Natural perchlorate (ClO4−) exists in many places on Earth, in lunar regolith, meteorites, and on the surface of Mars. Terrestrial natural ClO4− has widely variable Cl and O stable isotopic compositions (δ37Cl, δ18O, Δ17O). The δ18O and Δ17O values of ClO4− from the most hyper-arid locations co-vary. ClO4− from less arid areas has relatively little 17O excess and poor Δ17O-δ18O correlation. ClO4− from the Atacama Desert has unusually low δ37Cl (<−10‰) and exhibits a positive correlation between δ37Cl and δ18O, while the δ37Cl of ClO4− from all other locations varies between −5 and +7‰ with no δ37Cl-δ18O covariation. To evaluate the impact of different precursors (ClOx) and reaction pathways on the isotopic composition of ClO4−, we measured the isotopic composition of ClO4− produced in the laboratory by UV or O3 mediated aqueous oxidation of Cl−, OCl−, ClO2−, and ClO2° as well as O3 mediated oxidation of dry NaCl. ClOx oxidation in aqueous or dry systems enriched in O3 produced ClO4− with Δ17O values that generally increased with the number of O atoms required and included evidence that the site-specific 17O anomaly in O3 was preferentially transferred to ClO4−. Based on the inferred number of O atoms sourced from O3, and known Cl and O reaction pathways, it appears that ClO2° and ClO3* were required intermediates in the production of ClO4− in the O3 experiments. ClOx aqueous oxidation by UV irradiation produced ClO4− with a large range of δ18O values and little or no 17O anomaly. ClO3− was produced to a much greater extent than ClO4− in all experiments except dry oxidation of NaCl by O3. The isotopic composition of ClO3− was distinct from that of ClO4− produced from the same initial reactants. Combined results of O3 and UV mediated reactions largely bracketed the range of natural ClO4− δ18O and Δ17O values as well as δ37Cl values of non-Atacama natural samples, but no conditions produced the low δ37Cl values of Atacama ClO4−. Our results indicate that variation in production mechanisms, possibly combined with isotopically variable precursors, could be responsible for much of the observed isotopic variation in natural ClO4− and ClO3−.
Geobiology explores how Earth's system has changed over the course of geologic history and how living organisms on this planet are impacted by or are indeed causing these changes. For decades, geologists, paleontologists, and geochemists have generated data to investigate these topics. Foundational efforts in sedimentary geochemistry utilized spreadsheets for data storage and analysis, suitable for several thousand samples, but not practical or scalable for larger, more complex datasets. As results have accumulated, researchers have increasingly gravitated toward larger compilations and statistical tools. New data frameworks have become necessary to handle larger sample sets and encourage more sophisticated or even standardized statistical analyses.
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The theory of migratory coupling postulates that aggregations of migrant prey can induce large-scale synchronized movements in predators, and this coupling is consequential for the dynamics of ecological communities. The degree to which humans influence these interactions remains largely unknown. We tested whether creation of large resource pulses by humans such as seasonal herding of reindeer Rangifer tarandus and hunting of moose, Alces alces, can induce migratory coupling with Golden Eagles, Aquila chrysaetos, and whether these lead to demographic consequences for the eagles.
Fennoscandia.
We used movement data from 32 tracked Golden Eagles spanning 125 annual migratory cycles over 8 years. We obtained reindeer distribution data through collaboration with reindeer herders based on satellite tracking of reindeer, and moose harvest data from the national hunting statistics for Sweden. We assessed demographic consequences for eagles from ingesting lead from ammunition fragments in moose carcasses through survival estimates and their links with lead concentrations in eagles' blood.
In spring, eagles migrated hundreds of kilometres to be spatially and temporally coupled with calving reindeer, whereas in autumn, eagles matched their distribution with the location and timing of moose hunt. Juveniles were more likely to couple with reindeer calving, whereas adults were particularly drawn to areas of higher moose harvest. Due to this coupling, eagles ingested lead from spent ammunition in moose offal and carcasses and the resulting lead toxicity increased the risk of mortality by 3.4 times.
We show how migratory coupling connects landscape processes and that human actions can influence migratory coupling over large spatial scales and increase demographic risks for predators. We provide vital knowledge towards resolving human–wildlife conflicts and the conservation of protected species over a large spatial and temporal scale.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13373","usgsCitation":"Singh, N.J., Ecke, F., Katzner, T., Bagchi, S., Sandstrom, P., and Hornfeldt, B., 2021, Consequences of migratory coupling of predators and prey when mediated by human actions: Diversity and Distributions, v. 27, no. 9, p. 1848-1860, https://doi.org/10.1111/ddi.13373.","productDescription":"13 p.","startPage":"1848","endPage":"1860","ipdsId":"IP-074750","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":451636,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13373","text":"Publisher Index Page"},{"id":432753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Sweden","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[22.18317,65.72374],[21.21352,65.02601],[21.36963,64.41359],[19.77888,63.60955],[17.84778,62.7494],[17.11955,61.34117],[17.83135,60.63658],[18.78772,60.08191],[17.86922,58.95377],[16.82919,58.71983],[16.44771,57.04112],[15.87979,56.1043],[14.66668,56.20089],[14.10072,55.40778],[12.94291,55.36174],[12.6251,56.30708],[11.78794,57.44182],[11.02737,58.85615],[11.46827,59.43239],[12.30037,60.11793],[12.63115,61.29357],[11.99206,61.80036],[11.93057,63.12832],[12.57994,64.06622],[13.57192,64.04911],[13.91991,64.44542],[13.55569,64.78703],[15.10841,66.19387],[16.10871,67.30246],[16.76888,68.01394],[17.72918,68.01055],[17.99387,68.56739],[19.87856,68.40719],[20.02527,69.06514],[20.64559,69.10625],[21.97853,68.61685],[23.53947,67.93601],[23.56588,66.39605],[23.90338,66.00693],[22.18317,65.72374]]]},\"properties\":{\"name\":\"Sweden\"}}]}","volume":"27","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Singh, Navinder J.","contributorId":342307,"corporation":false,"usgs":false,"family":"Singh","given":"Navinder","email":"","middleInitial":"J.","affiliations":[{"id":81856,"text":"Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":909987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ecke, Fraucke","contributorId":342308,"corporation":false,"usgs":false,"family":"Ecke","given":"Fraucke","email":"","affiliations":[{"id":81856,"text":"Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":909988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":909989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagchi, Sumanta","contributorId":210387,"corporation":false,"usgs":false,"family":"Bagchi","given":"Sumanta","email":"","affiliations":[],"preferred":false,"id":909990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sandstrom, Per","contributorId":342309,"corporation":false,"usgs":false,"family":"Sandstrom","given":"Per","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":909991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hornfeldt, Birger","contributorId":342310,"corporation":false,"usgs":false,"family":"Hornfeldt","given":"Birger","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":909992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238936,"text":"70238936 - 2021 - Translational invasion ecology: Bridging research and practice to address one of the greatest threats to biodiversity","interactions":[],"lastModifiedDate":"2022-12-19T12:47:57.9032","indexId":"70238936","displayToPublicDate":"2021-07-05T06:41:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Translational invasion ecology: Bridging research and practice to address one of the greatest threats to biodiversity","docAbstract":"Effective natural resource management and policy is contingent on information generated by research. Conversely, the applicability of research depends on whether it is responsive to the needs and constraints of resource managers and policy makers. However, many scientific fields including invasion ecology suffer from a disconnect between research and practice. Despite strong socio-political imperatives, evidenced by extensive funding dedicated to addressing invasive species, the pairing of invasion ecology with stakeholder needs to support effective management and policy is lacking. As a potential solution, we propose translational invasion ecology (TIE). As an extension of translational ecology, as a framework to increase collaboration among scientists, practitioners, and policy makers to reduce negative impacts of invasive species. As an extension of translational ecology, TIE is an approach that embodies an intentional and inclusive process in which researchers, stakeholders, and decision makers collaborate to develop and implement ecological research via joint consideration of the ecological, sociological, economic, and/or political contexts in order to improve invasive species management. TIE ideally results in improved outcomes as well as shared benefits between researchers and managers. We delineate the steps of our proposed TIE approach and describe successful examples of ongoing TIE projects from the US and internationally. We suggest practical ways to begin incorporating TIE into research and management practices, including supporting boundary-spanning organizations and activities, expanding networks, sharing translational experiences, and measuring outcomes. We find that there is a need for strengthened boundary spanning, as well as funding and recognition for advancing translational approaches. As climate change and globalization exacerbate invasive species impacts, TIE provides a promising approach to generate actionable ecological research while improving outcomes of invasive species management and policy decisions.
Human-wildlife interactions (HWI) are driven fundamentally by overlapping space and resources. As competition intensifies, the likelihood of interaction and conflict increases. In turn, conflict may impede conservation efforts by lowering social tolerance of wildlife, especially when human-wildlife conflict (HWC) poses a threat to human safety and economic well-being. Thus, mitigating conflict is one of the most consequential components of a wildlife management program, particularly for large carnivores. However, unlike other large carnivores, the causative factors and conservation consequences of interactions between humans and polar bears (Ursus maritimus) are poorly understood. Historically, mitigation of human-polar bear conflict has been a low management priority with the exception of a few locations where conflict had been a chronic concern. In part, this was because of low human densities in most of the Arctic and sea ice act as a physical barrier regulating the frequency of human-polar bear interactions. However, as the Arctic has warmed, anthropogenic activities have increased, and polar bears have become more reliant on land. As a result, mitigating interaction and conflict between humans and polar bears has become a growing concern. In this chapter, we explore the nexus of polar bear and human behavior and environmental change in driving the nature and intensity of human-polar bear interaction and conflict. We first provide an overview of behaviors that contribute to the occurrence of interactions and conflicts. We then review historical and contemporary drivers of interaction and conflict and examine how climate-mediated changes to Arctic marine and terrestrial environments are likely to influence distribution and types of future incidents. We close by proposing a conceptual framework that conservationists and managers can use to mitigate the likelihood of future human-polar bear conflict in a rapidly changing Arctic.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ethology and behavioral ecology of sea otters and polar bears","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","usgsCitation":"Atwood, T.C., and Wilder, J., 2021, Human-polar bear interactions, chap. of Ethology and behavioral ecology of sea otters and polar bears, p. 325-353.","productDescription":"29 p.","startPage":"325","endPage":"353","ipdsId":"IP-112183","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":387142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387091,"type":{"id":15,"text":"Index Page"},"url":"https://www.springer.com/gp/book/9783030667955"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Davis, Randall W.","contributorId":131160,"corporation":false,"usgs":false,"family":"Davis","given":"Randall","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":819204,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":819205,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":819056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilder, James","contributorId":152610,"corporation":false,"usgs":false,"family":"Wilder","given":"James","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":819057,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225622,"text":"70225622 - 2021 - Sea otter foraging behavior","interactions":[],"lastModifiedDate":"2021-10-28T14:28:42.879694","indexId":"70225622","displayToPublicDate":"2021-07-04T09:24:30","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Sea otter foraging behavior","docAbstract":"Sea otters are marine specialists but diet generalists, which feed primarily on benthic mega-invertebrates (i.e., body dimension >1 cm). They locate and capture epibenthic and infaunal prey with their forepaws by relying on vision and tactile sensitivity during short-duration dives (generally <2 min) in shallow waters (routine dives <30 m and maximum dive depth ~100 m) of the littoral zone. Sea otters have an elevated resting metabolic rate and small or no energy reserves in the form of blubber, so they feed every 3–4 h. Foraging dives often occur in bouts (i.e., two or more consecutive dives), which may last several hours with 1–2 min between dives, depending on the type of prey. Sea otters consume small or soft prey entirely or use their teeth or stone tools to access the flesh of mega-invertebrates with a shell, test, or exoskeleton. The daily percentage of time that sea otters devote to foraging depends on age, sex, presence of a pup, time of year, and prey abundance, which varies geographically, seasonally, and episodically. In areas occupied by sea otters for many years, epifaunal prey generally decline first followed by infaunal species, and this may result in greater foraging effort and diet specialization associated with density-dependent competition for food. Although prey availability strongly influences sea otter carrying capacity, both intrinsic and extrinsic factors influence population equilibrium density, resulting in spatiotemporal variations in foraging behavior.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ethology and behavioral ecology of sea otters and polar bears","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66796-2_4","usgsCitation":"Davis, R.W., and Bodkin, J.L., 2021, Sea otter foraging behavior, chap. of Ethology and behavioral ecology of sea otters and polar bears, p. 57-81, https://doi.org/10.1007/978-3-030-66796-2_4.","productDescription":"25 p.","startPage":"57","endPage":"81","ipdsId":"IP-122462","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":451640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-030-66796-2_4","text":"Publisher Index Page"},{"id":391086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Randall W.","contributorId":131160,"corporation":false,"usgs":false,"family":"Davis","given":"Randall","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":825975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":825976,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221859,"text":"70221859 - 2021 - Evaluating corticosterone as a biomarker for amphibians exposed to increased salinity and ambient corticosterone","interactions":[],"lastModifiedDate":"2021-07-12T17:29:11.820999","indexId":"70221859","displayToPublicDate":"2021-07-03T12:25:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating corticosterone as a biomarker for amphibians exposed to increased salinity and ambient corticosterone","docAbstract":"Physiological biomarkers are commonly used to assess the health of taxa exposed to natural and anthropogenic stressors. Glucocorticoid (GC) hormones are often used as indicators of physiological stress in wildlife because they affect growth, reproduction and survival. Increased salinity from human activities negatively influences amphibians and their corticosterone (CORT; the main amphibian GC) physiology; therefore, CORT could be a useful biomarker. We evaluated whether waterborne CORT could serve as a biomarker of salt stress for three free-living amphibian species that vary in their sensitivity to salinity: boreal chorus frogs (Pseudacris maculata), northern leopard frogs (Rana pipiens) and barred tiger salamanders (Ambystoma mavortium). Across a gradient of contamination from energy-related saline wastewaters, we tested the effects of salinity on baseline and stress-induced waterborne CORT of larvae. Stress-induced, but not baseline, CORT of leopard frogs increased with increasing salinity. Salinity was not associated with baseline or stress-induced CORT of chorus frogs or tiger salamanders. Associations between CORT and salinity were also not related to species-specific sensitivities to salinity. However, we detected background environmental CORT (ambient CORT) in all wetlands and spatial variation was high within and among wetlands. Higher ambient CORT was associated with lower waterborne CORT of larvae in wetlands. Therefore, ambient CORT likely confounded associations between waterborne CORT and salinity in our analysis and possibly influenced physiology of larvae. We hypothesize that larvae may passively take up CORT from their environment and downregulate endogenous CORT. Although effects of some hormones (e.g. oestrogen) and endocrine disruptors on aquatic organisms are well described, studies investigating the occurrence and effects of ambient CORT are limited. We provide suggestions to improve collection methods, reduce variability and avoid confounding effects of ambient CORT. By making changes to methodology, waterborne CORT could still be a promising, non-invasive conservation tool to evaluate effects of salinity on amphibians.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/conphys/coab049","usgsCitation":"Tornabene, B., Hossack, B., Crespi, E.J., and Breuner, C., 2021, Evaluating corticosterone as a biomarker for amphibians exposed to increased salinity and ambient corticosterone: Conservation Physiology, v. 9, no. 1, coab049, 15 p., https://doi.org/10.1093/conphys/coab049.","productDescription":"coab049, 15 p.","ipdsId":"IP-127959","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":451642,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab049","text":"Publisher Index Page"},{"id":387129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.205078125,\n 47.45780853075031\n ],\n [\n -101.162109375,\n 47.45780853075031\n ],\n [\n -101.162109375,\n 48.951366470947725\n ],\n [\n -105.205078125,\n 48.951366470947725\n ],\n [\n -105.205078125,\n 47.45780853075031\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":819028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crespi, Erica J","contributorId":260876,"corporation":false,"usgs":false,"family":"Crespi","given":"Erica","email":"","middleInitial":"J","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":819030,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breuner, Creagh W","contributorId":241893,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh W","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":819031,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223170,"text":"70223170 - 2021 - The petrologic and degassing behavior of sulfur and other magmatic volatiles from the 2018 eruption of Kīlauea, Hawaiʻi: Melt concentrations, magma storage depths, and magma recycling","interactions":[],"lastModifiedDate":"2021-08-17T13:26:14.993263","indexId":"70223170","displayToPublicDate":"2021-07-03T08:23:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"The petrologic and degassing behavior of sulfur and other magmatic volatiles from the 2018 eruption of Kīlauea, Hawaiʻi: Melt concentrations, magma storage depths, and magma recycling","docAbstract":"Kīlauea Volcano’s 2018 lower East Rift Zone (LERZ) eruption produced exceptionally high lava effusion rates and record-setting SO2 emissions. The eruption involved a diverse range of magmas, including primitive basalts sourced from Kīlauea’s summit reservoirs. We analyzed LERZ matrix glasses, melt inclusions, and host minerals to identify melt volatile contents and magma storage depths. The LERZ glasses and melt inclusions span nearly the entire compositional range previously recognized at Kīlauea. Melt inclusions in Fo86-89 olivine from the main eruptive vent (fissure 8) underwent 70–170 °C cooling during transport in LERZ carrier melts, causing extensive post-entrapment crystallization and sulfide precipitation. Many of these melt inclusions have low sulfur (400–900 ppm) even after correction for sulfide formation. CO2 and H2O vapor saturation pressures indicate shallow melt inclusion trapping depths (1–5 km), consistent with formation within Kīlauea’s Halemaʻumaʻu and South Caldera reservoirs. Many of these inclusions also have degassed δ34S values (− 1.5 to − 0.5‰). Collectively, these results indicate that some primitive melts experienced near-surface degassing before being trapped into melt inclusions. We propose that decades-to-centuries of repeated lava lake activity and lava drain-back during eruptions (e.g., 1959 Kīlauea Iki) recycled substantial volumes of degassed magma into Kīlauea’s shallow reservoir system. Degassing and magma recycling from the 2008–2018 Halemaʻumaʻu lava lake likely reduced the volatile contents of LERZ fissure 8 magmas, resulting in lower fountain heights compared to many prior Kīlauea eruptions. The eruption’s extreme SO2 emissions were due to high lava effusion rates rather than particularly volatile-rich melts.
Mars exhibits diverse surface changes at all latitudes and all seasons. Active processes include impact cratering, aeolian sand and dust transport, a variety of slope processes, changes in polar ices, and diverse effects of seasonal CO2 frost. The extent of surface change has been surprising and indicates that the present climate is capable of reshaping the surface. Activity has important implications for the Amazonian history of Mars: understanding processes is a necessary step before we can understand their implications and variations over time.
The relentless role of invasive species in the extinction of native biota requires predictions of ecosystem vulnerability to inform proactive management strategies. The worldwide invasion and range expansion of predatory northern pike (Esox lucius) has been linked to the decline of native fishes and tools are needed to predict the vulnerability of habitats to invasion over broad geographic scales. To address this need, we coupled an intrinsic potential habitat modelling approach with a Bayesian network to evaluate the vulnerability of five culturally and economically vital species of Pacific salmon (Oncorhynchus spp.) to invasion by northern pike. This study was conducted along 22,875 stream km in the Southcentral region of Alaska, USA. Pink salmon (O. gorbuscha) were the most vulnerable species, with 15.2% (2,458 km) of their calculated extent identified as “highly” vulnerable, followed closely by chum salmon (O. keta, 14.8%; 2,557 km) and coho salmon (O. kisutch, 14.7%; 2,536 km). Moreover, all five Pacific salmon species were highly vulnerable in 1,001 stream km of shared habitat. This simple to implement, adaptable, and cost-effective framework will allow prioritizing habitats for early detection and monitoring of invading northern pike.
The strong increase of seismicity rates in the contiguous USA over the last 10 years is linked to the injection of huge amounts of wastewater from oil and gas production in unconventional hydrocarbon reservoirs. We calculated 549 moment tensors of induced earthquakes (MW ≤ 4.9) in southern Kansas to study their source mechanisms and their relation to injection activity. Seventeen percent of the events analyzed contained significant volumetric (ISO%) components, and these events mostly occurred near the two largest local earthquakes during the 4 months of largest active wastewater disposal. Mapping the local stress field, we determined that most of the region lies within a transtensional stress regime, with a maximum horizontal stress σHmax trending N75°E. In the epicentral area of the MW 4.9 Milan earthquake, the σHmax trend is rotated to about S80°E. Locally, two areas display a change in the stress field orientation with depth, from transtensional above 5.5 km depth to strike slip deeper in the basement. Relating the resolved fault geometries to the obtained local stress field orientation, we find that most of the activated fault planes were optimally oriented to the current stress field and thus small stress perturbations caused by the water injection could lead to failure.
Tracing produced water origins from wells hydraulically fractured with freshwater-based fluids is sometimes predicated on assumptions that (1) each geological formation contains compositionally unique brine and (2) produced water from recently hydraulically fractured wells resembles fresher meteoric water more so than produced water from older wells. These assumptions are not valid in Williston Basin oil wells sampled in this study. Although distinct average 228Ra/226Ra ratios were found in water produced from the Bakken and Three Forks Formations, average δ2H, δ18O, specific gravity, and conductivity were similar but exhibited significant variability across five oil fields within each formation. Furthermore, initial produced water (“flowback”) was operationally defined based on the presence of glycol ether compounds and water from wells that had produced <56% of the amount of fluids injected and sampled within 160 days of fracturing. Flowback unexpectedly exhibited higher temperature, specific gravity, conductivity, δ2H, and δ18O, but lower oxidation–reduction potential and δ11B, relative to the wells thought to be producing formation brines (from wells with a produced-to-injected water ratio [PIWR] > 0.84 and sampled more than 316 days after fracturing). As such, establishing an overall geochemical and isotopic signature of produced water compositions based solely on chemical similarity to meteoric water and formation without the consideration of well treatments, well completion depth, or lateral location across the basin could be misleading if these signatures are assumed to be applicable across the entire basin. These findings have implications for using produced water compositions to understand the interbasin fluid flow and trace sources of hydraulic fracturing fluids.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06789","usgsCitation":"Gallegos, T., Doolan, C.A., Caldwell, R.R., Engle, M.A., Varonka, M., Birdwell, J.E., Jolly, G.D., Coplen, T.B., and Oliver, T.A., 2021, Insights on geochemical, isotopic, and volumetric compositions of produced water from hydraulically fractured Williston Basin oil wells: Environmental Science and Technology, v. 55, no. 14, p. 10025-10034, https://doi.org/10.1021/acs.est.0c06789.","productDescription":"10 p.","startPage":"10025","endPage":"10034","ipdsId":"IP-118139","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":488266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06789","text":"Publisher Index Page"},{"id":386984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.18310546875,\n 47.30903424774781\n ],\n [\n -102.667236328125,\n 47.30903424774781\n ],\n [\n -102.667236328125,\n 48.31242790407178\n ],\n [\n -105.18310546875,\n 48.31242790407178\n ],\n [\n -105.18310546875,\n 47.30903424774781\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caldwell, Rodney R. 0000-0002-2588-715X caldwell@usgs.gov","orcid":"https://orcid.org/0000-0002-2588-715X","contributorId":2577,"corporation":false,"usgs":true,"family":"Caldwell","given":"Rodney","email":"caldwell@usgs.gov","middleInitial":"R.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":818720,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Varonka, Matthew S. 0000-0003-3620-5262","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":203231,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew S.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818721,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818748,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolly, Glenn D. 0000-0001-5876-5258 gdjolly@usgs.gov","orcid":"https://orcid.org/0000-0001-5876-5258","contributorId":260780,"corporation":false,"usgs":true,"family":"Jolly","given":"Glenn","email":"gdjolly@usgs.gov","middleInitial":"D.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":818722,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":818723,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oliver, Thomas A. 0000-0001-5994-2391 taoliver@usgs.gov","orcid":"https://orcid.org/0000-0001-5994-2391","contributorId":260781,"corporation":false,"usgs":true,"family":"Oliver","given":"Thomas","email":"taoliver@usgs.gov","middleInitial":"A.","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818724,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70222587,"text":"70222587 - 2021 - Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report","interactions":[],"lastModifiedDate":"2021-08-06T21:56:24.549864","indexId":"70222587","displayToPublicDate":"2021-07-01T16:45:02","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NCCN/NRR-2021/2284","title":"Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report","docAbstract":"In 2008, the U.S. Geological Survey (USGS) began collaborating with the National Park Service (NPS)-North Coast and Cascades Network (NCCN), the Muckleshoot Indian Tribe (MIT), Puyallup Tribe of Indians (PTOI), and Washington Department of Fish and Wildlife (WDFW) to develop a standard survey protocol for monitoring long-term changes in the abundance, distribution, and population composition of elk on key summer ranges within Mount Rainier National Park (MORA) and Olympic National Park (OLYM). In MORA, surveys were conducted in two trend count areas (TCAs) that correspond with primary summer ranges used by the North Rainier Herd, which winters outside the park to the North, and the South Rainier Herd, which winters outside the park primarily to the South. In OLYM, we defined five TCAs including an Olympic Core TCA (hereafter, Core TCA) that encompasses summer ranges on the flanks of Mount Olympus, and four TCAs that encompass other primary summer ranges throughout the park. \nThe standard protocol allows for estimating aerial survey detection biases and adjusting raw survey counts to account for elk that were likely present but not seen during surveys. Previously, we developed a suite of aerial-bias-correction models for use in estimating aerial detection biases and adjusting raw counts of elk in MORA based on sighting conditions related to elk group size, vegetation density, lighting conditions, elk movement, as well as combinations of these and other factors. The models were based on independent sighting records of elk groups by front-seat and back-seat observer pairs in a helicopter, including detection records of some radio-collared elk groups. \nHere, we analyze results of the first 10 years of elk monitoring in MORA (2008-2017) and 8 years in OLYM (2008-2015). In a previous report covering surveys conducted from 2008-2011, data were not sufficient to model detection biases of aerial surveys conducted in OLYM; hence, analyses of elk population trends were based on counts adjusted for detection biases in MORA, whereas trends in OLYM were based on raw, unadjusted counts (Griffin et al. 2013, Jenkins et al. 2015). \nOur objectives for the current summary were to:\n(1) incorporate additional data to update aerial-bias-correction models previously developed for use in MORA to include corrections for aerial detection bias in both MORA and OLYM,\n(2) examine trends in elk abundance, distribution, and population composition estimates for subalpine summer ranges within MORA and OLYM, and\n(3) estimate effects of seasonal variation and weather on elk abundance and population composition estimates for subalpine summer ranges in both parks.","language":"English","publisher":"National Park Service","usgsCitation":"Jenkins, K., Lubow, B., Happe, P.J., Braun, K., Boetsch, J., Baccus, W., Chestnut, T., Vales, D.J., Moeller, B.J., Tirhi, M., Holman, E., and Griffin, P.C., 2021, Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report: Natural Resource Report NPS/NCCN/NRR-2021/2284, xiii, 77 p.","productDescription":"xiii, 77 p.","ipdsId":"IP-123180","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":387745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387744,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/662550"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Ranier and Olympic National Parks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6680908203125,\n 46.592843997427416\n ],\n [\n -121.5582275390625,\n 46.69843486113957\n ],\n [\n -121.44287109374999,\n 46.694667307773116\n ],\n [\n -121.39892578125,\n 46.751153008636884\n ],\n [\n -121.53625488281249,\n 47.010225655683485\n ],\n [\n -121.9207763671875,\n 47.010225655683485\n ],\n [\n -121.8988037109375,\n 47.08508535995386\n ],\n [\n -122.16247558593751,\n 47.07386310181414\n ],\n [\n -122.31628417968749,\n 46.87145819560722\n ],\n [\n -122.03613281249999,\n 46.78501604269254\n ],\n [\n -121.9647216796875,\n 46.751153008636884\n ],\n [\n -121.9647216796875,\n 46.57774276255591\n ],\n [\n -121.6680908203125,\n 46.592843997427416\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.28857421875,\n 47.31648293428332\n ],\n [\n -122.904052734375,\n 47.70976154266637\n ],\n [\n -122.9754638671875,\n 47.989921667414194\n ],\n [\n -123.8818359375,\n 48.122101028190805\n ],\n [\n -124.32678222656249,\n 48.158757304569235\n ],\n [\n -124.4091796875,\n 47.912660308427654\n ],\n [\n -124.20043945312499,\n 47.76886840424207\n ],\n [\n -124.398193359375,\n 47.70606513569572\n ],\n [\n -124.112548828125,\n 47.506069781910846\n ],\n [\n -123.8323974609375,\n 47.47266286861342\n ],\n [\n -123.99719238281249,\n 47.342545069660225\n ],\n [\n -123.28857421875,\n 47.31648293428332\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Kurt 0000-0003-1415-6607","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":221472,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":820652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubow, B. C.","contributorId":64603,"corporation":false,"usgs":false,"family":"Lubow","given":"B. C.","affiliations":[],"preferred":false,"id":820667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Happe, P. J.","contributorId":219686,"corporation":false,"usgs":false,"family":"Happe","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":16133,"text":"National Park Service, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":820668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Braun, K.","contributorId":261796,"corporation":false,"usgs":false,"family":"Braun","given":"K.","email":"","affiliations":[],"preferred":false,"id":820669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boetsch, J.","contributorId":213934,"corporation":false,"usgs":false,"family":"Boetsch","given":"J.","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":820670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baccus, W.","contributorId":261797,"corporation":false,"usgs":false,"family":"Baccus","given":"W.","affiliations":[],"preferred":false,"id":820671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chestnut, T.","contributorId":261798,"corporation":false,"usgs":false,"family":"Chestnut","given":"T.","email":"","affiliations":[],"preferred":false,"id":820672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vales, D. J.","contributorId":261799,"corporation":false,"usgs":false,"family":"Vales","given":"D.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820673,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moeller, B. J.","contributorId":261800,"corporation":false,"usgs":false,"family":"Moeller","given":"B.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820674,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tirhi, M.","contributorId":261801,"corporation":false,"usgs":false,"family":"Tirhi","given":"M.","affiliations":[],"preferred":false,"id":820675,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Holman, E.","contributorId":261802,"corporation":false,"usgs":false,"family":"Holman","given":"E.","email":"","affiliations":[],"preferred":false,"id":820676,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Griffin, P. C.","contributorId":69499,"corporation":false,"usgs":false,"family":"Griffin","given":"P.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":820677,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221862,"text":"70221862 - 2021 - What is the effect of poaching activity on wildlife species?","interactions":[],"lastModifiedDate":"2021-10-06T15:13:10.834057","indexId":"70221862","displayToPublicDate":"2021-07-01T12:04:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"What is the effect of poaching activity on wildlife species?","docAbstract":"Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of 5 mammal species. Specifically, we focused on 2 species of conservation concern (mountain gorilla (Gorilla beringei beringei) and golden money (Cercopithecus mitis kandti)), and 3 species targeted by poachers (black-fronted duiker (Cephalophus nigrifrons), bushbuck (Tragelaphus scriptus), and African buffalo (Syncerus caffer)). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics (this paper) can be combined with information on the relationship between ranger patrols and poaching activity (Moore et al. 2017) to develop models useful for making wise decisions about ranger patrol deployment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2397","usgsCitation":"Moore, J.F., Uzabaho, E., Musana, A., Uwingell, P., Hines, J.E., and Nichols, J.D., 2021, What is the effect of poaching activity on wildlife species?: Ecological Applications, v. 31, no. 7, e02397, 12 p., https://doi.org/10.1002/eap.2397.","productDescription":"e02397, 12 p.","ipdsId":"IP-118381","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","otherGeospatial":"Volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 29.702911376953125,\n -1.3587440869100178\n ],\n [\n 29.663772583007812,\n -1.3882613601346867\n ],\n [\n 29.63081359863281,\n -1.3951257897508238\n ],\n [\n 29.59304809570312,\n -1.3882613601346867\n ],\n [\n 29.561462402343746,\n -1.384829137846475\n ],\n [\n 29.50721740722656,\n -1.4218968729661605\n ],\n [\n 29.49073791503906,\n -1.4383712317629698\n ],\n [\n 29.4927978515625,\n -1.4630825465188169\n ],\n [\n 29.480438232421875,\n -1.4692603328543323\n ],\n [\n 29.485244750976562,\n -1.4788701887242113\n ],\n [\n 29.461898803710938,\n -1.491912069367617\n ],\n [\n 29.439926147460934,\n -1.5269188384985064\n ],\n [\n 29.39804077148437,\n -1.5324100450044358\n ],\n [\n 29.442672729492188,\n -1.568788930117857\n ],\n [\n 29.481124877929688,\n -1.5660433757691457\n ],\n [\n 29.514770507812496,\n -1.5358420419244077\n ],\n [\n 29.519577026367188,\n -1.4891664166873633\n ],\n [\n 29.50996398925781,\n -1.4493540716333067\n ],\n [\n 29.540176391601562,\n -1.422583306939631\n ],\n [\n 29.54635620117188,\n -1.4184647000387454\n ],\n [\n 29.560775756835934,\n -1.408854588797322\n ],\n [\n 29.58549499511719,\n -1.4177782648419572\n ],\n [\n 29.641799926757812,\n -1.4232697407088846\n ],\n [\n 29.671325683593754,\n -1.412973212770802\n ],\n [\n 29.69467163085938,\n -1.4157189580307432\n ],\n [\n 29.70497131347656,\n -1.3875749160752702\n ],\n [\n 29.702911376953125,\n -1.3587440869100178\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":819048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uzabaho, Eustrate","contributorId":260880,"corporation":false,"usgs":false,"family":"Uzabaho","given":"Eustrate","email":"","affiliations":[{"id":52699,"text":"Intl. Gorilla Conservation Programme, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musana, Abel","contributorId":260881,"corporation":false,"usgs":false,"family":"Musana","given":"Abel","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uwingell, Prosper","contributorId":260882,"corporation":false,"usgs":false,"family":"Uwingell","given":"Prosper","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224605,"text":"70224605 - 2021 - Wetlands","interactions":[],"lastModifiedDate":"2021-09-29T17:05:36.934197","indexId":"70224605","displayToPublicDate":"2021-07-01T11:54:58","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wetlands","docAbstract":"During the last decades, soil organic carbon (SOC) attracted the attention of a much wider array of specialists beyond agriculture and soil science, as it was proven to be one of the most crucial components of the earth’s climate system, which has a great potential to be managed by humans. Soils as a carbon pool are one of the key factors in several Sustainable Development Goals, in particular Goal 15, “Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification and halt and reverse land degradation and halt biodiversity loss” with the SOC stock being explicitly cited in Indicator 15.3.1.
This technical manual is the first attempt to gather, in a standardized format, the existing data on the impacts of the main soil management practices on SOC content in a wide array of environments, including the advantages, drawbacks, and constraints. This manual presents different sustainable soil management (SSM) practices at different scales and in different contexts, supported by case studies that have been shown with quantitative data to have a positive effect on SOC stocks and successful experiences of SOC sequestration in practical field applications
Volume 2 includes a description of hot spots of SOC stocks. This manual defines hot spots of SOC as areas that represent a proportionally little of the global land surface but on which SOC storage is highly effective; bright spots as large land areas with low SOC stocks per km2 that represent a potential for further carbon sequestration.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Recarbonizing global soils – A technical manual of recommended management practices","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Food and Agriculture Organization of the United Nations","usgsCitation":"Tangen, B., and Bansal, S., 2021, Wetlands, 19 p.","productDescription":"19 p.","startPage":"36","endPage":"54","ipdsId":"IP-121703","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":389967,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fao.org/documents/card/en/c/cb6378en/"},{"id":389968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":824243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":824244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237360,"text":"70237360 - 2021 - Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","interactions":[],"lastModifiedDate":"2022-10-11T16:24:49.683141","indexId":"70237360","displayToPublicDate":"2021-07-01T11:21:09","publicationYear":"2021","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":"Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","docAbstract":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR029579","usgsCitation":"Willard, J., Read, J., Appling, A.P., Oliver, S.K., Jia, X., and Kumar, V., 2021, Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning: Water Resources Research, v. 57, no. 7, e2021WR029579, 20 p., https://doi.org/10.1029/2021WR029579.","productDescription":"e2021WR029579, 20 p.","ipdsId":"IP-119147","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451661,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr029579","text":"Publisher Index Page"},{"id":436285,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I00WFR","text":"USGS data release","linkHelpText":"Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)"},{"id":408165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854266,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248236,"text":"70248236 - 2021 - Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","interactions":[],"lastModifiedDate":"2023-09-05T15:18:59.650382","indexId":"70248236","displayToPublicDate":"2021-07-01T10:09:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","title":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","docAbstract":"The Queen Charlotte-Fairweather Fault (QC-FF) system off the coast of British Columbia and southeast Alaska is a highly active dextral strike-slip plate boundary that accommodates ∼50 mm/yr of relative motion between the Pacific and North America plates. Nine MW ≥ 6.7 earthquakes have occurred along the QC-FF system since 1910, including a MS(G-R)8.1 event in 1949. Two recent earthquakes, the October 28, 2012 Haida Gwaii (MW7.8) and January 5, 2013 Craig, Alaska (MW7.5) events, produced postseismic transient deformation that was recorded in the motions of 25 nearby continuous Global Positioning System (cGPS) stations. Here, we use 5+ yr of cGPS measurements to characterize the underlying mechanisms of postseismic deformation and to constrain the viscosity structure of the upper mantle surrounding the QC-FF. We construct forward models of viscoelastic deformation driven by coseismic stress changes from these two earthquakes and explore a large set of laterally heterogeneous viscosity structures that incorporate a relatively weak back-arc domain; we then evaluate each model based on its fit to the postseismic signals in our cGPS data. In determining best-fit model structures, we additionally incorporate the effects of afterslip following the 2012 event. Our results indicate the occurrence of a combination of temporally decaying afterslip and vigorous viscoelastic relaxation of the mantle asthenosphere. In addition, our best-fit viscosity structure (transient viscosity of 1.4–2.0 × 1018 Pa s; steady-state viscosity of 1019 Pa s) is consistent with the range of upper mantle viscosities determined in previous studies of glacial isostatic rebound and postseismic deformation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB021891","usgsCitation":"Guns, K.A., Pollitz, F., Lay, T., and Yue, H., 2021, Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure: Journal of Geophysical Research B: Solid Earth, v. 126, no. 7, e2021JB021891, 20 p., https://doi.org/10.1029/2021JB021891.","productDescription":"e2021JB021891, 20 p.","ipdsId":"IP-127502","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":420483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","city":"Craig","otherGeospatial":"Haida Gwaii Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.04582537099918,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.778120902444044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.70446739512911,\n 53.158925565323756\n ],\n [\n -132.34949768033022,\n 52.76369069159804\n ],\n [\n -131.7793948050469,\n 52.469804491078975\n ],\n [\n -131.66107156678072,\n 52.29252484773727\n ],\n [\n -130.97264545323122,\n 51.88274035718925\n ],\n [\n -130.75751229274715,\n 51.94245878634544\n ],\n [\n -131.7471248309743,\n 53.33271351252088\n ],\n [\n -132.30647104823325,\n 53.16537474581318\n ],\n [\n -132.70446739512911,\n 53.158925565323756\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Guns, Katherine A.","contributorId":329359,"corporation":false,"usgs":false,"family":"Guns","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":16619,"text":"UCSD","active":true,"usgs":false}],"preferred":false,"id":882060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":882061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":328838,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":882062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yue, Han","contributorId":329362,"corporation":false,"usgs":false,"family":"Yue","given":"Han","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":882063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223240,"text":"70223240 - 2021 - National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","interactions":[],"lastModifiedDate":"2021-08-19T15:13:17.0885","indexId":"70223240","displayToPublicDate":"2021-07-01T10:01:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2021/2285","title":"National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC.
To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type.
A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines.
Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.
A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha).
A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval.
The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286888","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., White, R., Pyne, M., Govus, T., and Dieck, J., 2021, National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project: Natural Resource Report 2021/2285, 220 p., https://doi.org/10.36967/nrr-2286888.","productDescription":"220 p.","ipdsId":"IP-120204","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.023681640625,\n 35.594785665487244\n ],\n [\n -83.09783935546875,\n 35.806676609227054\n ],\n [\n -83.40545654296875,\n 35.762114795721\n ],\n [\n -83.88336181640625,\n 35.68853320738875\n ],\n [\n -84.034423828125,\n 35.545635932499415\n ],\n [\n -83.90808105468749,\n 35.43605776486772\n ],\n [\n -83.5565185546875,\n 35.39800594715108\n ],\n [\n -83.30657958984375,\n 35.47409160773029\n ],\n [\n -83.023681640625,\n 35.594785665487244\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":821498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":821499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Govus, Tom","contributorId":264417,"corporation":false,"usgs":false,"family":"Govus","given":"Tom","email":"","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":821500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229733,"text":"70229733 - 2021 - Context-dependent deep learning","interactions":[],"lastModifiedDate":"2022-05-03T15:07:59.274845","indexId":"70229733","displayToPublicDate":"2021-07-01T09:59:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10746,"text":"Modeling and Using Context","active":true,"publicationSubtype":{"id":10}},"title":"Context-dependent deep learning","docAbstract":"Explicitly representing an agent’s context has been shown to have many benefits, which should also apply to machine learning. In this paper, we describe an approach to do this called context-dependent deep learning (CDDL), which is based on earlier work in context-mediated behavior (CMB) that uses contextual schemas (c-schemas) to represent clas-ses of situations along with knowledge useful in them. These are then recalled, and they guide reasoning in the corre-sponding contexts. CDDL stores knowledge about deep neural network structure and weights in c-schemas, which al-lows context-specific learning. Our work is being developed in the domain of seabird detection in aerial images of islands for use by biologists.
","language":"English","publisher":"ISTE OpenScience","doi":"10.21494/ISTE.OP.2021.0690","usgsCitation":"Turner, R.M., Loftin, C., Revello, A., Kline, L.R., Lewis, M., and Yasai-Sekeh, S., 2021, Context-dependent deep learning: Modeling and Using Context, v. 4, 7 p., https://doi.org/10.21494/ISTE.OP.2021.0690.","productDescription":"7 p.","ipdsId":"IP-129640","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21494/iste.op.2021.0690","text":"Publisher Index Page"},{"id":400059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Turner, Roy M.","contributorId":288598,"corporation":false,"usgs":false,"family":"Turner","given":"Roy","email":"","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Revello, Alex","contributorId":288599,"corporation":false,"usgs":false,"family":"Revello","given":"Alex","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kline, Logan R.","contributorId":288606,"corporation":false,"usgs":false,"family":"Kline","given":"Logan","email":"","middleInitial":"R.","affiliations":[{"id":61812,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Meredith","contributorId":288607,"corporation":false,"usgs":false,"family":"Lewis","given":"Meredith","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yasai-Sekeh, Salimeh","contributorId":288602,"corporation":false,"usgs":false,"family":"Yasai-Sekeh","given":"Salimeh","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838132,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223213,"text":"70223213 - 2021 - Alaska Landbird Conservation Plan","interactions":[],"lastModifiedDate":"2021-09-08T14:58:22.299368","indexId":"70223213","displayToPublicDate":"2021-07-01T09:57:54","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Alaska Landbird Conservation Plan","docAbstract":"Alaska is a land of extremes. The diversity of its avifauna reflects the heterogeneity of its landscape, with more than 500 species of birds recorded in the state. Species inhabiting primarily terrestrial habitats, known collectively as landbirds, constitute the largest and most ecologically diverse component of the Alaska avifauna. Habitats used by landbirds range from temperate rainforests in southeastern Alaska to Arctic tundra across much of northern Alaska. Most of these landbird species are migratory, and four major global migration flyways converge on rich breeding areas in Alaska.\n\nAlaska has one endemic landbird species, the McKay’s Bunting, and is home to an impressive number of landbird populations for which it hosts a large proportion of the regional, continental, or global population. Thus, Alaska has a significant stewardship responsibility for these particular landbird species and subspecies.\n\nHabitats in Alaska remain largely pristine due to the region’s remote nature, vast size, and small human population. Alaska’s growing population and attendant economic development, however, present many challenges that could affect all wildlife, including landbirds. Threats in Alaska are often considered to be less significant than those occurring elsewhere, where habitats are being altered by more rapidly increasing anthropogenic pressures, but they carry far-reaching consequences nonetheless. Habitats and ecosystem dynamics are changing rapidly due to the magnitude of climate warming at high latitudes. As such, effective landbird conservation in Alaska requires a broad, landscape-scale approach.\n\nConservation of landbirds over such an extensive and diverse landscape also demands that we integrate efforts in habitat management, population monitoring, research, education, and outreach at local, regional, continental, and international scales. Information on distribution and habitat requirements of landbirds should be incorporated into land-use planning decisions. Synthesizing information on distribution and population trends of landbirds is a critical, time-sensitive task. Such information should be provided in a form that is readily available to land managers and policy decision-makers.\n\nThe primary objectives of this plan are to (1) describe the region and Alaska’s landbird avifauna; (2) identify species of concern, important habitats, and key information needs; (3) highlight major conservation issues and threats to landbirds; and (4) identify potential conservation actions. We first examine landbird conservation at the statewide level within broad perspectives (regional, continental, and global), then take a detailed look at the specific issues, information needs, and potential conservation actions within each Bird Conservation Region in Alaska.","language":"English","publisher":"Boreal Partners in Flight","usgsCitation":"Booms, T.L., Cady, M.N., Carrothers, C.A., DeCicco, L.H., de Zeeuw, M.L., Flamme, M.J., Hagelin, J., Handel, C.M., Johnson, J., Kirchoff, M., Kissling, M.L., Lewis, S.B., Matsuoka, S.M., Nigro, D.A., Perkins, D.E., Renner, H.M., Savage, S.E., Sowl, K.M., Sharbaugh, S.M., Stenhouse, I.J., and Van Hemert, C.R., 2021, Alaska Landbird Conservation Plan (Version 2.0), x, 146 p.","productDescription":"x, 146 p.","ipdsId":"IP-070079","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":388944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388079,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/centers/asc/science/boreal-partners-flight"}],"country":"United 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However, classification decisions are liable to two errors: i) failing to classify a species as threatened or endangered that should be classified (underprotection), or ii) classifying a species as threatened or endangered when it is not warranted (overprotection). Recent surveys indicate threatened spectacled eider populations are increasing in western Alaska, prompting the U.S. Fish and Wildlife Service to reconsider the federal listing status. There are multiple criteria set for assessing spectacled eider status, and here we focus on the abundance and decision analysis criteria. We estimated population metrics using state-space models for Alaskan breeding populations of spectacled eiders. We projected abundance over 50 years using posterior estimates of abundance and process variation to estimate the probability of quasi-extinction. The decision analysis maps the risk of quasi-extinction to the loss associated with making a misclassification error (i.e., underprotection) through a loss function. Our results indicate that the Yukon Kuskokwim Delta breeding population in western Alaska has met the recovery criteria but the Arctic Coastal Plain population in northern Alaska has not. The methods employed here provide an example of accounting for uncertainty and incorporating value judgements in such a way that the decision-makers may understand the risk of committing a misclassification error. Incorporating the abundance threshold and decision analysis in the reclassification criteria greatly increases the transparency and defensibility of the classification decision, a critical aspect for making effective decisions about species management and conservation.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0253895","usgsCitation":"Dunham, K., Osnas, E., Frost, C., Fischer, J., and Grand, J.B., 2021, Assessing recovery of spectacled eiders using a Bayesian decision analysis: PLoS ONE, v. 16, no. 7, e0253895, 17 p., https://doi.org/10.1371/journal.pone.0253895.","productDescription":"e0253895, 17 p.","ipdsId":"IP-118460","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451667,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0253895","text":"Publisher Index Page"},{"id":432285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, K.D.","contributorId":287550,"corporation":false,"usgs":false,"family":"Dunham","given":"K.D.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osnas, E.E.","contributorId":341825,"corporation":false,"usgs":false,"family":"Osnas","given":"E.E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frost, C.","contributorId":336910,"corporation":false,"usgs":false,"family":"Frost","given":"C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":908940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fischer, J.B.","contributorId":341826,"corporation":false,"usgs":false,"family":"Fischer","given":"J.B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908942,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237231,"text":"70237231 - 2021 - Knowledge synthesis of Cape Sable Seaside Sparrow science","interactions":[],"lastModifiedDate":"2022-10-05T14:55:48.03111","indexId":"70237231","displayToPublicDate":"2021-07-01T09:42:25","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"IAA 4500126696","title":"Knowledge synthesis of Cape Sable Seaside Sparrow science","docAbstract":"This report represents a literature review of science conducted on the Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis, hereafter “CSSS” or “spar-row”). This information can be used as a foundation for the upcoming Species Status Assessment and for updating the CSSS Recovery Plan. This report focuses on areas of interest relative to CSSS management such as habitat, hydrology, fire, and population estimates. We include peer-reviewed scientific literature, synthesis reports, and some field reports. Many field reports to funding agencies and other documents exist beyond what is reviewed here; however, we include the most relevant documents relative to the focus of CSSS management. All documents reviewed are listed in the References section.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Benscoter, A., Haider, S., Guilbeau, K.G., and Romanach, S., 2021, Knowledge synthesis of Cape Sable Seaside Sparrow science, v, 62 p.","productDescription":"v, 62 p.","ipdsId":"IP-132903","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":407962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407930,"type":{"id":15,"text":"Index Page"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/139083"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.65,\n 25\n ],\n [\n -80.6,\n 25\n ],\n [\n -80.6,\n 26\n ],\n [\n -81.65,\n 26\n ],\n [\n -81.65,\n 25\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benscoter, Allison 0000-0003-4205-3808","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":216194,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haider, Saira M. 0000-0001-9306-3454","orcid":"https://orcid.org/0000-0001-9306-3454","contributorId":206253,"corporation":false,"usgs":true,"family":"Haider","given":"Saira","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guilbeau, Kelly G.","contributorId":297126,"corporation":false,"usgs":false,"family":"Guilbeau","given":"Kelly","email":"","middleInitial":"G.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220093,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853681,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216757,"text":"70216757 - 2021 - Economic geology and environmental characteristics of antimony deposits","interactions":[],"lastModifiedDate":"2021-10-01T14:40:56.858064","indexId":"70216757","displayToPublicDate":"2021-07-01T09:40:27","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Economic geology and environmental characteristics of antimony deposits","docAbstract":"Antimony is commonly listed as a critical mineral, particularly in the United States and European Union [1]. Its criticality, or supply risk, is derived from a combination of economic vulnerability, disruption potential of supply, and trade exposure [2].Disruption potential relates a country’s ability and willingness to supply a commodity. Commodities for which supply is concentrated in the fewest countries have the greatest potential for supply disruption, trade exposure, and economic vulnerability. In 2018, approximately 61% of the world production of antimony was mined from China, followed by Russia (20%) and Tajikistan (10%) [3] (Fig. 3.1). Several reviews of antimony as a critical mineral have been recently published [4, 5].
The uses of antimony can be divided into three main categories: metal products, non-metal uses, and flame retardants [5]. Most metallic antimony use is inlead-acid batteries. Antimony trioxide (Sb2O3) combined with halogenated com-pounds is used as a fire retardant in plastics, fabrics, and other applications. Other non-metallic uses include as a catalyst for plastics, and in the glass industry. Emerging uses include data storage and novel photovoltaic cells. Recycling of batteries represents an important reuse of antimony, but other uses of antimony do not lend themselves to recycling.
This chapter describes mineral deposit types that are primary sources of antimony, and environmental effects related to their mining. Antimony can be re-covered as either a primary commodity from some deposits or as a by product commodity from some gold or silver deposits. Environmental risks associated with antimony mining include those related to mining in general, such as the acid-generating potential of solid mine waste, and some issues specific to antimony, as described below.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Antimony","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"De Gruyter","doi":"10.1515/9783110668711-003","usgsCitation":"Seal,, R., 2021, Economic geology and environmental characteristics of antimony deposits, chap. 3 of Antimony, p. 49-72, https://doi.org/10.1515/9783110668711-003.","productDescription":"24 p.","startPage":"49","endPage":"72","ipdsId":"IP-123217","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":390119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":806092,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222561,"text":"70222561 - 2021 - Climate change scenario planning for resource stewardship at Wind Cave National Park","interactions":[],"lastModifiedDate":"2021-08-05T14:49:49.415903","indexId":"70222561","displayToPublicDate":"2021-07-01T09:38:32","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/NRR—2021/2274","title":"Climate change scenario planning for resource stewardship at Wind Cave National Park","docAbstract":"This report explains scenario planning as a climate change adaptation tool in general, then describes how it was applied to Wind Cave National Park as the second part of a pilot project to dovetail climate change scenario planning with National Park Service (NPS) Resource Stewardship Strategy development.
In the orientation phase, Park and regional NPS staff, other subject-matter experts, natural and cultural resource planners, and the climate change core team who led the scenario planning project identified priority resource management topics and associated climate sensitivities. Next, the climate change core team used this information to create a set of four divergent climate futures—summaries of relevant climate data from individual climate projections—to encompass the range of ways climate could change in coming decades in the park. Participants in the scenario planning workshop then developed climate futures into robust climate-resource scenarios that considered expert-elicited resource impacts and identified potential management responses. Finally, the scenario-based resource responses identified by park staff and subject matter experts were used to integrate climate-informed adaptations into resource stewardship goals and activities for the park's Resource Stewardship Strategy. This process of engaging resource managers in climate change scenario planning ensures that their management and planning decisions are informed by assessments of critical future climate uncertainties.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286672","usgsCitation":"Runyon, A., Schuurman, G.W., Miller, B.W., Symstad, A., and Hardy, A., 2021, Climate change scenario planning for resource stewardship at Wind Cave National Park: Natural Resource Report NPS/NRSS/NRR—2021/2274, ix, 114 p., https://doi.org/10.36967/nrr-2286672.","productDescription":"ix, 114 p.","ipdsId":"IP-122369","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":387718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.49327087402344,\n 43.54008705264584\n ],\n [\n -103.41293334960938,\n 43.54008705264584\n ],\n [\n -103.41293334960938,\n 43.61544814581711\n ],\n [\n -103.49327087402344,\n 43.61544814581711\n ],\n [\n -103.49327087402344,\n 43.54008705264584\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Runyon, Amber N. 0000-0002-7282-1217","orcid":"https://orcid.org/0000-0002-7282-1217","contributorId":261745,"corporation":false,"usgs":false,"family":"Runyon","given":"Amber N.","affiliations":[{"id":52985,"text":"National Park Service Climate Change Response Program","active":true,"usgs":false}],"preferred":false,"id":820555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":820556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":820557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":820558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hardy, Amanda","contributorId":261746,"corporation":false,"usgs":false,"family":"Hardy","given":"Amanda","email":"","affiliations":[{"id":52985,"text":"National Park Service Climate Change Response Program","active":true,"usgs":false}],"preferred":false,"id":820559,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221875,"text":"70221875 - 2021 - A tale of two valleys: Endangered species policy and the fate of the giant gartersnake","interactions":[],"lastModifiedDate":"2021-07-13T09:58:24.914439","indexId":"70221875","displayToPublicDate":"2021-07-01T09:28:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8958,"text":"California Fish and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"A tale of two valleys: Endangered species policy and the fate of the giant gartersnake","docAbstract":"By the mid-20th Century, giant gartersnakes (Thamnophis gigas) had lost more than 90% of their Central Valley marsh habitat and were extirpated from more than two-thirds of their range. This massive habitat loss led to their inclusion in the inaugural list of rare species under the California Endangered Species Act (CESA). Listing under the CESA provided giant gartersnakes legal protection and mechanisms for recovery, and subsequent listing under the U.S. Endangered Species Act (U.S. ESA) further fortified these protections. But how effective has listing under these endangered species acts (ESAs) been at achieving their goal of giant gartersnake recovery? Herein, we review relevant aspects of giant gartersnake ecology, illustrate how listing has benefitted giant gartersnakes and what challenges have been faced in slowing declines and recovering populations, and chart a course towardprovide options for improved conservation, management, and recovery of giant gartersnakes. Although listing as threatened under both state and federal ESAs has not yet achieved recovery of giant gartersnakes, the increased knowledge gained and mechanisms for protecting giant gartersnake habitat on private and public lands developed over the past 50 years has improved conservation of this endemic California snake.","language":"English","publisher":"California Department of Fish and Wildlife","doi":"10.51492/cfwj.cesasi.16","usgsCitation":"Halstead, B., Valcarcel, P., Kim, R., Jordan, A., Rose, J.P., Skalos, S., Reyes, G., Ersan, J., Casazza, M.L., Essert, A., and Fulton, A.M., 2021, A tale of two valleys: Endangered species policy and the fate of the giant gartersnake: California Fish and Wildlife, p. 264-283, https://doi.org/10.51492/cfwj.cesasi.16.","productDescription":"20 p.","startPage":"264","endPage":"283","ipdsId":"IP-123451","costCenters":[{"id":651,"text":"Western Ecological Research 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aessert@usgs.gov","orcid":"https://orcid.org/0000-0003-4408-5934","contributorId":199341,"corporation":false,"usgs":true,"family":"Essert","given":"Allison","email":"aessert@usgs.gov","affiliations":[],"preferred":true,"id":819164,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fulton, Alexandria M","contributorId":260937,"corporation":false,"usgs":false,"family":"Fulton","given":"Alexandria","email":"","middleInitial":"M","affiliations":[{"id":39913,"text":"former WERC","active":true,"usgs":false}],"preferred":false,"id":819165,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ]}