To track the illegal pangolin trade from Africa to Asia, we analyzed 1800 DNA samples from 30 seizures of African pangolin scales in Hong Kong during the period 2012–2016. We concluded that all four African pangolin species were present in trade, and that the white-bellied pangolin (Phataginus tricuspis) appeared most frequently (88.5%) in our samples. All six previously described phylogeographic lineages originating from the entire distribution range of P. tricuspis were found in the seizures, and the western central African lineage alone accounted for 67.1% of the samples of this species. Confirmed by modelling data, high DNA haplotype richness was present in most of the pangolin scale seizures, including those contained in small air parcels and large-volume sea shipments. Results suggest that African pangolins were hunted across large areas of their natural range and then delivered to a small number of trade transit hubs. Our study illustrates the utility of genetic analysis for characterizing the illegal pangolin trade and identifying the geographic origin of poaching hotspots.
3‐Trifluoromethyl‐4‐nitrophenol (TFM) has been used for more than 60 yr to control the invasive parasitic sea lamprey (Petromyzon marinus ) in the Great Lakes Basin (USA/Canada). In the early 1990s, researchers reported that TFM induced vitellogenin in fish and that TFM was an agonist for the rainbow trout estrogen receptor. To support continued registration of TFM for sea lamprey control, regulatory agencies required further testing to evaluate potential endocrine disruption effects. Fathead minnow (Pimephales promelas ) were exposed to TFM at measured concentrations of 0.0659, 0.181, 0.594, 1.79, and 5.11 mg active ingredient (a.i.)/L for 21 d. No‐observable‐ and lowest‐observable‐effect concentrations (NOEC and LOEC, respectively) were determined to be 1.79 mg/L or greater for each endpoint. Male survival in the highest treatment group was reduced relative to the controls. Percentage of egg fertility was reduced in the highest treatment group, resulting in an estimated NOEC of 1.79 mg/L. Whereas no effect on the gonadosomatic index (GSI) was observed for males, female GSI was increased in the 5.11‐mg/L treatment. Vitellogenin production was not altered relative to the controls for all TFM treatment groups. However, female testosterone was elevated in the 5.11‐mg/L treatment. The results suggest that prolonged exposure to TFM at concentrations exceeding 1.79 mg/L has the potential to disrupt endocrine function. Biologically relevant effects were found at the highest exposure concentration following a 21‐d exposure. However, the duration of exposure in our study is not consistent with typical treatment durations (12 h) for sea lamprey control. Environ Toxicol Chem 2020;39:1599–1607. © 2020 SETAC
Sedimentation and turbidity have effects on habitat suitability in the San Francisco Bay‐Delta (Bay‐Delta), concerning key species in the bay as well as the ability of the delta marshes to keep pace with sea level rise. A daily rainfall runoff and transport model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the Bay‐Delta for the next century (water years, WY2010–2099). The model was calibrated to historical streamflow and sediment data and applied using 10 Global Climate Models with two representative concentration pathways (RCP) each for WY1980–2099 from the IPCC 5th Assessment Report. Results indicate average increases in peak streamflow of +58% and +66% for the RCP 4.5 and 8.5 ensembles, respectively, by mid‐century and +62 and +96% by end‐of‐century. Sediment loads increased by +39% and +69% by end‐of‐century. Suspended sediment concentrations (SSC) increased on average by +4.6% and +6.7% for RCP 4.5 and 8.5, respectively, by end‐of‐century. Individual scenario results varied, and statistically significant increasing trends of sediment loads to the Bay‐Delta were found for the RCP 4.5 and 8.5 ensembles and five individual scenarios. Increased suspended sediment loads may have negative effects such as contaminant transport but also have positive effects that help protect against sea level rise, increase turbidity and fish habitat, and sustain wetland habitats in the Bay‐Delta.
No abstract available.
","language":"English","publisher":"AAAS","doi":"10.1126/science.abb4120","usgsCitation":"Major, J.J., 2020, Mount St. Helens at 40: Science, v. 368, no. 6492, p. 704-705, https://doi.org/10.1126/science.abb4120.","productDescription":"2 p.","startPage":"704","endPage":"705","ipdsId":"IP-118170","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"links":[{"id":382285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.32315063476562,\n 46.09228143052647\n ],\n [\n -122.09655761718749,\n 46.09228143052647\n ],\n [\n -122.09655761718749,\n 46.29096881904223\n ],\n [\n -122.32315063476562,\n 46.29096881904223\n ],\n [\n -122.32315063476562,\n 46.09228143052647\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"368","issue":"6492","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808441,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70214962,"text":"70214962 - 2020 - Testing environmental DNA from wolf snow tracks for species, sex, and individual identification","interactions":[],"lastModifiedDate":"2020-10-03T14:45:17.478007","indexId":"70214962","displayToPublicDate":"2020-05-15T09:40:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5015,"text":"Canadian Wildlife Biology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Testing environmental DNA from wolf snow tracks for species, sex, and individual identification","docAbstract":"Monitoring elusive, relatively low-density, large predators, such as the grey wolf (Canis lupus), has often been accomplished by live-capture and radiocollaring. Increasingly, non-invasive methods are considered best practice whenever it is possible to use them. Recently, environmental DNA (eDNA) deposited in snow tracks was demonstrated as useful for identifying lynx (Lynx canadensis), fisher (Pekania pennanti), wolverine (Gulo gulo), sika deer (Cervus nippon), red fox (Vulpes vulpes), and the Japanese marten (Martes melampus) to species level using mitochondrial DNA (mtDNA) markers. We tested whether eDNA from fresh wolf snow tracks collected in the Superior National Forest, Minnesota, USA during winter 2019 could be used to identify species, sex, and individual. Seven of the 8 snow track samples were successfully identified to “wolf-dog” species using mtDNA, with alleles amplifying in 5 of the samples at 1 or both of loci u250 and FH2096 in the allele range for wolves for this population. None yielded enough high-quality DNA to obtain genotypes to determine individual or sex. We recommend additional field trials to determine the minimum number of tracks required per individual to obtain sufficient, high-quality eDNA, as well as collecting associated urine or blood (from estrus) when possible. If individual wolves could be identified and sexed by the eDNA in their snow tracks, researchers should be able to determine population and family or group metrics with greater precision and less effort than typically required when conducting winter scat or hair-based genetic field studies of low density, elusive carnivores. Furthermore, this method would have applications to other areas of research and management, such as hunting quota determinations, validation of field methods and, in particular regards to wolves and other predators, livestock depredation issues. Overall, this technique holds significant future promise as field and laboratory methods are further refined for greater precision and optimized regarding the varying collection, filtration, and extraction protocols for different species in various environments.
Despite the wealth of studies on seasonal movements of birds between southern nonbreeding locations and High Arctic breeding locations, the key mechanisms of navigation during these migrations remain elusive. A flight along the shortest possible route between pairs of points on a sphere (‘orthodrome’) requires a bird to be able to assess its current location in relation to its migration goal and to make continuous adjustment of heading to reach that goal. Alternatively, birds may navigate along a vector with a fixed orientation (‘loxodrome’) based on magnetic and/or celestial compass mechanisms. Compass navigation is considered especially challenging for summer migrations in Polar regions, as continuous daylight and complexity in the geomagnetic field may complicate the use of both celestial and magnetic compasses here. We examine the possible use of orientation mechanisms during migratory flights across the Greenland Icecap. Using a novel 2 g solar-powered satellite transmitter, we documented the flight paths travelled by a female red knot (Calidris canutus islandica) during two northward and two southward migrations. The geometry of the paths suggests that red knots can migrate across the Greenland Icecap along the shortest-, orthodrome-like, path instead of the previously suggested loxodrome path. This particular bird’s ability to return to locations visited in a previous year, together with its sudden course changes (which would be appropriate responses to ambient wind fields), suggest a map sense that enables red knots to determine location, so that they can tailor their route depending on local conditions.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.02464","usgsCitation":"Kok, E., Tibbitts, T.L., Douglas, D.C., Howey, P., Dekinga, A., Gnep, B., and Piersma, T., 2020, A red knot as a black swan: How a single bird shows navigational abilities during repeat crossings of the Greenland Icecap: Journal of Avian Biology, v. 51, no. 8, e2464, 11 p., https://doi.org/10.1111/jav.02464.","productDescription":"e2464, 11 p.","ipdsId":"IP-115751","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":456764,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jav.02464","text":"Publisher Index Page"},{"id":375948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland, Iceland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -13.886718749999998,\n 65.29346780107583\n ],\n [\n -21.97265625,\n 69.83962194067463\n ],\n [\n -18.80859375,\n 75.14077784070429\n ],\n [\n -17.05078125,\n 79.59234918793305\n ],\n [\n -11.25,\n 81.4139332828511\n ],\n [\n -22.8515625,\n 83.15311098437887\n ],\n [\n -29.355468750000004,\n 83.67694304841554\n ],\n [\n -45.52734375,\n 83.29825018349662\n ],\n [\n -64.51171875,\n 81.64692672160267\n ],\n [\n -72.421875,\n 78.42019327591201\n ],\n [\n -69.08203125,\n 76.3518964311259\n ],\n [\n -61.34765625,\n 75.93088543216642\n ],\n [\n -53.78906249999999,\n 71.52490903732816\n ],\n [\n -53.78906249999999,\n 69.2249968541159\n ],\n [\n -51.50390625,\n 64.54844014422517\n ],\n [\n -46.93359375,\n 61.01572481397616\n ],\n [\n -44.29687499999999,\n 59.712097173322924\n ],\n [\n -17.05078125,\n 63.78248603116502\n ],\n [\n -13.886718749999998,\n 65.29346780107583\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kok, Eva","contributorId":225537,"corporation":false,"usgs":false,"family":"Kok","given":"Eva","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":791529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":102185,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":791530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":791531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howey, Paul","contributorId":225538,"corporation":false,"usgs":false,"family":"Howey","given":"Paul","email":"","affiliations":[{"id":41157,"text":"Microwave Telemetry Ltd","active":true,"usgs":false}],"preferred":false,"id":791532,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dekinga, Anne","contributorId":203122,"corporation":false,"usgs":false,"family":"Dekinga","given":"Anne","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":791533,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gnep, Benjamin","contributorId":225539,"corporation":false,"usgs":false,"family":"Gnep","given":"Benjamin","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":791534,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Piersma, Theunis 0000-0001-9668-466X","orcid":"https://orcid.org/0000-0001-9668-466X","contributorId":203123,"corporation":false,"usgs":false,"family":"Piersma","given":"Theunis","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":791535,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216926,"text":"70216926 - 2020 - Hawaiian hoary bat (Lasiurus cinereus semotus) behavior at wind turbines on Maui","interactions":[],"lastModifiedDate":"2020-12-16T13:46:38.158273","indexId":"70216926","displayToPublicDate":"2020-05-15T07:43:01","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Hawaiian hoary bat (Lasiurus cinereus semotus) behavior at wind turbines on Maui","docAbstract":"This study examined the activity of the endemic Hawaiian hoary bat (Lasiurus cinereus semotus) at wind turbines operated by Auwahi Wind Energy, LLC, on southern Maui Island, from August to November 2018. The research was conducted to assess the potential effect of wind speed and turbine operation on bat presence and behavior and compared information obtained from both acoustic monitoring and thermal videography.
|
Chemical gradients between fresh, brackish and saline waters shape biogeochemical reactions and organic matter transformation within subterranean estuaries. In the Yucatán Peninsula’s karst subterranean estuary (KSE), methane and dissolved organic matter generated during the anaerobic decomposition of tropical forest vegetation are transported into flooded cave networks where microbial consumption greatly reduces their concentrations in the groundwater. To test the hypothesis that chemoclines associated with salinity gradients of the KSE are sites of methane oxidation, we obtained methane concentration and δ13C profiles of unprecedented vertical resolution from within a fully-submerged cave system located 6.6 km inland from the coastline using the ‘OctoPiPi’ (OPP) water sampler. Along a 12–24 cm thick low-salinity-halocline at ∼4.5 m water depth, salinity increased from fresh to brackish (0.2–1.8 psu), methane concentrations decreased, and δ13C values increased, as expected for microbial methane oxidation. The underlying brackish water had elevated oxygen concentrations compared to the always anoxic freshwater, suggesting that aerobic methane oxidation is the dominant process facilitating methane consumption. By contrast, as salinity increased from 1.8 to 36 psu through a 24–36 cm thick high-salinity-halocline between the meteoric lens and the saline groundwater at ∼20 m water depth, methane concentrations and δ13C values were constant. Conservative mixing and kinetic isotope models incorporating the methane data confirm a hotspot for microbial methane oxidation at the low-salinity-halocline. At least 98% of methane originating in the anoxic freshwaters was removed before its transport via channelized flow towards the coastline. These findings provide novel insight into the spatial constraints of methane dynamics within a karst subterranean estuary.
Quantifying system-wide biogeochemical dynamics and ecosystem metabolism in estuaries is often attempted using a long-term continuous record at a single site or short-term records at multiple sites due to sampling limitations that preclude long-term monitoring. However, differences in the dominant primary producer at a given location (e.g., phytoplankton versus benthic producers) control diel variations in dissolved oxygen and associated ecosystem metabolism, and they may confound metabolic estimates that do not account for this variability. We hypothesize that even in shallow, well-mixed estuaries there is strong spatiotemporal variability in ecosystem metabolism due to benthic and water-column properties, as well as ensuing feedbacks to sediment resuspension, light attenuation, and primary production. We tested this hypothesis by measuring hydrodynamic properties, biogeochemical variables (fluorescent dissolved organic matter – fDOM, turbidity, chlorophyll a fluorescence, dissolved oxygen), and photosynthetically active radiation (PAR) over 1 year at 15 min intervals at paired channel (unvegetated) and shoal (vegetated by eelgrass) sites in Chincoteague Bay, Maryland–Virginia, USA, a shallow back-barrier estuary. Light attenuation (KdPAR) at all sites was dominated by turbidity from suspended sediment, with lower contributions from fDOM and chlorophyll a. However, there was significant seasonal variability in the resuspension–shear stress relationship on the vegetated shoals, but not in adjacent unvegetated channels. This indicated that KdPAR on the shoals was mediated by submerged aquatic vegetation (SAV) and possibly microphytobenthos presence in the summer, which reduced resuspension and therefore KdPAR. We also found that gross primary production (Pg) and KdPAR were significantly negatively correlated on the shoals and uncorrelated in the channels, indicating that Pg over the vegetated shoals is controlled by a feedback loop between benthic stabilization by SAV and/or microphytobenthos, sediment resuspension, and light availability. Metabolic estimates indicated substantial differences in net ecosystem metabolism between vegetated and unvegetated sites, with the former tending towards net autotrophy in the summer. Ongoing trends of SAV loss in this and other back-barrier estuaries suggest that these systems may also shift towards net heterotrophy, reducing their effectiveness as long-term carbon sinks. With regards to temporal variability, we found that varying sampling frequency between 15 min and 1 d resulted in comparable mean values of biogeochemical variables, but extreme values were missed by daily sampling. In fact, daily resampling minimized the variability between sites and falsely suggested spatial homogeneity in biogeochemistry, emphasizing the need for high-frequency sampling. This study confirms that properly quantifying ecosystem metabolism and associated biogeochemical variability requires characterization of the diverse estuarine environments, even in well-mixed systems, and demonstrates the deficiencies introduced by infrequent sampling to the interpretation of spatial variability.
","language":"English","publisher":"European Geodsciences Union","doi":"10.5194/os-16-593-2020","usgsCitation":"Ganju, N.K., Testa, J., Suttles, S.E., and Aretxabaleta, A., 2020, Spatiotemporal variability of light attenuation and net ecosystem metabolism in a back-barrier estuary: Ocean Science, v. 16, p. 593-614, https://doi.org/10.5194/os-16-593-2020.","productDescription":"22 p.","startPage":"593","endPage":"614","ipdsId":"IP-103242","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/os-16-593-2020","text":"Publisher Index Page"},{"id":377456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.39093017578125,\n 37.917117737741826\n ],\n [\n -75.30166625976562,\n 37.95827503526034\n ],\n [\n -75.20416259765625,\n 38.08809407886683\n ],\n [\n -75.16021728515624,\n 38.18098951438852\n ],\n [\n -75.13687133789061,\n 38.23710146774334\n ],\n [\n -75.09841918945311,\n 38.3211882645322\n ],\n [\n -75.15060424804688,\n 38.28131307922966\n ],\n [\n -75.18081665039062,\n 38.23494411562881\n ],\n [\n -75.19454956054688,\n 38.272688535980976\n ],\n [\n -75.28106689453125,\n 38.19502155795575\n ],\n [\n -75.311279296875,\n 38.11727165830543\n ],\n [\n -75.32913208007812,\n 38.111869281948955\n ],\n [\n -75.36483764648438,\n 38.07404145941957\n ],\n [\n -75.35659790039062,\n 38.06106741381201\n ],\n [\n -75.3936767578125,\n 38.034030762875844\n ],\n [\n -75.43212890625,\n 37.98750437106374\n ],\n [\n -75.44036865234375,\n 37.95827503526034\n ],\n [\n -75.39093017578125,\n 37.917117737741826\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganju, Neil Kamal 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":192273,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil","email":"nganju@usgs.gov","middleInitial":"Kamal","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":796060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Testa, Jeremy","contributorId":199779,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","affiliations":[],"preferred":false,"id":796061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suttles, Steven E. 0000-0002-4119-8370 ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":192272,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven","email":"ssuttles@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":796062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":796063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213698,"text":"70213698 - 2020 - Biological soil crusts in ecological restoration: Emerging research and perspectives","interactions":[],"lastModifiedDate":"2020-09-18T21:33:47.885725","indexId":"70213698","displayToPublicDate":"2020-05-14T16:31:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Biological soil crusts in ecological restoration: Emerging research and perspectives","docAbstract":"Drylands encompass over 40% of terrestrial ecosystems and face significant anthropogenic degradation causing a loss of ecosystem integrity, services, and deterioration of social‐ecological systems. To combat this degradation, some dryland restoration efforts have focused on the use of biological soil crusts (biocrusts): complex communities of cyanobacteria, algae, lichens, bryophytes, and other organisms living in association with the top millimeters of soil. Biocrusts are common in many ecosystems and especially drylands. They perform a suite of ecosystem functions: stabilizing soil surfaces to prevent erosion, contributing carbon through photosynthesis, fixing nitrogen, and mediating the hydrological cycle in drylands. Biocrusts have emerged as a potential tool in restoration; developing methods to implement effective biocrust restoration has the potential to return many ecosystem functions and services. Although culture‐based approaches have allowed researchers to learn about the biology, physiology, and cultivation of biocrusts, transferring this knowledge to field implementation has been more challenging. A large amount of research has amassed to improve our understanding of biocrust restoration, leaving us at an opportune time to learn from one another and to join approaches for maximum efficacy. The articles in this special issue improve the state of our current knowledge in biocrust restoration, highlighting efforts to effectively restore biocrusts through a variety of different ecosystems, across scales and utilizing a variety of lab and field methods. This collective work provides a useful resource for the scientific community as well as land managers.
","language":"English","doi":"10.1111/rec.13201","usgsCitation":"Antoninka, A., Faist, A.M., Rodriguez-Caballero, E., Young, K., Chaudhary, V., Condon, L.A., and Pyke, D.A., 2020, Biological soil crusts in ecological restoration: Emerging research and perspectives: Restoration Ecology, v. 28, no. S2, p. s3-s8, https://doi.org/10.1111/rec.13201.","productDescription":"6 p.","startPage":"s3","endPage":"s8","ipdsId":"IP-117103","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":456772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13201","text":"Publisher Index Page"},{"id":378584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"S2","noUsgsAuthors":false,"publicationDate":"2020-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Antoninka, Anita","contributorId":166769,"corporation":false,"usgs":false,"family":"Antoninka","given":"Anita","affiliations":[{"id":24503,"text":"Northern Arizona University, School of Forestry, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":799232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faist, Akasha M.","contributorId":193038,"corporation":false,"usgs":false,"family":"Faist","given":"Akasha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodriguez-Caballero, Emilio 0000-0002-5934-3214","orcid":"https://orcid.org/0000-0002-5934-3214","contributorId":205639,"corporation":false,"usgs":false,"family":"Rodriguez-Caballero","given":"Emilio","email":"","affiliations":[{"id":37132,"text":"Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":799234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Kristina E.","contributorId":195945,"corporation":false,"usgs":false,"family":"Young","given":"Kristina E.","affiliations":[],"preferred":false,"id":799235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chaudhary, V Bala","contributorId":240984,"corporation":false,"usgs":false,"family":"Chaudhary","given":"V Bala","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":799236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":799237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":799238,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228556,"text":"70228556 - 2020 - A decision-support tool to prioritize candidate landscapes for lesser prairie-chicken conservation","interactions":[],"lastModifiedDate":"2022-02-14T19:36:31.861247","indexId":"70228556","displayToPublicDate":"2020-05-14T14:36:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A decision-support tool to prioritize candidate landscapes for lesser prairie-chicken conservation","docAbstract":"Context
Development of systematic methods for conservation planning has improved effectiveness and efficiency of implementing such plans. The lesser prairie-chicken (Tympanuchus pallidicinctus) is a grouse species of conservation concern native to the southwestern Great Plains of the United States. Recent lesser prairie-chicken conservation planning has involved identifying ecologically important areas but has not incorporated economic data into prioritization of areas to target for conservation management.
Objectives
We used the program Marxan to develop a decision-support tool for managers in Kansas to prioritize tracts for improving lesser prairie-chicken habitat quality and increasing habitat availability. We developed three different conservation scenarios and evaluated the tradeoffs among multiple planning objectives in these scenarios.
Methods
We incorporated population targets from an existing conservation plan and agricultural economic data to help select land with maximum ecological value and minimum economic productivity to prioritize for lesser prairie-chicken conservation. We compared potential conservation plans and incorporated a post hoc connectivity model to test potential for individuals to travel among habitat patches in these plans during dispersal events.
Results
We found that different conservation scenarios led to different solutions, though differences varied by ecoregion. Potential solutions for all scenarios contained habitat patches not currently included in existing conservation plans and had high connectivity potential.
Conclusions
These results provide context for spatial prioritization of lesser prairie-chicken habitat management in Kansas. Application of this approach to species of conservation interest could help managers incorporate socioeconomic factors into planning methods and identify important tracts for conservation currently overlooked by existing planning methods.
The 2018 Kīlauea Volcano eruption lasted 107 days, and now ranks as the most destructive event at Kilauea since 1790, and as one of the most costly volcanic disasters in U.S. history. Multiple simultaneous hazard events unfolded, including sustained seismic activity leading to collapse at the summit of Halema'uma'u crater and severe damage to the HVO facility, with additional eruption of lava in the Kīlauea Lower East Rift Zone that progressively grew to a total of 24 fissure openings. In response to the complex and uncertain nature of the eruption, U.S. Geological Survey (USGS) team activities tended to coalesce around several interrelated core functional areas: science operations, emergency management and administration, and external communications. Upon cessation of the eruption, the USGS Hazard Response Executive Committee charged the Alaska Regional Office to lead an After-Action Review team, which focused on two basic questions: (1) what went well, and why?; and (2) what can be improved, and how? The purpose of the After-Action Review is to identify priorities for programmatic or policy improvements that the USGS can feasibly implement to advance strategic preparation for future disasters, and thereby reduce public vulnerabilities. Specifically, the review is intended to help USGS respond even better to the next eruption in Hawai'i or elsewhere by advancing any of the following goals:
Regional Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
Factors affecting iron fouling in wet areas adjacent to roadways were investigated by collecting field rock cut and aqueous physicochemical data; developing exploratory predictive models; and developing geochemical models. Basic data included the identification of iron fouling from aerial imagery and field visits at 374 New Hampshire rock cut locations, and their associated rock-fill sites. Based on field water quality measurements from wet areas at 36 of the rock-fill sites, the occurrence of iron fouling was associated with higher values of specific conductance, lower concentrations of dissolved oxygen and lower pH compared to areas without iron fouling. A statistical model, using boosted regression trees, was developed to predict the occurrence of iron fouling in wet areas adjacent to roadways where rock-fill from nearby rock cuts was used in roadway construction. The model was used to develop a continuous iron fouling probability map for the state of New Hampshire that can be used to better understand the occurrence of iron fouling. Geochemical models illustrate how iron fouling of waters increases along roadways built with fill from sulfidic rock cuts as a result of acid generation from pyrite dissolution and ferrous iron (Fe2+) oxidation and increases in areas with greater specific conductance from deicing runoff caused by cation exchange. More iron is precipitated as goethite in simulations that include pyrite, and in simulations with deicing salts added, indicating that rock-fill sites with rocks that contain pyrite and water with greater salt content could have enhanced iron fouling.
","language":"English","publisher":"Springer","doi":"10.1007/s42452-020-2849-2","usgsCitation":"Lombard, M.A., Lombard, P.J., Brown, C., and Degnan, J., 2020, A multi-model approach toward understanding iron fouling at rock-fill drainage sites along roadways in New Hampshire, USA: SN Applied Sciences, 1073, 16 p., https://doi.org/10.1007/s42452-020-2849-2.","productDescription":"1073, 16 p.","ipdsId":"IP-100784","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":456777,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s42452-020-2849-2","text":"Publisher Index Page"},{"id":436987,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TUOH95","text":"USGS data release","linkHelpText":"Iron fouling data associated with drainage from roadway sites constructed with rock fill in New 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Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":794079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":203509,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Degnan, James R. 0000-0002-5665-9010","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":218796,"corporation":false,"usgs":true,"family":"Degnan","given":"James R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794082,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228606,"text":"70228606 - 2020 - Tracking fisheries through time: The American Fisheries Society as an historical lens","interactions":[],"lastModifiedDate":"2024-09-18T15:54:07.350505","indexId":"70228606","displayToPublicDate":"2020-05-14T09:20:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Tracking fisheries through time: The American Fisheries Society as an historical lens","docAbstract":"The historical context of current environment conditions offers vital guidance to North American fisheries professionals as they strive to develop effective management plans and policies. This retrospective includes understanding the remarkable historic fisheries resource and causative reasons for establishing their public fisheries agencies and the American Fisheries Society (Society). Through a review of primary historical documents and literature for North America, this paper identifies: factors contributing to the of founding of the Society and public fish commissions; shows how selected resource issues evolved; and documents how and why selected fisheries and aquatic habitat policies changed in response to those issues. Overexploitation, landscape-scale habitat alterations, mining, and dams were the causative agents for emergence of fisheries agencies and the Society, and these environmental factors remain relevant today. Beginning in the 1960s, after 100 years of inaction, North Americans grew tired of degraded waters and fisheries and forced policy changes that have directly and indirectly affected the fisheries we manage today. The historical events and resulting corrective legislation are taken for granted by those unaware that our Society has actively participated in developing policies to address these environmental harms. Further, the Society continues to play a vital role in identifying key issues, providing conduits for information to cope with impairments, and advocating for policies to conserve intact habitats and improve degraded systems. The struggles documented in this paper offer crucial lessons as we continue to be challenged by legacy resource issues and face emerging environmental stressors such as climate change as well as regression in long-standing environmental protection policies.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10457","usgsCitation":"Whelan, G., Day, D., Casselman, J., Gephart, L., Hall, C., Lichatowich, J., Matylewich, M., Miranda, L.E., Roulson, L., Shirey, P., Mercado-Silva, N., Waldman, J., and Winters, D., 2020, Tracking fisheries through time: The American Fisheries Society as an historical lens: Fisheries Magazine, v. 45, no. 8, p. 392-426, https://doi.org/10.1002/fsh.10457.","productDescription":"35 p.","startPage":"392","endPage":"426","ipdsId":"IP-117673","costCenters":[{"id":198,"text":"Coop Res Unit 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estimation methods that transfer information from an index gage to an ungaged site are commonly used; however, uncertainty in daily streamflow estimates are often not adequately quantified. In this study, daily streamflow was simulated at 1,331 validation streamgages across the continental United States using four transfer-based streamflow estimation methods. Empirical 95 percent uncertainty intervals were computed for estimated daily streamflows. Uncertainty intervals were evaluated for reliability, sharpness, and overall ability to accurately quantify the uncertainty inherent in the estimated daily streamflow. Uncertainty intervals performed reliably in the Eastern U.S. and Pacific Northwest regions of the country, containing a median of 96 and 99 percent of the observed values respectively. Uncertainty intervals were less reliable in the Great Plains and arid Southwest regions, where uncertainty intervals contained a median of 83 and 94 percent of the observed streamflows respectively. Uncertainty interval performance was correlated with gage density and hydrologic similarity near the validation site, as well as the aridity and base-flow indices at the site.","language":"English","publisher":"MDPI","doi":"10.3390/w12051390","collaboration":"","usgsCitation":"Levin, S., and Farmer, W.H., 2020, Evaluation of uncertainty intervals for daily, statistically derived streamflow estimates at ungaged basins across the continental U.S.: Water, v. 12, no. 5, 1390, 20 p., https://doi.org/10.3390/w12051390.","productDescription":"1390, 20 p.","ipdsId":"IP-117236","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456781,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12051390","text":"Publisher Index Page"},{"id":436988,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KCLD9W","text":"USGS data release","linkHelpText":"Performance of 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0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":789281,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210180,"text":"70210180 - 2020 - Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol","interactions":[],"lastModifiedDate":"2020-05-19T12:35:26.049125","indexId":"70210180","displayToPublicDate":"2020-05-14T07:27:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5897,"text":"Environmental Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol","docAbstract":"Background \nAmong the most widely anticipated climate-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution in response to changing climate conditions. In particular, a series of commonly articulated hypotheses have emerged: species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations to track spatial changes in the temperature of their current range. Yet, many species are not demonstrating range shifts consistent with these hypotheses. Resolving this discrepancy and providing effective explanations for the observed variability in species’ range shifts is urgently needed to help support a range of natural resource management decisions. Here, we propose a protocol to review the body of evidence for commonly-held climate change range shift hypotheses at the species level focusing on observed latitudinal, longitudinal, elevational, and depth shifts in response to temperature and precipitation changes. We aim to answer the question: what is the impact of anthropogenic climate change (specifically changes in temperature and precipitation) on species ranges?\n \nMethods \nIn this review protocol, we propose to conduct a systematic search of literature from internet databases and search engines in English. Articles will be screened in a two-stage process (title/abstract and full text) to evaluate whether they meet a list of eligibility criteria (e.g., presents species-level data, compares >1 time period). Initial data coding and extraction will be completed by four reviewers and checked by a secondary reviewer from among our co-authors. We will perform a formal meta-analysis to document estimated effect size using the subset of available range-shift data expressed in distance per time (e.g., km/decade). We will also use multinomial logistic regression models to assess the probability that species are shifting in a direction that supports our hypotheses (i.e. towards higher latitudes, greater elevations, and deeper depths). We will account for study methodology as a potential source of variation.","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-020-00194-9","collaboration":"","usgsCitation":"Rubenstein, M.A., Weiskopf, S.R., Carter, S., Eaton, M.J., Johnson, C., Lynch, A., Miller, B.W., Morelli, T.L., Rodriguez, M.A., Terando, A., and Thompson, L., 2020, Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol: Environmental Evidence, v. 9, 10, 10 p., https://doi.org/10.1186/s13750-020-00194-9.","productDescription":"10, 10 p.","ipdsId":"IP-113427","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":456784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-020-00194-9","text":"Publisher Index Page"},{"id":374913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Rubenstein, Madeleine A. 0000-0001-8569-781X mrubenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-8569-781X","contributorId":203206,"corporation":false,"usgs":true,"family":"Rubenstein","given":"Madeleine","email":"mrubenstein@usgs.gov","middleInitial":"A.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Shawn 0000-0002-0045-4681","orcid":"https://orcid.org/0000-0002-0045-4681","contributorId":216490,"corporation":false,"usgs":true,"family":"Carter","given":"Shawn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":789448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Ciara","contributorId":224775,"corporation":false,"usgs":false,"family":"Johnson","given":"Ciara","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":789449,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789450,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Brian W. 0000-0003-1716-1161 bwmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":191731,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"bwmiller@usgs.gov","middleInitial":"W.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":false,"id":789451,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789452,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rodriguez, Mari Angel 0000-0002-3372-1897","orcid":"https://orcid.org/0000-0002-3372-1897","contributorId":224776,"corporation":false,"usgs":true,"family":"Rodriguez","given":"Mari","email":"","middleInitial":"Angel","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":789453,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":789454,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789455,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70211827,"text":"70211827 - 2020 - Food web controls on mercury fluxes and fate in the Colorado River, Grand Canyon","interactions":[],"lastModifiedDate":"2020-08-07T21:59:17.624094","indexId":"70211827","displayToPublicDate":"2020-05-13T16:52:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Food web controls on mercury fluxes and fate in the Colorado River, Grand Canyon","docAbstract":"Mercury (Hg) biomagnification in aquatic food webs is a global concern; yet, the ways species traits and interactions mediate these fluxes remain poorly understood. Few pathways dominated Hg flux in the Colorado River despite large spatial differences in food web complexity, and fluxes were mediated by one functional trait, predation resistance. New Zealand mudsnails are predator resistant and a trophic dead end for Hg in food webs we studied. Fishes preferred blackflies, which accounted for 56 to 80% of Hg flux to fishes, even where blackflies were rare. Food web properties, i.e., match/mismatch between insect production and fish consumption, governed amounts of Hg retained in the river versus exported to land. An experimental flood redistributed Hg fluxes in the simplified tailwater food web, but not in complex downstream food webs. Recognizing that species traits, species interactions, and disturbance mediate contaminant exposure can improve risk management of linked aquatic-terrestrial ecosystems.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.aaz4880","usgsCitation":"Walters, D., Cross, W., Kennedy, T., Baxter, C., Hall, R., and Rosi, E.J., 2020, Food web controls on mercury fluxes and fate in the Colorado River, Grand Canyon: Science Advances, v. 6, no. 20, eaaz4880, 10 p., https://doi.org/10.1126/sciadv.aaz4880.","productDescription":"eaaz4880, 10 p.","ipdsId":"IP-111739","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":456788,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aaz4880","text":"Publisher Index Page"},{"id":436989,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NBAHFF","text":"USGS data release","linkHelpText":"Consumption rates and total mercury concentration of food items and consumers collected at six sites on the Colorado River in the Grand Canyon, USA, 2007-2009"},{"id":377213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.39038085937499,\n 36.712467243386264\n ],\n [\n -112.313232421875,\n 36.60670888641815\n ],\n [\n -112.862548828125,\n 36.518465989675875\n ],\n [\n -113.741455078125,\n 36.36822190085111\n ],\n [\n -114.12597656249999,\n 36.217687122250574\n ],\n [\n -114.32373046875,\n 36.518465989675875\n ],\n [\n -114.949951171875,\n 36.1733569352216\n ],\n [\n -114.840087890625,\n 35.93354064249312\n ],\n [\n -114.554443359375,\n 35.96022296929667\n ],\n [\n -113.97216796875,\n 35.951329861522666\n ],\n [\n -113.291015625,\n 35.55010533588552\n ],\n [\n -113.126220703125,\n 35.951329861522666\n ],\n [\n -112.642822265625,\n 36.11125252076156\n ],\n [\n -111.895751953125,\n 35.782170703266075\n ],\n [\n -111.39038085937499,\n 36.712467243386264\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Wyatt F.","contributorId":237773,"corporation":false,"usgs":false,"family":"Cross","given":"Wyatt F.","affiliations":[{"id":47607,"text":"Department of Ecology, Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":795261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795262,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baxter, Colden V.","contributorId":47334,"corporation":false,"usgs":false,"family":"Baxter","given":"Colden V.","affiliations":[{"id":13656,"text":"Idaho State Univ.","active":true,"usgs":false}],"preferred":false,"id":795263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, R. O. Jr.","contributorId":216427,"corporation":false,"usgs":false,"family":"Hall","given":"R. O.","suffix":"Jr.","affiliations":[{"id":39416,"text":"Flathead Lake Biological Station, University of Montana","active":true,"usgs":false}],"preferred":false,"id":795264,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rosi, Emma J.","contributorId":201758,"corporation":false,"usgs":false,"family":"Rosi","given":"Emma","email":"","middleInitial":"J.","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":795265,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208647,"text":"ofr20201014 - 2020 - Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality","interactions":[],"lastModifiedDate":"2023-03-28T15:36:57.475785","indexId":"ofr20201014","displayToPublicDate":"2020-05-13T16:44:02","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1014","displayTitle":"Time-Series Model, Statistical Methods, and Software Documentation for R–QWTREND—An R Package for Analyzing Trends in Stream-Water Quality","title":"Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality","docAbstract":"As part of a U.S. Geological Survey water-quality study started in 2018, in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency, a publicly available software package called R–QWTREND was developed for analyzing trends in stream-water quality. The R–QWTREND package is a collection of functions written in R, an open source language and a general environment for statistical computing and graphics. The package uses a parametric time-series model to express logarithmically transformed concentration in terms of flow-related variability, trend, and serially correlated model errors. Flow-related variability captures natural variability in concentration on the basis of concurrent and antecedent streamflow. The trend identifies systematic changes in concentration in terms of potential step trends, piecewise monotonic trends, or user-specified trends. Maximum likelihood estimation is used to estimate model parameters and determine the best-fit trend model. This report describes the time-series model and statistical methodology behind R–QWTREND and provides formal documentation for installing and using the package.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201014","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency","usgsCitation":"Vecchia, A.V., and Nustad, R.A., 2020, Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality (ver. 1.2, March 2023): U.S. Geological Survey Open-File Report 2020–1014, 51 p., https://doi.org/10.3133/ofr20201014.","productDescription":"Report: viii, 52 p.; Appendix; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109088","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":414697,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1014/versionHist.txt","text":"Version History","size":"2 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":374762,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System","linkHelpText":"USGS water data for the Nation"},{"id":414696,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1014/ofr20201014.pdf","text":"Report","size":"4.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–2014"},{"id":374759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1014/coverthb3.jpg"},{"id":374761,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1014/downloads/","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020–2014 Appendix 1","linkHelpText":"R–QWTREND Software Package"}],"edition":"Version 1.0: May 13, 2020; Version 1.1: November 30, 2021; Version 1.2: March 28, 2023","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Sustainability of dryland ecosystems depends on the functionality of soil-vegetation feedbacks that affect ecosystem processes, such as nutrient cycling, water capture and retention, soil erosion and deposition, and plant establishment and reproduction. Useful, common indicators can provide information on soil and site stability, hydrologic function, and biotic integrity. Evaluation of rangeland health thus relies on describing the condition and sustainability of these individual, measurable, and observable indicators that are linked to important ecosystem processes. This report focuses on the ~200,000 acres of the Grand Canyon-Parashant National Monument that is administered by the National Park Service (NPS)—one of the largest NPS units where livestock grazing is a permitted land-use activity. Many ecosystems in the monument are characterized by a low degree of resilience to improper grazing because of low and variable precipitation. The monument is marked by a high degree of environmental heterogeneity, including a large elevation gradient, widely differing precipitation patterns, a diversity of geologic substrates, and unique combinations of plant species.
The objective of this report is to (1) increase our understanding of the underlying landscape, soil, and climate setting factors that affect Grand Canyon-Parashant National Monument dryland ecosystem structure and function (also referred to as land potential) and (2) characterize the condition of monument ecosystems in relation to management concepts, such as rangeland health.
Data were analyzed by elevation zone using both univariate and multivariate approaches. Survey results document the high level of diversity within the study area, including 15 unique soil taxa and 271 species of plants. We collected three new plant species for Grand Canyon-Parashant National Monument and 17 new species for the NPS portion of the monument. Results also document a strong association between rangeland health indicators and elevation, topographic setting, and soils. Soil factors found to explain important variation across plots include the amount of exposed bedrock, soil rockiness, soil texture (and associated hydrologic properties), and soil depth. We also found that dominant species turnover across elevation may represent species’ differences in adaptation to climates, including Larrea tridentata, Coleogyne ramosissima, and Artemisia spp. Bromus rubens is the most common invasive species of concern recorded in this study, but other common invasive species are Bromus tectorum, Erodium cicutarium, and Schismus arabicus. Correlations between an index of cattle use and indicators of rangeland health suggest that areas with high cattle use have increased bare ground, decreased ground cover, increased frequency of Schismus arabicus, decreased cover of Coleogyne ramosissima and Ephedra spp., and increased cover of Gutierrezia spp. The few strong correlations observed between indicators of vascular plant community cover or abundance and indicators of cattle activity support rangeland assessment and monitoring strategies that do not rely solely on plant-based indicators are needed.
This work supports management of dryland ecosystems, including Grand Canyon-Parashant National Monument, using concepts of land potential. We conclude the report with recommendations on improving existing land-potential-based classification systems, associated interpretations, and methods for moving forward with a Grand Canyon-Parashant National Monument rangeland monitoring program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201040","usgsCitation":"Duniway, M.C., and Palmquist, E.C., 2020, Assessment of rangeland ecosystem conditions in Grand Canyon-Parashant National Monument, Arizona: U.S. Geological Survey Open-File Report 2020–1040, 42 p., https://doi.org/10.3133/ofr20201040.","productDescription":"Report: viii, 42 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-106479","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":374803,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJSJHT","linkHelpText":"Rangeland Ecosystem Data, Grand Canyon - Parashant National Monument, AZ, USA"},{"id":374801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1040/coverthb.jpg"},{"id":374802,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1040/ofr20201040.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon-Parashant National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.005126953125,\n 35.679609609368576\n ],\n [\n -111.57714843749999,\n 35.679609609368576\n ],\n [\n -111.57714843749999,\n 36.97622678464096\n ],\n [\n -114.005126953125,\n 36.97622678464096\n ],\n [\n -114.005126953125,\n 35.679609609368576\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a complete inventory and simply shows general trends and examples of types and distribution of ground failures documented. Overflight observations (December 1–6) documented landslide and liquefaction presence or absence within the Chugach Mountains and along Cook Inlet in regions inaccessible to vehicles, which greatly expanded the geographic scope of this reconnaissance. Field-mapped ground-failure observations have been augmented with a set of 565 georeferenced and annotated images from both field and overflight reconnaissance cataloging additional ground-failure presence or absence from the Anchorage earthquake.
Tidal erosion, fresh snowfall, limited daylight, and adverse flying conditions contributed significantly to the uncertainty and incompleteness of ground-failure observations during this reconnaissance. Notably, substantial liquefaction features at the mouths of the Little Susitna River (December 1) and Ingram Creek (December 5) were absent during subsequent overlapping missions (December 6 and 9, respectively) because of tidal action. A large rockfall observed on December 1 on Rainbow Peak was not observed during a subsequent December 5 overflight because of snow cover, which suggests that the general lack of observations of landsliding within the Chugach Mountains is uncertain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201043","usgsCitation":"Grant, A.R.R., Jibson, R.W., Witter, R.C., Allstadt, K.E., Thompson, E.M., and Bender, A.M., 2020, Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake: U.S. Geological Survey Open-File Report 2020–1043, 21 p., https://doi.org/10.3133/ofr20201043.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-106267","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1043/coverthb.jpg"},{"id":374799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1043/ofr20201043.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99ONUNM","linkHelpText":"Field Reconnaissance of Ground Failure Triggered by Shaking during the 2018 M7.1 Anchorage, Alaska, Earthquake"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.54541015625,\n 60.66241476534369\n ],\n [\n -148.82080078125,\n 60.66241476534369\n ],\n [\n -148.82080078125,\n 61.77312286453146\n ],\n [\n -151.54541015625,\n 61.77312286453146\n ],\n [\n -151.54541015625,\n 60.66241476534369\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035