The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.
Droughts that are hotter, more frequent, and last longer; pest outbreaks that are more extensive and more common; and fires that are more frequent, more extensive, and perhaps more severe have raised concern that forests in the western United States may not return once disturbed by one or more of these agents. Numerous field-based studies have been undertaken to better understand forest response to these changing disturbance regimes. Meta-analyses of these studies provide broad guidelines on the biotic and abiotic factors that hinder forest recovery, but study-to-study differences in methods and objectives do not support estimation of the total extent of potentially impaired forest succession. In this research, we provide an estimate of the area of potentially impaired forest succession. The estimate was derived from modeling of an 18-year land cover and Normalized Difference Vegetation Index (NDVI) time series supported by an extensive ancillary dataset. We estimate an upper bound of approximately 3470 km2 of disturbed forest that may not return or reattain prior composition and structure. Based on the data used, fire appears to be the main disturbance agent of impaired forest succession, although climatic factors cannot be discounted. The numerous field studies routinely cite distal seed sources as a factor that hinders forest recovery, and we estimate that 20 % of the upper bound estimate has no forest cover within a 4.4-ha neighborhood. Our upper bound estimate is about 0.5 % of the 2001 mapped extent of western United States forests. The estimate is cognizant of measurement and modeling uncertainties (i.e., upper bound) and uncertainties related to successional rates and trajectories (i.e., potential).
Stocking of hatchery-reared fishes has been used with variable success as a management action to promote the recovery of populations and species. The practice has been controversial for several reasons, including uncertainty about whether the hatchery rearing experience may affect reproduction after release. Fine-scale acoustic telemetry was used during three spawning seasons to test whether hatchery rearing affects the reproductive behavior of lake trout using a spawning shoal complex in northern Lake Huron. Within sex, wild- and hatchery-reared fish behaved similarly, but significant behavioral differences occurred between sexes. Lake trout of both sexes moved synchronously onto the spawning shoals at the completion of autumn thermal turnover and occupied the same spawning sites (confirmed visually by presence of fertilized eggs) on the shoals. Male lake trout tended to congregate directly on spawning sites, with duration of occupancy varying greatly among years. Female lake trout spent less time on spawning shoals than males and congregated less at spawning sites on shoals. Most fish visited multiple spawning sites among shoals per season, with many making multiple transits among individual spawning sites. We found no evidence to support the hypothesis that hatchery rearing impairs spawning behavior of lake trout and, therefore, conclude that behavior deficiencies on the spawning ground are likely not an impediment to rehabilitation of lake trout in northern Lake Huron. Our study narrows the field of possible impediments to lake trout rehabilitation in the Great Lakes and provides insights that expand the conceptual model of lake trout spawning behavior.
No abstract available.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3984","usgsCitation":"Keeley, J., 2023, Spatial and temporal strategies of resprouting and seeding in a chaparral shrub species: Ecology, v. 104, no. 4, e3984, 4 p., https://doi.org/10.1002/ecy.3984.","productDescription":"e3984, 4 p.","ipdsId":"IP-143847","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498014,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3984","text":"Publisher Index Page"},{"id":435476,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9713QT4","text":"USGS data release","linkHelpText":"Field Studies of Ceanothus leucodermis Chaparral Burned Sites in California"},{"id":413607,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865389,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70245772,"text":"70245772 - 2023 - A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA","interactions":[],"lastModifiedDate":"2023-06-27T11:42:05.565969","indexId":"70245772","displayToPublicDate":"2023-02-01T06:40:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA","docAbstract":"Reported here are geological, geophysical, mineralogical, and geochemical data on a previously unknown trachyte-hosted rare earth element (REE)-Nb-Zr occurrence at Pennington Mountain in northern Maine, USA. This occurrence was newly discovered by a regional multiparameter, airborne radiometric survey that revealed anomalously high equivalent Th (eTh) and U (eU), confirmed by a detailed ground radiometric survey and by portable X-Ray fluorescence (pXRF) and whole-rock analyses of representative rock samples. The mineralized area occurs within an elongate trachyte body (~1.2 km2) that intrudes Ordovician volcanic rocks. Geologic constraints suggest that the trachyte is also Ordovician in age. The eastern lobe (~900 × ~400 m) of the trachyte is pervasively brecciated with a matrix containing seams, lenses, and veinlets composed mainly of potassium feldspar, albite, and fine-grained zircon and monazite. Barite is locally abundant. Minor minerals within the matrix include columbite, bastnäsite, euxenite, chlorite, pyrite, sphalerite, and magnetite. The pXRF analyses of 22 samples (App. Table A1) collected from the eastern lobe demonstrate that this entire part of the trachyte is highly mineralized. Whole-rock geochemical analyses for samples from the eastern lobe document high average contents of Zr (1.17 wt %), Nb (1,656 ppm), Ba (3,132 ppm), Y (1,140 ppm), Hf (324 ppm), Ta (122 ppm), Th (124 ppm), U (36.5 ppm), Zn (689 ppm), and Sn (106 ppm). Among light REE, the highest average concentrations are shown by La (763 ppm) and Ce (1,479 ppm). For heavy REE (HREE), Dy and Er are the most abundant on average (167 and 114 ppm, respectively). No HREE-rich minerals such as xenotime have been identified; the HREE may reside chiefly in monazite and bastnäsite, and within the fine-grained zircon. Very strong positive correlations (R2) of 0.92 to 0.98 exist between Th and Zr, Nb, Y, Ce, Yb, and Sn, indicating that the radiometric data for eTh are valid proxies for concentrations of these metals in the mineralized rocks.
","language":"English","publisher":"Society for Economic Geologists","doi":"10.5382/econgeo.4993","usgsCitation":"Wang, C., Slack, J.F., Shah, A.K., Yates, M.G., Lentz, D.R., Whittaker, A.T., and Marvinney, R.G., 2023, A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA: Economic Geology, v. 118, no. 1, p. 1-13, https://doi.org/10.5382/econgeo.4993.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-143441","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.4993","text":"Publisher Index Page"},{"id":418496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.73204287455003,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 45.7741074745758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Chunzeng 0000-0002-8362-5174","orcid":"https://orcid.org/0000-0002-8362-5174","contributorId":295415,"corporation":false,"usgs":false,"family":"Wang","given":"Chunzeng","email":"","affiliations":[{"id":63866,"text":"University of Maine at Presque-Isle","active":true,"usgs":false}],"preferred":false,"id":876284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":876285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":876286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yates, Martin G.","contributorId":313571,"corporation":false,"usgs":false,"family":"Yates","given":"Martin","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":876287,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lentz, David R.","contributorId":313573,"corporation":false,"usgs":false,"family":"Lentz","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":876288,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whittaker, Amber T.H.","contributorId":313574,"corporation":false,"usgs":false,"family":"Whittaker","given":"Amber","email":"","middleInitial":"T.H.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marvinney, Robert G.","contributorId":131130,"corporation":false,"usgs":false,"family":"Marvinney","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876290,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251801,"text":"70251801 - 2023 - Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","interactions":[],"lastModifiedDate":"2024-02-29T12:41:18.941661","indexId":"70251801","displayToPublicDate":"2023-02-01T06:37:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","docAbstract":"Carlin-type gold deposits (CTDs) of Nevada are the largest producers of gold in the United States, a leader in world gold production. Although much has been resolved about the characteristics and origin of CTDs in Nevada, major questions remain, especially about (1) the role of magmatism, whether only a source of heat or also metals, (2) whether CTDs only formed in the Eocene, and (3) whether pre-Eocene metal concentrations contributed to Eocene deposits. These issues are exemplified by the CTDs of the Cortez region, the second largest concentration of these deposits after the Carlin trend.
Carlin-type deposits are notoriously difficult to date because they rarely generate dateable minerals. An age can be inferred from crosscutting relationships with dated dikes and other intrusions, which we have done for the giant Cortez Hills CTD. What we term “Cortez rhyolites” consist of two petrographic-geochemical groups of siliceous dikes: (1) quartz-sanidine-plagioclase-biotite-phyric, high-SiO2 rhyolites emplaced at 35.7 Ma based on numerous 40Ar/39Ar dates and (2) plagioclase-biotite-quartz ± hornblende-phyric, low-SiO2 rhyolites, which probably were emplaced at the same time but possibly as early as ~36.2 Ma. The dikes form a NNW-trending belt that is ~6 to 10 km wide × 40 km long and centered on the Cortez Hills deposit, and they require an underlying felsic pluton that fed the dikes. Whether these dikes pre- or postdated mineralization has been long debated. We show that dike emplacement spanned the time of mineralization. Many of both high- and low-SiO2 dikes are altered and mineralized, although none constitute ore. In altered-mineralized dikes, plagioclase has been replaced by kaolinite and calcite, and biotite by smectite, calcite, and marcasite. Sanidine is unaltered except in a few samples that are completely altered to quartz and kaolinite. Sulfides present in mineralized dikes are marcasite, pyrite, arsenopyrite, and As-Sb–bearing pyrite. Mineralized dikes are moderately enriched in characteristic Carlin-type elements (Au, Hg, Sb, Tl, As, and S), as well as elements found in some CTDs (Ag, Bi, Cu, Mo), and variably depleted in MgO, CaO, Na2O, K2O, MnO, Rb, Sr, and Ba. In contrast, some high-SiO2 rhyolites are unaltered and cut high-grade ore, which shows that they are post-ore. Both mineralized and post-ore dikes have indistinguishable sanidine 40Ar/39Ar dates. These characteristics, along with published interpretations that other giant CTDs formed in a few tens of thousands of years, indicate the Cortez Hills CTD formed at 35.7 Ma. All Cortez-area CTDs are in or adjacent to the Cortez rhyolite dike swarm, which suggests that the felsic pluton that fed the dikes was the hydrothermal heat source. Minor differences in alteration and geochemistry between dikes and typical Paleozoic sedimentary rock-hosted ore probably reflect low permeability and low reactivity of the predominantly quartzofeldspathic dikes.
Despite widespread pre-35.7 Ma mineralization in the Cortez region, including deposits near several CTDs, we find no evidence that older deposits or Paleozoic basinal rocks contributed metals to Cortez-area CTDs. Combining our new information about the age of Cortez Hills with published and our dates on other CTDs demonstrates that CTD formation coincided with the southwestern migration of magmatism across Nevada, supporting a genetic relationship to Eocene magmatism. CTDs are best developed where deep-seated (~6–8 km), probably granitic plutons, expressed in deposits only as dikes, established large, convective hydrothermal systems.
Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.
Finchite (IMA2017-052), Sr(UO2)2(V2O8)·5H2O, is the first uranium mineral known to contain essential Sr. The new mineral occurs as yellow-green blades up to ~10 µm in length in surface outcrops of the calcrete-type uranium deposit at Sulfur Springs Draw, Martin County, Texas, U.S.A. Crystals of finchite were subsequently discovered underground in the Pandora mine, La Sal, San Juan County, Utah, U.S.A., as diamond-shaped golden-yellow crystals reaching up to 1 mm. The crystal structure of finchite from both localities was determined using single-crystal X-ray diffraction and is orthorhombic, Pcan, with a = 10.363(6) Å, b = 8.498(5) Å, c = 16.250(9) Å, V = 1431.0(13) Å3, Z = 4 (R1 = 0.0555) from Sulfur Springs Draw; and a = 10.3898(16), b = 8.5326(14), c = 16.3765(3) Å, V = 1451.8(4) Å3, Z = 4 (R1 = 0.0600) from the Pandora mine. Electron-probe microanalysis provided the empirical formula (Sr0.88K0.17Ca0.10Mg0.07Al0.03Fe0.02)Σ1.20(UO2)2(V2.08O8)·5H2O for crystals from Sulfur Springs Draw, and (Sr0.50Ca0.28Ba0.22K0.05)Σ0.94(U0.99O2)2(V2.01O8)·5H2O for crystals from the Pandora mine, based on 17 O atoms per formula unit. The structure of finchite contains uranyl vanadate sheets based upon the francevillite topology. Finchite is a possible immobilization species for both uranium and the dangerous radionuclide 90Sr because of the relative insolubility of uranyl vanadate minerals in water.
The Penobscot River Restoration Project in Maine was a large river rehabilitation project that culminated in the removal of the two lowermost dams and improvements to fish passage on several remaining dams. Fish assemblages were surveyed for 3 years prior to rehabilitation, 3 years after rehabilitation, and 8 years after rehabilitation. Approximately 475 km of shoreline were sampled via boat electrofishing, yielding 133,394 individual fish of 41 species. The greatest shifts in assemblage structure occurred immediately after dam removal in formerly impounded sections, with an increased prevalence of riverine and migratory species. Long-term sampling documented changes within tributaries and tidally influenced river segments, where large schools of adult and young-of-the-year alosines increased in abundance. Upstream of the lowermost dam, the river remains dominated by lacustrine species, while adult anadromous fishes continue to be most abundant immediately downstream of the lowermost dam. Our results provide increased evidence that dam removals result in altered fish assemblages, which are now dominated by riverine and anadromous species in previously impounded habitats. Alosines in the Penobscot River have exhibited the greatest long-term response to river restoration efforts.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10227","usgsCitation":"Whittum, K., Zydlewski, J.D., Coghlan, S., Hayes, D., Watson, J., and Kiraly, I., 2023, Fish assemblages in the Penobscot River: A decade after dam removal: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 1, e10227, 18 p., https://doi.org/10.1002/mcf2.10227.","productDescription":"e10227, 18 p.","ipdsId":"IP-146223","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10227","text":"Publisher Index Page"},{"id":466424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.68431252548459,\n 45.55932755468805\n ],\n [\n -69.68431252548459,\n 44.23415058804457\n ],\n [\n -68.27321921462578,\n 44.23415058804457\n ],\n [\n -68.27321921462578,\n 45.55932755468805\n ],\n [\n -69.68431252548459,\n 45.55932755468805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Whittum, Kory A.","contributorId":348238,"corporation":false,"usgs":false,"family":"Whittum","given":"Kory A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":923299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coghlan, Stephen M. Jr.","contributorId":348224,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen M. Jr.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Daniel B.","contributorId":348239,"corporation":false,"usgs":false,"family":"Hayes","given":"Daniel B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":923301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watson, Jonathan","contributorId":348240,"corporation":false,"usgs":false,"family":"Watson","given":"Jonathan","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":923302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kiraly, Ian","contributorId":348241,"corporation":false,"usgs":false,"family":"Kiraly","given":"Ian","affiliations":[{"id":83328,"text":"Gomez and Sullivan Engineers, DPC","active":true,"usgs":false}],"preferred":false,"id":923303,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266303,"text":"70266303 - 2023 - Connecting research and practice to enhance the evolutionary potential of species under climate change","interactions":[],"lastModifiedDate":"2025-05-02T15:32:10.928537","indexId":"70266303","displayToPublicDate":"2023-02-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Connecting research and practice to enhance the evolutionary potential of species under climate change","docAbstract":"Resource managers have rarely accounted for evolutionary dynamics in the design or implementation of climate change adaptation strategies. We brought the research and management communities together to identify challenges and opportunities for applying evidence from evolutionary science to support on-the-ground actions intended to enhance species' evolutionary potential. We amalgamated input from natural-resource practitioners and interdisciplinary scientists to identify information needs, current knowledge that can fill those needs, and future avenues for research. Three focal areas that can guide engagement include: (1) recognizing when to act, (2) understanding the feasibility of assessing evolutionary potential, and (3) identifying best management practices. Although researchers commonly propose using molecular methods to estimate genetic diversity and gene flow as key indicators of evolutionary potential, we offer guidance on several additional attributes (and their proxies) that may also guide decision-making, particularly in the absence of genetic data. Finally, we outline existing decision-making frameworks that can help managers compare alternative strategies for supporting evolutionary potential, with the goal of increasing the effective use of evolutionary information, particularly for species of conservation concern. We caution, however, that arguing over nuance can generate confusion; instead, dedicating increased focus on a decision-relevant evidence base may better lend itself to climate adaptation actions.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12855","usgsCitation":"Thompson, L., Thurman, L., Cook, C.N., Beever, E.A., Sgro, C., Battles, A., Botero, C., Gross, J.E., Hall, K., Hendry, A.P., Hoffmann, A., Hoving, C., LeDee, O.E., Mengelt, C., Nicotra, A., Niver, R., Pérez-Jvostov, F., Quiñones, R., Schuurman, G.W., Schwartz, M.K., Szymanski, J., and Whiteley, A., 2023, Connecting research and practice to enhance the evolutionary potential of species under climate change: Conservation Science and Practice, v. 5, no. 2, e12855, 18 p., https://doi.org/10.1111/csp2.12855.","productDescription":"e12855, 18 p.","ipdsId":"IP-134501","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":487930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12855","text":"Publisher Index Page"},{"id":485334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":212190,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":935461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurman, Lindsey 0000-0003-3142-4909","orcid":"https://orcid.org/0000-0003-3142-4909","contributorId":269425,"corporation":false,"usgs":true,"family":"Thurman","given":"Lindsey","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science 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M.","contributorId":354358,"corporation":false,"usgs":false,"family":"Quiñones","given":"Rebecca M.","affiliations":[],"preferred":false,"id":935478,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":935479,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Schwartz, Michael K.","contributorId":199035,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":935480,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":935481,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Whiteley, Andrew R.","contributorId":286853,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":935482,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70262834,"text":"70262834 - 2023 - Soil and geomorphic patterns within relict charcoal hearths could represent unique ecosystem niches","interactions":[],"lastModifiedDate":"2025-01-24T15:40:58.468588","indexId":"70262834","displayToPublicDate":"2023-02-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Soil and geomorphic patterns within relict charcoal hearths could represent unique ecosystem niches","docAbstract":"Hearths used for 19th and 20th century charcoal manufacturing have been found to have unique plant communities or to produce unique growth characteristics for some species but not others. Given known differences in hearth morphology, within hearth physical and chemical differences may exist and result in unique ecologic niches. We examined soil stratigraphy across 8 relict charcoal hearths (RCH) and control soils on different landforms near a 19th century furnace complex (Greenwood Furnace, northcentral Appalachians USA). Soils were analyzed for particle size, total and trace elements, and fertility. Platform creation resulted in soils from upslope RCH positions mixed with subsurface materials on the downslope side to create a stabilized platform. The thin, uniform thickness of charcoal surface horizons (Ac) suggests that RCHs were not used more than once for charcoal manufacturing or that charcoal was always removed very efficiently. While rubification of soil or rock from high heat was seen in 6 of 8 sites sampled, it was not extensive across sampled areas of any one hearth, which suggests hearth use may not have frequent, hot enough, or spatially disparate. Soil fertility characteristics change within RCHs but also by landscape position. Downslope RCH positions are enriched in some parameters compared to control soils (total C, Mehlich 3 Mg, Ca, and Aquia Regia digestion Mn) and that enrichment often is from the surface downward. Downslope enrichment within RCH soils may have occurred from charcoal and released ions, slope erosion and accumulation, or transportation of constructed materials during RCH creation. Landscape position may accentuate or mute soil chemistry differences. Greenwood Furnace RCHs have unique patterns of chemistry, which could result in unique niches for flora and maybe fauna within RCH. Future research should more closely investigate whether hearths support unique species assemblages and how they may play a role in enhancing today’s forest biodiversity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2022.108525","usgsCitation":"Bayuzick, S., Guarin, D., Benavides, J., Bonhage, A., Hirsch, F., Diefenbach, D.R., McDill, M., Raab, T., and Drohan, P., 2023, Soil and geomorphic patterns within relict charcoal hearths could represent unique ecosystem niches: Geomorphology, v. 422, 108525, 16 p., https://doi.org/10.1016/j.geomorph.2022.108525.","productDescription":"108525, 16 p.","ipdsId":"IP-144124","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2022.108525","text":"Publisher Index Page"},{"id":481138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Greenwood Furnace State Park, Rothrock State Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.88527087498365,\n 40.75432923716272\n ],\n [\n -77.88527087498365,\n 40.594519811533985\n ],\n [\n -77.64685924071077,\n 40.594519811533985\n ],\n [\n -77.64685924071077,\n 40.75432923716272\n ],\n [\n -77.88527087498365,\n 40.75432923716272\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"422","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bayuzick, S.","contributorId":348658,"corporation":false,"usgs":false,"family":"Bayuzick","given":"S.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guarin, D.","contributorId":348662,"corporation":false,"usgs":false,"family":"Guarin","given":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benavides, J.","contributorId":349873,"corporation":false,"usgs":false,"family":"Benavides","given":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":925005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonhage, A.","contributorId":348664,"corporation":false,"usgs":false,"family":"Bonhage","given":"A.","affiliations":[{"id":83395,"text":"Brandenburg University of Technology","active":true,"usgs":false}],"preferred":false,"id":924954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hirsch, F.","contributorId":348665,"corporation":false,"usgs":false,"family":"Hirsch","given":"F.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924956,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McDill, M.","contributorId":348666,"corporation":false,"usgs":false,"family":"McDill","given":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924957,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raab, T.","contributorId":348667,"corporation":false,"usgs":false,"family":"Raab","given":"T.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924958,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Drohan, P.J.","contributorId":348668,"corporation":false,"usgs":false,"family":"Drohan","given":"P.J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924959,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262355,"text":"70262355 - 2023 - Effects of freshwater residence time on reproductive success in anadromous alewife (Alosa pseudoharengus): climate change implications","interactions":[],"lastModifiedDate":"2025-01-17T17:23:28.08865","indexId":"70262355","displayToPublicDate":"2023-02-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Effects of freshwater residence time on reproductive success in anadromous alewife (Alosa pseudoharengus): climate change implications","docAbstract":"Earlier spring warming and anadromous fish migrations prompted by climate change are linked to shorter freshwater residency. Impacts of phenological change on anadromous fish populations are poorly understood with limited studies focused on iteroparous non-salmonids. We assessed freshwater residence time and reproductive success in an iteroparous clupeid, alewife (Alosa pseudoharengus) using a pedigree analysis and otolith-based spawning dates from captured juveniles. The primary objectives were to (1) estimate adult spawning duration in a freshwater pond (freshwater residence time) and (2) evaluate adult freshwater residence time, arrival date, length, sex, and reproductive success across 2 years in one system. Estimated freshwater residence times varied widely (1–64 days), and longer residence times were associated with earlier arrival dates, higher reproductive success, and more mating events. Longer freshwater residence times may allow alewife to spawn with more mates, produce more gametes, and experience a range of spawning and nursery conditions. Plasticity in alewife freshwater residence time could support earlier and shorter migration periods but may result in lower reproductive output if adults spend less time in freshwater ponds.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2022-0086","usgsCitation":"Marjadi, M., Roy, A.H., Devine, M., Gahagan, B., Jordaan, A., Rosset, J., and Whiteley, A., 2023, Effects of freshwater residence time on reproductive success in anadromous alewife (Alosa pseudoharengus): climate change implications: Canadian Journal of Fisheries and Aquatic Sciences, v. 80, no. 3, p. 563-576, https://doi.org/10.1139/cjfas-2022-0086.","productDescription":"14 p.","startPage":"563","endPage":"576","ipdsId":"IP-139986","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":501006,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/126432","text":"External Repository"},{"id":480755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Pentucket Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.00429206328943,\n 42.73849221387013\n ],\n [\n -71.00429206328943,\n 42.729638028060464\n ],\n [\n -70.98878808335037,\n 42.729638028060464\n ],\n [\n -70.98878808335037,\n 42.73849221387013\n ],\n [\n -71.00429206328943,\n 42.73849221387013\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marjadi, Meghna N.","contributorId":348960,"corporation":false,"usgs":false,"family":"Marjadi","given":"Meghna N.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":923900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devine, Matthew T.","contributorId":348961,"corporation":false,"usgs":false,"family":"Devine","given":"Matthew T.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":923902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gahagan, Benjamin I.","contributorId":348962,"corporation":false,"usgs":false,"family":"Gahagan","given":"Benjamin I.","affiliations":[{"id":39892,"text":"Massachusetts Division of Marine Fisheries","active":true,"usgs":false}],"preferred":false,"id":923903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jordaan, Adrian","contributorId":348963,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":923904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rosset, Julianne","contributorId":348964,"corporation":false,"usgs":false,"family":"Rosset","given":"Julianne","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whiteley, Andrew R.","contributorId":348965,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":923906,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70241891,"text":"70241891 - 2023 - Monitoring native nonsalmonids for the incidence of gas bubble trauma downstream of Snake and Columbia River dams during the spring spill season, 2022","interactions":[],"lastModifiedDate":"2023-03-30T15:25:01.120613","indexId":"70241891","displayToPublicDate":"2023-01-31T10:06:22","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"90045-1","title":"Monitoring native nonsalmonids for the incidence of gas bubble trauma downstream of Snake and Columbia River dams during the spring spill season, 2022","docAbstract":"In 2020, a new spill program was implemented to aid the downstream passage of juvenile salmonids at mainstem dams on the Snake and Columbia rivers. Under this program, the total \ndissolved gas (TDG) cap was increased to 125% and monitoring of native nonsalmonids for gas \nbubble trauma (GBT) became a requirement. The primary objective of this work was to measure\nthe incidence and severity of GBT in native nonsalmonids resulting from increased juvenile fish \npassage spill and associated levels of TDG during the spring spill period. Native nonsalmonids \nwere collected downstream from Bonneville, McNary, Ice Harbor, and Lower Granite dams and \nexamined for the incidence and severity of GBT in 2022. Fish were collected at each location weekly (4 April to 13 June) during the spring spill period by backpack electrofishing and beach seining. Washington and Oregon state water quality agencies established minimum and target sample sizes for monitoring, but the minimum sample size of 50 fish and target sample size of \n100 fish were not met in all weeks at individual projects due to high water flows and resulting low fish collections. Collected fish were examined for GBT according to the criteria and protocol established for the regional smolt monitoring program (SMP). Overall, GBT incidence and \nseverity rankings were low and did not exceed the thresholds that would have triggered changes\nto the spill program. Using SMP criteria, maximum weekly GBT incidences were 1.9% \ndownstream from Bonneville Dam, 8.6% downstream from McNary Dam, 7.7% downstream \nfrom Ice Harbor Dam, and 3.4% downstream from Lower Granite Dam. In contrast to 2021, \nseveral species showed signs of GBT in 2022, and GBT was commonly observed in fins and \nbody locations other than the unpaired fins and eyes (i.e., SMP criteria). In general, the observed \nsigns of GBT according to SMP criteria were not severe (i.e., ranks 1 and 2). Although TDG \nreached the gas cap, particularly late in the spring spill season, GBT incidence rates were not \nhigh probably due to factors such as habitat-related variability in TDG, species composition and \nvarying tolerance to high TDG, low power to detect GBT incidence at small sample sizes, and \nunknown exposure history.","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Tiffan, K.F., Liedtke, B.D., Lebeda, D.D., Benson, S.L., and Warren, J.J., 2023, Monitoring native nonsalmonids for the incidence of gas bubble trauma downstream of Snake and Columbia River dams during the spring spill season, 2022, vii, 49 p.","productDescription":"vii, 49 p.","ipdsId":"IP-148619","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":414974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414942,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Columbia River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.05159276847027,\n 46.83175760373237\n ],\n [\n -123.95675689537998,\n 46.83175760373237\n ],\n [\n -123.95675689537998,\n 45.17347125976491\n ],\n [\n -117.05159276847027,\n 45.17347125976491\n ],\n [\n -117.05159276847027,\n 46.83175760373237\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":220176,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":868109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Brad D. 0000-0002-0458-7377","orcid":"https://orcid.org/0000-0002-0458-7377","contributorId":303795,"corporation":false,"usgs":true,"family":"Liedtke","given":"Brad","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":868110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lebeda, Dalton Dirk 0000-0001-9071-8400","orcid":"https://orcid.org/0000-0001-9071-8400","contributorId":251867,"corporation":false,"usgs":true,"family":"Lebeda","given":"Dalton","email":"","middleInitial":"Dirk","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":868111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benson, Scott Louis 0000-0003-0397-1200","orcid":"https://orcid.org/0000-0003-0397-1200","contributorId":303796,"corporation":false,"usgs":true,"family":"Benson","given":"Scott","email":"","middleInitial":"Louis","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":868112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warren, Joe J. 0000-0001-5632-730X jwarren@usgs.gov","orcid":"https://orcid.org/0000-0001-5632-730X","contributorId":265131,"corporation":false,"usgs":true,"family":"Warren","given":"Joe","email":"jwarren@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":868113,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240146,"text":"sir20225109 - 2023 - Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18","interactions":[],"lastModifiedDate":"2026-02-23T19:37:25.988863","indexId":"sir20225109","displayToPublicDate":"2023-01-31T10:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5109","displayTitle":"Comparison of Surrogate Models To Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites During Water Years 2013–18","title":"Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18","docAbstract":"During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality.
The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R2), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit.
In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225109","programNote":"National Water Quality Program","usgsCitation":"Covert, S.A., Bunch, A.R., Crawford, C.G., and Oelsner, G.P., 2023, Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18: U.S. Geological Survey Scientific Investigations Report 2022–5109, 17 p., https://doi.org/10.3133/sir20225109.","productDescription":"Report: v, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-132946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":500456,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114298.htm","text":"central U.S. study 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Each mineral commodity chapter of the 2023 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. The MCS is the earliest comprehensive source of 2022 mineral production data for the world. More than 90 individual minerals and materials are covered by 2-page synopses.
For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the 2-page synopsis.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2023 are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2023","usgsCitation":"U.S. Geological Survey, 2023, Mineral commodity summaries 2023: U.S. Geological Survey, 210 p., https://doi.org/10.3133/mcs2023.","productDescription":"Report: 210 p.; Data Release","numberOfPages":"210","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147940","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":499701,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114301.htm","linkFileType":{"id":5,"text":"html"}},{"id":412083,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WCYUI6","text":"USGS data release","linkHelpText":"Data release for mineral commodity summaries 2023"},{"id":412082,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":412081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2023"},{"id":412080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/mcs2023.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2023"},{"id":412079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/coverthb.jpg"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","conferenceDate":"January 24-25, 2023","conferenceLocation":"Flagstaff, AZ","language":"English","collaboration":"Bureau of Reclamation","usgsCitation":"Deemer, B., Voichick, N., Sabol, T.A., Andrews, C.M., and Mihalevich, B.A., 2023, Appendix 1: Lake Powell water quality monitoring, in Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program, Flagstaff, AZ, January 24-25, 2023, p. 134-142.","productDescription":"9 p.","startPage":"134","endPage":"142","ipdsId":"IP-147768","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":416490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416481,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/uc/progact/amp/twg/2023-01-26-twg-meeting/20230126-AnnualReportingMeeting-ProceedingsFY2022AnnualReportingMeeting-508-UCRO.pdf"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.98991581400394,\n 36.92878972693423\n ],\n [\n -109.82511758909659,\n 36.92878972693423\n ],\n [\n -109.82511758909659,\n 38.054642642406435\n ],\n [\n -111.98991581400394,\n 38.054642642406435\n ],\n [\n -111.98991581400394,\n 36.92878972693423\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voichick, Nicholas 0000-0002-9716-5906 nvoichick@usgs.gov","orcid":"https://orcid.org/0000-0002-9716-5906","contributorId":203632,"corporation":false,"usgs":true,"family":"Voichick","given":"Nicholas","email":"nvoichick@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mihalevich, Bryce Anthony 0000-0001-5492-221X","orcid":"https://orcid.org/0000-0001-5492-221X","contributorId":304586,"corporation":false,"usgs":true,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"Anthony","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871019,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240201,"text":"70240201 - 2023 - Joint spatiotemporal models to predict seabird densities at sea","interactions":[],"lastModifiedDate":"2023-02-01T13:11:05.524963","indexId":"70240201","displayToPublicDate":"2023-01-31T07:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Joint spatiotemporal models to predict seabird densities at sea","docAbstract":"Introduction: Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.
Methods: Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions.
Results: The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).
Discussion: Our results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2023.1078042","usgsCitation":"Arimitsu, M.L., Piatt, J., Thorson, J., Kuletz, K., Drew, G., Schoen, S.K., Cushing, D., Kroeger, C., and Sydeman, W., 2023, Joint spatiotemporal models to predict seabird densities at sea: Frontiers in Marine Science, v. 10, 1078042, 11 p., https://doi.org/10.3389/fmars.2023.1078042.","productDescription":"1078042, 11 p.","ipdsId":"IP-145204","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":444664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1078042","text":"Publisher Index Page"},{"id":412531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.65747250220048,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 58.811862749051784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatt, John F. 0000-0002-4417-5748","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":244053,"corporation":false,"usgs":true,"family":"Piatt","given":"John F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorson, James","contributorId":301893,"corporation":false,"usgs":false,"family":"Thorson","given":"James","affiliations":[{"id":65201,"text":"NOAA AFSC","active":true,"usgs":false}],"preferred":false,"id":862947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuletz, Kathy","contributorId":301894,"corporation":false,"usgs":false,"family":"Kuletz","given":"Kathy","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":862948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drew, Gary 0000-0002-6789-0891","orcid":"https://orcid.org/0000-0002-6789-0891","contributorId":300561,"corporation":false,"usgs":false,"family":"Drew","given":"Gary","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":862949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cushing, Dan","contributorId":301895,"corporation":false,"usgs":false,"family":"Cushing","given":"Dan","email":"","affiliations":[{"id":65202,"text":"Pole Star Ecological LLC","active":true,"usgs":false}],"preferred":false,"id":862951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kroeger, Caitlin","contributorId":301897,"corporation":false,"usgs":false,"family":"Kroeger","given":"Caitlin","email":"","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862952,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sydeman, William","contributorId":301898,"corporation":false,"usgs":false,"family":"Sydeman","given":"William","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862953,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241025,"text":"70241025 - 2023 - Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","interactions":[],"lastModifiedDate":"2023-03-07T12:47:56.075162","indexId":"70241025","displayToPublicDate":"2023-01-31T06:40:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","docAbstract":"Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.
Evolutionary relatedness underlies patterns of functional diversity in the natural world. Hyperspectral remote sensing has the potential to detect these patterns in plants through inherited patterns of leaf reflectance spectra. We collected leaf reflectance data from across the California flora from plants grown in a common garden. Regions of the reflectance spectra vary in the depth and strength of phylogenetic signal. We also show that these differences are much greater than variation due to the geographic origin of the plant. At the phylogenetic extent of the California flora, spectral variation explained by the combination of ecotypic variation (divergent evolution) and convergent evolution of disparate lineages was minimal (3 to 7 %) but statistically significant. Interestingly, at the extent of a single genus (Arctostaphylos) no unique variation could be attributed to geographic origin. However, up to 18% of the spectral variation among Arctostaphylos individuals was shared between phylogeny and intraspecific variation stemming from ecotypic differences (i.e., geographic origin). Future studies could conduct more structured experiments (e.g., transplants or observations along environmental gradients) to disentangle these sources of variation and include other intraspecific variation (e.g., plasticity). We constrain broad scale spectral variability due to ecotypic sources, providing further support for the idea that phylogenetic clusters of species might be detectable through remote sensing. Phylogenetic clusters could represent a valuable dimension of biodiversity monitoring and detection.
The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and Escherichia coli at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225130","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., 2023, Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5130, 14 p., https://doi.org/10.3133/sir20225130.","productDescription":"Report: vii, 14 p.; 14 Appendixes; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141414","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":412468,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225130/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412446,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412445,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5130/downloads","text":"Appendixes 1–14","linkFileType":{"id":1,"text":"pdf"}},{"id":412443,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500484,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114304.htm","linkFileType":{"id":5,"text":"html"}},{"id":412442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.pdf","text":"Report","size":"5.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5130"},{"id":412441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5130/coverthb.jpg"},{"id":412444,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5130/images"}],"country":"United States","state":"Kansas","city":"Topeka","otherGeospatial":"Kansas River, Topeka Weir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.66443090940162,\n 39.06286764035286\n ],\n [\n -95.67191642810837,\n 39.06117240426602\n ],\n [\n -95.64218228435746,\n 39.06262546626132\n ],\n [\n -95.64239021543237,\n 39.064239944513304\n ],\n [\n -95.63864745607941,\n 39.07925282377511\n ],\n [\n -95.63812762839112,\n 39.08401430643988\n ],\n [\n -95.63251348936144,\n 39.0836915042114\n ],\n [\n -95.63199366167355,\n 39.08054410506932\n ],\n [\n -95.61452745135817,\n 39.07780010407046\n ],\n [\n -95.61463141689583,\n 39.07465244209993\n ],\n [\n -95.6078736569523,\n 39.057620349038984\n ],\n [\n -95.60225951792262,\n 39.05794327053502\n ],\n [\n -95.59716520658046,\n 39.05826619055418\n ],\n [\n -95.58572899744581,\n 39.0606880436211\n ],\n [\n -95.58884796357322,\n 39.063432710002644\n ],\n [\n -95.59300658507682,\n 39.070778203846714\n ],\n [\n -95.59092727432522,\n 39.08958241237616\n ],\n [\n -95.60589831173833,\n 39.09127696606694\n ],\n [\n -95.60849745017785,\n 39.08732294411979\n ],\n [\n -95.61702262426043,\n 39.08691946002881\n ],\n [\n -95.62222090113984,\n 39.08611248492275\n ],\n [\n -95.63532055887627,\n 39.090712119361\n ],\n [\n -95.64166245666956,\n 39.08917894121052\n ],\n [\n -95.65101935505261,\n 39.08361080342297\n ],\n [\n -95.65663349408229,\n 39.076266645265974\n ],\n [\n -95.66058418451098,\n 39.067145912321024\n ],\n [\n -95.66443090940162,\n 39.06286764035286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223016","usgsCitation":"Dahm, K.G., Jones, D.K., Anderson, P.J., Dick, M.C., Hawbaker, T.J., and Horton, R.J., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST): U.S. Geological Survey Fact Sheet 2022–3016, 4 p., https://doi.org/10.3133/fs20223016.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-133121","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":416352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3016"},{"id":416351,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.xml"},{"id":411878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3016/coverthb.jpg"},{"id":411879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.pdf","text":"Report","size":"5.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3016"},{"id":416350,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3016/images"}],"country":"Mexico, United Statetes","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225
The U.S. Geological Survey (USGS) conducts a wide variety of science that improves understanding of droughts and their effects on ecosystems and society. This work includes data collection and monitoring of aquatic and terrestrial systems; assessment and analysis of patterns, trends, drivers, and impacts of drought; development and application of predictive models; and delivery of information and decision-making tools to stakeholders. Stakeholders, which include Federal, Tribal, State, and local agencies, nongovernmental organizations, and others, use this information to anticipate, assess, react to, and mitigate drought conditions and impacts. There is no obvious near-term solution to reduce the frequency and severity of droughts or to mitigate drought impacts. Multidecadal drought is a “grand challenge” that benefits from integration of existing technologies, data, knowledge, and models across related and disparate disciplines, facilitated by new science and technology. In response, the USGS initiated a new integrated-science approach in the Colorado River Basin in 2020. The Colorado River Basin was specifically selected because of concerns about future drought and its consequences for the region. This document explains how the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) project extends and enhances the science supported by USGS Mission Areas and Programs and articulates scientific gaps and stakeholder needs to identify and reduce drought risks. The approach seeks to answer complex Earth science questions developed in partnership with stakeholders about severe long-term drought. An integrated approach is required to tackle these complex questions, which any single science discipline cannot answer on its own.
In addition to increased understanding of drought and drought effects in complex systems, the Colorado River Basin ASIST project was designed to improve efficiencies through rapid location of a broad array of data sources, assembly of model-ready multidisciplinary data, and delivery of actionable science to stakeholders at the speeds and scales needed for decision making. The project team identified the following actions needed for USGS to implement and advance an integrated science approach in the Colorado River Basin: (1) engage with stakeholders to document their needs and iteratively co-produce science and science delivery tools to address these needs, (2) integrate monitoring and observation systems developed by USGS and other agencies that track droughts and their effects, (3) collect and provide analysis-ready data to support integrated applications, (4) integrate data and model connections to predict multiple drought impacts, (5) conduct multidisciplinary coordination to improve interpretations, (6) leverage the knowledge base across USGS to enhance decision making, and (7) support the development of new integrated science approaches and technologies that provide analysis and management tools that can be used to adapt to the effects of drought in the Colorado River Basin. Proposals for initial short-term use-case projects were solicited, a subset of which was selected for funding to test implementation of these actions. Additionally, the project organized and convened a series of science and technology collaboration workshops in the USGS focused on challenges that were identified and prioritized by the short-term use-case projects and during the initial stakeholder analysis. These workshops were designed to bring together diverse perspectives to discuss science and technology challenges, stakeholder needs, capabilities, and knowledge gaps, with the goal of determining how the USGS can address challenges, identify future opportunities for continued engagement between participants, and inform the next steps for the Colorado River Basin ASIST project. Continuing to collaboratively engage with a wide range of stakeholders using an integrated approach will provide a suitable foundation of data and tools to formulate actionable intelligence for predicting droughts and informing adaptation to the effects of long-term drought in a holistic, timely, and effective manner.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1502","usgsCitation":"Dahm, K., Hawbaker, T., Frus, R., Monroe, A., Bradford, J., Andrews, W., Torregrosa, A., Anderson, E., Dean, D., and Qi, S., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy: U.S. Geological Survey Circular 1502, 57 p., https://doi.org/10.3133/cir1502.","productDescription":"vi, 53 p.","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-130999","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":416723,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1502/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1502"},{"id":411865,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1502/circ1502.xml"},{"id":411861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1502/coverthb.jpg"},{"id":411862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1502/circ1502.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1502"},{"id":411864,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1502/images"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225
Health and diseases are integral parts of the life of seabirds that merit attention if we expect to truly understand, protect, and conserve them. Diseases such as avian influenza, avian pox, pasteurellosis, and paralytic shellfish poisoning have a proven history of decreasing the survival or breeding success of seabirds. However, each host-pathogen-environment system is unique, and our current knowledge about seabird health is limited and subject to biases. Thus, an exploratory mindset should be maintained, always considering that new or previously undiagnosed diseases could have substantial effects on a given seabird population. Therefore, incorporating a health monitoring component in seabird population monitoring programs, wherein data and biological samples are routinely collected for long-term pathogen surveillance and physiological analyses, would help us understand factors that limit seabird populations. Finally, the implementation of biosecurity best practices at seabird aggregations is imperative to avoid the accidental introduction or spread of pathogens.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Conservation of marine birds","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","doi":"10.1016/B978-0-323-88539-3.00003-0","usgsCitation":"Vanstreels, R., Uhart, M., and Work, T.M., 2023, Chapter 5: Health and diseases, chap. of Conservation of marine birds, p. 131-176, https://doi.org/10.1016/B978-0-323-88539-3.00003-0.","productDescription":"46 p.","startPage":"131","endPage":"176","ipdsId":"IP-134281","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":408613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vanstreels, Ralph","contributorId":298368,"corporation":false,"usgs":false,"family":"Vanstreels","given":"Ralph","email":"","affiliations":[{"id":64540,"text":"Institute of Research and Rehabilitation of Marine Animals","active":true,"usgs":false}],"preferred":false,"id":855456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uhart, Marcella","contributorId":298369,"corporation":false,"usgs":false,"family":"Uhart","given":"Marcella","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":855458,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}