luz","email":"","middleInitial":"M.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835136,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mataloni, Gabriela 0000-0002-6852-6143","orcid":"https://orcid.org/0000-0002-6852-6143","contributorId":279568,"corporation":false,"usgs":false,"family":"Mataloni","given":"Gabriela","email":"","affiliations":[{"id":57288,"text":"Instituto de Investigación e Ingeniería Ambiental -IIIA, UNSAM, CONICET, Campus Miguelete, 1650-San Martín, Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":835137,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Mlambo, Musa C.","contributorId":279569,"corporation":false,"usgs":false,"family":"Mlambo","given":"Musa","email":"","middleInitial":"C.","affiliations":[{"id":57289,"text":"Department of Freshwater Invertebrates, Albany Museum, and Department of Zoology and Entomology, Rhodes University, Makhanda (Grahamstown) 6139, South Africa.","active":true,"usgs":false}],"preferred":false,"id":835138,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Oertli, Beat 0000-0002-8372-9045","orcid":"https://orcid.org/0000-0002-8372-9045","contributorId":279570,"corporation":false,"usgs":false,"family":"Oertli","given":"Beat","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835139,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Sala, Jordi","contributorId":279571,"corporation":false,"usgs":false,"family":"Sala","given":"Jordi","email":"","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835140,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Scheibler, Erica E. 0000-0001-6802-8702","orcid":"https://orcid.org/0000-0001-6802-8702","contributorId":279572,"corporation":false,"usgs":false,"family":"Scheibler","given":"Erica","email":"","middleInitial":"E.","affiliations":[{"id":57290,"text":"Entomology Laboratory, IADIZA CCT Mendoza CONICET, Av. Adrián Ruiz Leal s/n, Parque General San Martín, 5500, Mendoza, Argentina","active":true,"usgs":false}],"preferred":false,"id":835141,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wu, Haitao","contributorId":279573,"corporation":false,"usgs":false,"family":"Wu","given":"Haitao","email":"","affiliations":[{"id":57291,"text":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, 130012, China","active":true,"usgs":false}],"preferred":false,"id":835142,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Wissinger, Scott A","contributorId":279574,"corporation":false,"usgs":false,"family":"Wissinger","given":"Scott A","affiliations":[{"id":57292,"text":"Biology and Environmental Science Departments, Allegheny College, Meadville, PA 16335, USA","active":true,"usgs":false}],"preferred":false,"id":835143,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Batzer, Darold P.","contributorId":279575,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":57293,"text":"Department of Entomology, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":835144,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70236588,"text":"70236588 - 2022 - Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","interactions":[],"lastModifiedDate":"2022-09-12T14:35:34.563214","indexId":"70236588","displayToPublicDate":"2022-01-19T09:29:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","docAbstract":"Predicting how species respond to changes in climate is critical to conserving biodiversity. Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. We identify both biological uncertainty (unknown diapause survival rate) and climate uncertainty (ambiguity in the sign of precipitation change across climate models), and their interaction as key factors that determine persistence vs. extinction. Despite uncertainty, the most optimistic iteration of the model predicts that Miami blue butterfly populations will decline under the higher emissions scenario (RCP 8.5). The lack of climate model agreement across the projection ensemble suggests that investigations into the effect of climate change on precipitation-driven phenology require a higher level of rigor in the uncertainty analysis compared to analogous studies of temperature. For tropical species, a mechanistic approach that incorporates both biological and climate uncertainty is the best path forward to understand the effect shifting precipitation regimes have on phenology and population dynamics.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2022.100051","usgsCitation":"Henry, E.H., Terando, A., Morris, W., Daniels, J.C., and Haddad, N.M., 2022, Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms: Climate Change Ecology, v. 3, 100051, 10 p., https://doi.org/10.1016/j.ecochg.2022.100051.","productDescription":"100051, 10 p.","ipdsId":"IP-115502","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":449111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2022.100051","text":"Publisher Index Page"},{"id":406534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.18597412109375,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.505893706264033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henry, Erica H","contributorId":296418,"corporation":false,"usgs":false,"family":"Henry","given":"Erica","email":"","middleInitial":"H","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morris, William F.","contributorId":296419,"corporation":false,"usgs":false,"family":"Morris","given":"William F.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Jaret C.","contributorId":223585,"corporation":false,"usgs":false,"family":"Daniels","given":"Jaret","email":"","middleInitial":"C.","affiliations":[{"id":40743,"text":"Florida Museum of Natural History and University of Florida","active":true,"usgs":false}],"preferred":false,"id":851459,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haddad, Nick M.","contributorId":229345,"corporation":false,"usgs":false,"family":"Haddad","given":"Nick","email":"","middleInitial":"M.","affiliations":[{"id":41625,"text":"Kellogg Biological Station and Department of Integrative Biology, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":851460,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237287,"text":"70237287 - 2022 - Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams","interactions":[],"lastModifiedDate":"2022-10-06T13:37:24.906327","indexId":"70237287","displayToPublicDate":"2022-01-19T08:26:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams","docAbstract":"Metals and polycyclic aromatic hydrocarbons (PAHs) are common pollutants in urban streambed sediment, yet their occurrence is highly variable and difficult to predict. To investigate sources of PAHs and metals to streambed sediment, we sampled pavement dust, soil, and streambed sediment in 10 urban watersheds in three regions of the United States and applied a fallout-radionuclide-based sediment-source analysis to quantify the pavement dust contribution to stream sediment (%dust). We also mapped the area of sealcoated pavement in each watershed (%sealed) to investigate the role of coal-tar pavement sealant (CTS) as a PAH source. Median total and carbon-normalized total PAH concentrations were significantly higher in streambed sediment in the Northeast (54.3 mg/kg and 2.71 mg/gOC) and Southeast (5.37 mg/kg and 1.36 mg/gOC), where CTS is commonly used, than in the Northwest (2.11 mg/kg and 0.071 mg/gOC), where CTS is rarely used. Generalized additive models indicated that %sealed and in some cases %dust significantly affected total PAH concentrations in streambed sediments. The %dust was a significant variable for common urban metals: Cu, Pb, and Zn. These findings advance our quantitative understanding of the role of pavement dust as a source and a vector of contaminants to urban streams.
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sealed and unsealed pavement and concentrations of metals, polycyclic aromatic hydrocarbons, and radionuclides for soils, pavement dust, and stream sediment for 10 urban watersheds"},{"id":408026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, New York, North Carolina, Oregon, Virginia, Washington","city":"Albany, Atlanta, Charlotte, Greensboro, Portland, Rochester, Salem, Seattle, Syracuse","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44262695312501,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.27922900257082\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n 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dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines","interactions":[],"lastModifiedDate":"2025-01-16T17:46:45.52861","indexId":"70262394","displayToPublicDate":"2022-01-18T11:43:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines","docAbstract":"Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min–1. Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49490.1","usgsCitation":"Van Eaton, A.R., Smith, C.M., Pavolonis, M.J., and Said, R., 2022, Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines: Geology, v. 50, no. 4, p. 491-495, https://doi.org/10.1130/G49490.1.","productDescription":"5 p.","startPage":"491","endPage":"495","ipdsId":"IP-128543","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/geol.s.17265287","text":"External Repository"},{"id":466649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Taal volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 120.90268362290232,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 14.102133334852596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra M","contributorId":257012,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra","email":"","middleInitial":"M","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":924037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":924038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Said, Ryan 0000-0002-8095-4204","orcid":"https://orcid.org/0000-0002-8095-4204","contributorId":257003,"corporation":false,"usgs":false,"family":"Said","given":"Ryan","email":"","affiliations":[{"id":51953,"text":"Vaisala, Inc.","active":true,"usgs":false}],"preferred":false,"id":924039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229710,"text":"70229710 - 2022 - No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","interactions":[],"lastModifiedDate":"2022-03-16T14:50:49.888833","indexId":"70229710","displayToPublicDate":"2022-01-18T09:44:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","docAbstract":"Volcanic fallout in polar ice sheets provides important opportunities to date and correlate ice-core records as well as to investigate the environmental impacts of eruptions. Only the geochemical characterization of volcanic ash (tephra) embedded in the ice strata can confirm the source of the eruption, however, and is a requisite if historical eruption ages are to be used as valid chronological checks on annual ice layer counting. Here we report the investigation of ash particles in a Greenland ice core that are associated with a volcanic sulfuric acid layer previously attributed to the 79 CE eruption of Vesuvius. Major and trace element composition of the particles indicates that the tephra does not derive from Vesuvius but most likely originates from an unidentified eruption in the Aleutian arc. Using ash dispersal modeling, we find that only an eruption large enough to include stratospheric injection is likely to account for the sizable (24–85 µm) ash particles observed in the Greenland ice at this time. Despite its likely explosivity, this event does not appear to have triggered significant climate perturbations, unlike some other large extratropical eruptions. In light of a recent re-evaluation of the Greenland ice-core chronologies, our findings further challenge the previous assignation of this volcanic event to 79 CE. We highlight the need for the revised Common Era ice-core chronology to be formally accepted by the wider ice-core and climate modeling communities in order to ensure robust age linkages to precisely dated historical and paleoclimate proxy records.
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Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5119","displayTitle":"Characterization of Ambient Groundwater Quality Within a Statewide, Fixed-Station Monitoring Network in Pennsylvania, 2015–19","title":"Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","docAbstract":"Pennsylvania leads the Nation in the number of individuals that use groundwater for private domestic water supply; more than 3 million rural and suburban Pennsylvania residents rely on private domestic supplies for drinking water. These supplies are not regulated nor routinely monitored; thus relevant groundwater-quality information is not widely available. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP) Safe Drinking Water Bureau, established a statewide, fixed-station ambient groundwater quality network in 2015. The goals for the Pennsylvania Groundwater Monitoring Network (GWMN) include characterizing ambient groundwater quality conditions in rural areas of the State and documenting potential changes in conditions over time. Seventeen wells were selected for monitoring at 6-month intervals beginning in 2015. Since then, several wells have been added to the GWMN, bringing the total number of wells sampled in the fall of 2019 to 28. Routinely monitored constituents included physical characteristics and chemical concentrations in filtered and unfiltered samples (major and trace elements, nutrients, and organic compounds). Samples for volatile organic compounds (VOCs), radionuclides, and dissolved hydrocarbon gases were collected during the first sampling event at each well.
To offer insights on the quality of groundwater used for domestic supply in Pennsylvania, summary statistics for the 221 GWMN samples collected during 2015–19 are compared to U.S. Environmental Protection Agency (EPA) drinking-water standards, which are applicable to public water supplies. Results show that samples across the GWMN generally meet drinking-water standards for inorganic and organic constituents; however, a percentage of samples had concentrations that exceeded maximum contaminant level (MCL) thresholds for nitrate (3 percent) and secondary maximum contaminant level (SMCL) thresholds for iron (32 percent), manganese (36 percent), and aluminum (5 percent). Radon-222 activities, which were sampled only during the initial visit to a well, exceeded the lower proposed drinking water standard of 300 picocuries per liter (pCi/L) in 64 percent of wells in the GWMN; additionally, 7 percent of wells exceeded the higher proposed standard of 4,000 pCi/L. There were no exceedances for VOCs, but one well had a tribromomethane detection. Three wells had detectable concentrations of methane, with one sample exceeding the Pennsylvania action level of 7 milligrams per liter (mg/L).
The pH and dissolved oxygen concentrations varied widely across the GWMN and were correlated with dissolved metal concentrations and other chemical characteristics of groundwater samples. Considering all samples collected for the study, the pH ranged from 4.2 to 8.3; 42 percent of pH values were either above or below the SMCL range of 6.5–8.5. The highest pH values resulted from contamination of loose grout used in the construction of one well and decreased to levels consistent with other wells in the vicinity after repeated sampling rounds. Dissolved oxygen (DO), which ranged from 0 to 13.9 mg/L, influences the mobility and prevalence of constituents with variable oxidation state, including iron, manganese, and nitrogen species. Samples with acidic pH (less than 6.5) and (or) low DO had the highest concentrations of manganese and iron, whereas those with neutral to alkaline pH values had the highest concentrations of calcium, magnesium, sodium, and other major ions. Analysis of major ions indicates that calcium/bicarbonate water types are the most common, with a few characterized as calcium/chloride or sodium/chloride, and most others as mixed water types including calcium-magnesium/bicarbonate, sodium-magnesium/bicarbonate, and sodium/bicarbonate-chloride.
Nonparametric statistical methods were used to evaluate the data for spatial and temporal trends. A principal components analysis (PCA) model developed with ranked data values for the entire network resulted in three components, (1) dissolved solids, (2) redox, and (3) sodium-chloride, which explained 74.5 percent of variance in the dataset. On the basis of individual contributions to the PCA, certain wells were identified through hierarchical cluster analysis that shared relevant water-quality characteristics. The spatial distribution of sampling locations and the temporal trends of constituent concentrations indicate that hydrogeologic setting and topographic position as defined in the PCA model are important factors affecting the spatial and temporal patterns of groundwater quality in the GWMN.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215119","collaboration":"Prepared in cooperation with Pennsylvania Department of Environmental Protection","usgsCitation":"Conlon, M.D., and Duris, J.W., 2022, Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19: U.S. Geological Survey Scientific Investigations Report 2021–5119, 118 p., https://doi.org/10.3133/sir20215119.","productDescription":"Report: x, 118 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120798","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394185,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWQW7C","text":"USGS data release","linkHelpText":"Data for characterization of ambient groundwater quality within a state-wide, fixed station 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Subduction zone earthquakes result in some of the most devastating natural hazards on Earth. Knowledge of where great (moment magnitude M ≥ 8) subduction zone earthquakes can occur and how they rupture is critical to constraining future seismic and tsunami hazards. Since the occurrence of well-instrumented great earthquakes, such as the 2004 M9.1 Sumatra–Andaman and 2011 M9.1 Tohoku earthquakes, the hypotheses that plate age and convergence rate influence the ability of subduction zones to host large earthquakes have been dispelled. In this Review, we highlight how certain subduction zone properties might influence the location and characteristics of great earthquake rupture and impact seismic and tsunami hazard. The rupture characteristics of great earthquakes that most heavily impact earthquake hazards include the rupture extent (seaward and landward), location of strong motion-generating areas and earthquake recurrence. By contrast, large slip or displacement at the seafloor is one of the major controls of tsunami hazard. Future improvements in addressing hazards posed by subduction zones depend heavily on sustained geophysical monitoring in subduction zone systems (both onshore and offshore), expanded development of palaeoseismic data sets and improved integration of observations and models across disciplines and timescales.
Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.
Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.
BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s
Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid.
As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.
","language":"English","publisher":"BioMed Central Ltd.","doi":"10.1186/s40317-022-00273-3","usgsCitation":"Nebiolo, K., and Castro-Santos, T.R., 2022, BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data: Animal Biotelemetry, v. 10, p. 1-16, https://doi.org/10.1186/s40317-022-00273-3.","productDescription":"2, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-122256","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-022-00273-3","text":"Publisher Index Page"},{"id":394441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nebiolo, Kevin","contributorId":271123,"corporation":false,"usgs":false,"family":"Nebiolo","given":"Kevin","email":"","affiliations":[{"id":56294,"text":"Kleinschmidt Associates, Essex, CT","active":true,"usgs":false}],"preferred":false,"id":830917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":830918,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227815,"text":"70227815 - 2022 - Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile","interactions":[],"lastModifiedDate":"2022-02-02T14:20:06.194879","indexId":"70227815","displayToPublicDate":"2022-01-15T13:52:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile","docAbstract":"Background
Invasive reptiles pose a serious threat to global biodiversity, but early detection of individuals in an incipient population is often hindered by their cryptic nature, sporadic movements, and variation among individuals. Little is known about the mechanisms that affect the movement of these species, which limits our understanding of their dispersal. Our aim was to determine whether translocation or small-scale landscape features affect movement patterns of brown treesnakes (Boiga irregularis), a destructive invasive predator on the island of Guam.
Methods
We conducted a field experiment to compare the movements of resident (control) snakes to those of snakes translocated from forests and urban areas into new urban habitats. We developed a Bayesian hierarchical model to analyze snake movement mechanisms and account for attributes unique to invasive reptiles by incorporating multiple behavioral states and individual heterogeneity in movement parameters.
Results
We did not observe strong differences in mechanistic movement parameters (turning angle or step length) among experimental treatment groups. We found some evidence that translocated snakes from both forests and urban areas made longer movements than resident snakes, but variation among individuals within treatment groups weakened this effect. Snakes translocated from forests moved more frequently from pavement than those translocated from urban areas. Snakes translocated from urban areas moved less frequently from buildings than resident snakes. Resident snakes had high individual heterogeneity in movement probability.
Conclusions
Our approach to modeling movement improved our understanding of invasive reptile dispersal by allowing us to examine the mechanisms that influence their movement. We also demonstrated the importance of accounting for individual heterogeneity in population-level analyses, especially when management goals involve eradication of an invasive species.
Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80–94%. Accounting for previously unmapped dams resulted in a 62–90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.113952","usgsCitation":"Buchanan, B., Sethi, S., Cuppett, S., Lung, M., Jackman, G., Zarri, L., Duvall, E., Dietrich, J., Sullivan, P., Dominitz, A., Archibald, J., Flecker, A., and Rahm, B., 2022, A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams: Journal of Environmental Management, v. 302, no. Part A, 113952, 11 p., https://doi.org/10.1016/j.jenvman.2021.113952.","productDescription":"113952, 11 p.","ipdsId":"IP-129279","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467204,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2021.113952","text":"Publisher Index Page"},{"id":465979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Foundry Brook, Lattintown Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.85258131006508,\n 41.29588488429172\n ],\n [\n -73.85258131006508,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.29588488429172\n ],\n [\n -73.85258131006508,\n 41.29588488429172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"302","issue":"Part A","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buchanan, Brian","contributorId":348048,"corporation":false,"usgs":false,"family":"Buchanan","given":"Brian","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":922901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cuppett, Scott","contributorId":348049,"corporation":false,"usgs":false,"family":"Cuppett","given":"Scott","affiliations":[{"id":56439,"text":"NY DEC","active":true,"usgs":false}],"preferred":false,"id":922902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lung, Megan","contributorId":348050,"corporation":false,"usgs":false,"family":"Lung","given":"Megan","affiliations":[{"id":56439,"text":"NY DEC","active":true,"usgs":false}],"preferred":false,"id":922903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackman, George","contributorId":348051,"corporation":false,"usgs":false,"family":"Jackman","given":"George","affiliations":[{"id":83297,"text":"Riverkeeper, Inc.","active":true,"usgs":false}],"preferred":false,"id":922904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zarri, Liam","contributorId":348052,"corporation":false,"usgs":false,"family":"Zarri","given":"Liam","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duvall, Ethan","contributorId":348053,"corporation":false,"usgs":false,"family":"Duvall","given":"Ethan","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dietrich, Jeremy","contributorId":348054,"corporation":false,"usgs":false,"family":"Dietrich","given":"Jeremy","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sullivan, Patrick","contributorId":348055,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922908,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dominitz, Alon","contributorId":348057,"corporation":false,"usgs":false,"family":"Dominitz","given":"Alon","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":922909,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Archibald, Josephine","contributorId":348060,"corporation":false,"usgs":false,"family":"Archibald","given":"Josephine","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":922910,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Flecker, Alexander","contributorId":348061,"corporation":false,"usgs":false,"family":"Flecker","given":"Alexander","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922911,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rahm, Brian","contributorId":348062,"corporation":false,"usgs":false,"family":"Rahm","given":"Brian","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922912,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70262536,"text":"70262536 - 2022 - Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations","interactions":[],"lastModifiedDate":"2025-01-22T23:17:51.695114","indexId":"70262536","displayToPublicDate":"2022-01-15T00:00:00","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":"Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations","docAbstract":"Historically, the Monongahela, Tygart, and Cheat River watersheds in West Virginia were impaired by acidification from acid mine drainage and Walleye Sander vitreus were extirpated from these watersheds by the 1940s. Walleye were reestablished after water quality improvements following passage of environmental legislation and subsequent reintroduction efforts. We compared population characteristics, with emphasis on growth, of Walleye and used modeling to predict the potential effects of harvest regulations in the Monongahela River and two main-stem reservoirs in the Cheat River and Tygart River watersheds. Statistical comparisons of von Bertalanffy growth curves and relative growth indices indicated that Walleye growth significantly differed across all water bodies. Relative growth index results suggested that Walleye growth was above average in Cheat Lake, average in the Monongahela River, and below average in Tygart Lake relative to other North American populations. Growth was negatively correlated with Walleye relative abundance and positively correlated with estimates of productivity (total phosphorus, chlorophyll a). Walleye diets significantly differed across all water bodies, with diets dominated by Yellow Perch Perca flavescens and Gizzard Shad Dorosoma cepedianum in Cheat Lake, where growth was fastest. Population modeling suggested that effects of exploitation on yield, spawning potential, and size structure were similar under regulations of no length limit and a minimum length limit (381 mm). Models suggested that removing length limits in Tygart Lake could increase angler harvest opportunities and pose minimal threat to the fishery. Models suggested that a protected slot limit could provide increased protection to the spawning potential of Cheat Lake and the Monongahela River populations. Additionally, models predicted that a protected slot limit could increase the number of large (>630-mm) Walleye in these waters. Our findings demonstrate the different characteristics that Walleye populations can develop after reestablishment based on abiotic and biotic conditions and the need for watershed-specific management.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10723","usgsCitation":"Smith, D., Hilling, C., Welsh, S.A., and Wellman Jr., D., 2022, Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations: North American Journal of Fisheries Management, v. 42, no. 3, p. 612-629, https://doi.org/10.1002/nafm.10723.","productDescription":"18 p.","startPage":"612","endPage":"629","ipdsId":"IP-127935","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Cheat Lake, Monongahela River, Tygart Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.43609032800657,\n 39.70971363776053\n ],\n [\n -80.43609032800657,\n 39.288105835150134\n ],\n [\n -79.48527811621443,\n 39.288105835150134\n ],\n [\n -79.48527811621443,\n 39.70971363776053\n ],\n [\n -80.43609032800657,\n 39.70971363776053\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Dustin M.","contributorId":349597,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":924503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hilling, Corbin D.","contributorId":349598,"corporation":false,"usgs":false,"family":"Hilling","given":"Corbin D.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":924504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welsh, Stuart A. 0000-0003-0362-054X","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":217037,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wellman Jr., David I.","contributorId":349599,"corporation":false,"usgs":false,"family":"Wellman Jr.","given":"David I.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":924505,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227372,"text":"ofr20211120 - 2022 - Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","interactions":[],"lastModifiedDate":"2026-03-25T17:51:49.580485","indexId":"ofr20211120","displayToPublicDate":"2022-01-14T14:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1120","displayTitle":"Implementation Plan of the National Cooperative Geologic Mapping Program Strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","title":"Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","docAbstract":"The U.S. Geological Survey (USGS) National Cooperative Geologic Mapping Program (NCGMP) has published a strategic plan entitled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge”. This plan provides the following vision, mission, and goals for the program for the years 2020–30:
To achieve the goal outlined in the strategic plan, the NCGMP has developed an Implementation Plan. This Implementation Plan will guide annual reviews of the FEDMAP component (that is, the component of the USGS NCGMP that funds geologic mapping by USGS geologists) of the NCGMP projects described in the plan and the development of the annual FEDMAP prospectus, which will ensure the application of the NCGMP strategy.
This publication is part of the Implementation Plan of the NCGMP strategy and addresses the following three major topics:
The regions used in this chapter correspond with physiographic divisions of the United States as defined by Fenneman. Physiographic divisions are delineated on the basis of topography, and to a lesser extent, the geologic structure and history. The physiographic divisions are subdivided into physiographic provinces, and the physiographic provinces are subdivided into physiographic sections. Fenneman’s physiographic divisions of the United States provide a robust and useful spatial organization for delineating large geographic regions of the United States for various scientific and industrial applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211120","usgsCitation":"Swezey, C.S., Blome, C.D., Kincare, K.A., Lundstrom, S.C., Stone, B.D., Sweetkind, D.S., Berg, R.C., Brown, S.E., and Yellich, J.A., 2022, Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces): U.S. Geological Survey Open-File Report 2021–1120, 24 p., https://doi.org/10.3133/ofr20211120.","productDescription":"iv, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128891","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501534,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112124.htm","linkFileType":{"id":5,"text":"html"}},{"id":394416,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211120/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":394244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1120/coverthb.jpg"},{"id":394245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.pdf","text":"Report","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1120"},{"id":394246,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1120/images/"},{"id":394247,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.XML"}],"country":"Canada, United States","otherGeospatial":"Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 34.016241889667015\n ],\n [\n -75.234375,\n 34.016241889667015\n ],\n [\n -75.234375,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
The U.S. Geological Survey (USGS) 21st-century science strategy 2020–30 promotes a bureau-wide strategy to develop and deliver an integrated, predictive science capability that works at the scales and timelines needed to inform societally relevant resource management and protection and public safety and environmental health decisions (U.S. Geological Survey, 2021). This is the overarching goal of the USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) vision, which consists of three components: (1) integrated data and information, (2) integrated predictive science, and (3) actionable information—all designed and delivered to respond to user needs. To launch this vision and help shape the design and implementation of integrated predictive science, the USGS Regional Offices each developed a set of use cases (hereafter Use Cases)—short descriptions of potential science applications that could clearly address high priority decision-making needs of our stakeholders and that align with an integrated science focus. Use Cases are not actionable science planning documents, nor stand-alone scholarly works, but should be considered as innovative, next-generation science ideas that can be considered as potential components of science plans still under development. The goal of Use Case development was to (1) identify and characterize existing USGS scientific capacities and expertise that can support science goals and products, (2) identify opportunities to leverage current capacities for next-generation science, and (3) foster engagement across the entire Bureau to further refine the USGS strategy for EarthMAP and integrated predictive science.
The Use Case development effort documented in this report was coordinated by the Use Case Development Team (UCDT), consisting of representatives from each region. The UCDT undertook five tasks: (1) develop a unified approach to engage bureau scientists consistently across all regions in aspirational thinking about what can be accomplished; (2) work with the regions and their Science Centers to generate an initial set of Use Cases, authored directly by scientists; (3) characterize, summarize, and document the initial set of Use Case submissions from authors to illuminate bureau-level demand for integrated science; (4) compare existing and needed capacities from the Use Case descriptions with preliminary results of the EarthMAP Capacity Assessment (Keisman and others, 2021); and (5) describe lessons learned from the Use Case development process and provide recommendations to inform future efforts to generate integrated science activities. This report outlines the approach the UCDT developed to solicit Use Cases from the regions and summarizes the high-level qualitative findings from this first-round effort.
The UCDT received 36 Use Cases from the regions and identified potential points of convergence and commonalities considered useful in making connections among the participating scientists. The Southwest (SW) Region and the Rocky Mountain (RM) Region asked scientists to give special consideration to Use Cases with applicability to the Colorado River Basin, and seven of the Use Cases specifically named that geographic area as a focus. Coastal hazards and coastal resilience were identified in Use Cases from the Alaska (AK), Northeast (NE), and Southeast (SE) Regions. Aspects of wildfire and post-wildfire response were part of Uses Cases from AK, RM, and SW Regions. The greatest convergence of Use Case themes was related to conservation of public lands and waters, which is a powerful linkage lending strength to future collaborative efforts.
The most common type of stakeholder decisions that would be informed by the Use Case science applications were related to adaptation, mitigation, and response (for example, how to increase the resilience of coastal communities to climate-related stressors and how to prevent or respond to harmful algal blooms). Other common types of decisions included water and land management decisions (including operational water management decisions such as reservoir operations and land use planning in the sagebrush biome), decisions about how to manage and conserve habitats and species, and risk management decisions (such as managing the post-wildfire flood risks). These decision types are not exclusive because many Use Cases cross categories.
Use Case authors identified existing and needed science and technology capabilities required for Use Case implementation, which were then aligned to capabilities assessed in the EarthMAP Capacity Assessment (Keisman and others, 2021). Strong alignment was found for data and information integration approaches, modeling and prediction approaches, and capabilities related to delivery of actionable information. A majority of Use Cases indicated insufficient current capacity for needed data collection methods, data integration, and modeling and prediction approaches, whereas only 25 percent indicated insufficient capacity for actionable information delivery. Overall, many Use Case capacity demand gaps could potentially be met by existing bureau-wide capacity. In addition, nearly half of the Use Cases could potentially be implemented within 3 years if funding, capabilities, and personnel impediments were removed and science priorities were realigned.
Several challenges emerged during the Use Case development process. The first challenge was developing an approach that was flexible enough to accommodate regional differences in planning and implementation, while also ensuring enough guidance to promote meaningful summary analyses. The UCDT encountered a strong demand for continuous communication and education to improve overall understanding of the integrated predictive science strategy. Another challenge was managing expectations about EarthMAP activities as a design effort that was not aligned to an immediate funding opportunity. Connecting the Use Cases to stakeholder needs without the opportunity for direct stakeholder engagement was also challenging. The last notable challenge was in obtaining consistent interpretation and characterization of the qualitative data housed in the narrative descriptions of Use Cases, written in different styles.
Overall, the 36 Use Cases can serve as components of a road map for advancing integrated monitoring and predictive science throughout the USGS by revealing opportunities to (1) encourage cross-region initiatives that address shared interests in common themes by integrating similar Use Cases and through direct involvement of stakeholders in identifying needs and designing effective responses, (2) leverage the Use Cases to target investments that are aligned with the Bureau and Department of the Interior (DOI) priorities, (3) connect Use Cases and the results of the companion EarthMAP Capacity Assessment (Keisman and others, 2021) to identify potential priorities for capacity building investments, and (4) raise awareness of common integrated and interdisciplinary science interests within and across the regions through Use Case and Capacity Assessment summary outreach activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211108","programNote":"Science Synthesis, Analysis and Research Program","usgsCitation":"Wilson, T.S., Wiltermuth, M.T., Jenni, K.E., Horton, R.J., Hunt, R.J., Williams, D.M., Nolan, V.P., Aumen, N.G., Brown, D.S., Blasch, K.W., and Murdoch, P.S., 2022, Use case development for earth monitoring, analysis, and prediction (EarthMAP)—A road map for future integrated predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021–1108, 137 p., https://doi.org/10.3133/ofr20211108.","productDescription":"vii, 132 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-129972","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":394402,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1108/covrthb.jpg"},{"id":394403,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394404,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.xml"},{"id":394405,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1108/images"}],"contact":"Director,
U.S. Geological Survey
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Nutrient enrichment can simultaneously increase and destabilise plant biomass production, with co-limitation by multiple nutrients potentially intensifying these effects. Here, we test how factorial additions of nitrogen (N), phosphorus (P) and potassium with essential nutrients (K+) affect the stability (mean/standard deviation) of aboveground biomass in 34 grasslands over 7 years. Destabilisation with fertilisation was prevalent but was driven by single nutrients, not synergistic nutrient interactions. On average, N-based treatments increased mean biomass production by 21–51% but increased its standard deviation by 40–68% and so consistently reduced stability. Adding P increased interannual variability and reduced stability without altering mean biomass, while K+ had no general effects. Declines in stability were largest in the most nutrient-limited grasslands, or where nutrients reduced species richness or intensified species synchrony. We show that nutrients can differentially impact the stability of biomass production, with N and P in particular disproportionately increasing its interannual variability.
Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.
Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.
We present a new field measurement and numerical interpretation method (combined termed ‘test’) to parameterize the diffusion of trichloroethene (TCE) and its biodegradation products (DPs) from the matrix of sedimentary rock. The method uses a dual-packer system to interrogate a low-permeability section of the rock matrix adjacent to a previously contaminated borehole and uses the borehole monitoring history to establish the pre-test condition. TCE and its DPs are removed from the groundwater between the packers at the onset of the testing. The parameters estimated by fitting a radial diffusion model to the concentration history and borehole concentration data, also termed back-diffusion, are the tortuosity factor and sorption coefficients of TCE and DPs in the rock matrix and the TCE and DP biodegradation rate coefficients in the borehole. We demonstrate the equipment design and the interpretive method using a borehole accessing the grey mudstone at a TCE contaminated site in the Newark Basin. In this test, both nonreactive (bromide) and reactive (trichlorofluoroethene) tracers are used to constrain the estimated parameters; however, the bromide tracer was not needed to estimate the parameters in this test. The parameters estimated from the field test are consistent with values measured independently in laboratory experiments using field samples of similar lithology. From the interpretation, we compute the TCE and DP concentration distributions in the rock matrix prior to the test to illustrate how the results can be used to enhance understanding of contaminant distribution in the rock matrix.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12495","usgsCitation":"Allen-King, R.M., Kiekhaefer, R.L., Goode, D.J., Hsieh, P.A., Lorah, M.M., and Imbrigiotta, T.E., 2022, A borehole test for chlorinated solvent diffusion and degradation rates in sedimentary rock: Groundwater Monitoring and Remediation, v. 42, no. 2, p. 23-34, https://doi.org/10.1111/gwmr.12495.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-122580","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394804,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99I50JE","text":"USGS data release","linkHelpText":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen-King, Richelle M. 0000-0001-5559-1213","orcid":"https://orcid.org/0000-0001-5559-1213","contributorId":272047,"corporation":false,"usgs":false,"family":"Allen-King","given":"Richelle","email":"","middleInitial":"M.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiekhaefer, Rebecca L.","contributorId":272048,"corporation":false,"usgs":false,"family":"Kiekhaefer","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":831402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}