Hawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.
ABSTRACT: The ‘bet hedging’ life history strategy of long-lived iteroparous species reduces short-term reproductive output to minimize the risk of reproductive failure over a lifetime. For desert-dwelling ectotherms living in variable and unpredictable environments, reproductive output is further influenced by precipitation and temperature via effects on food availability and limits on activity. We assembled multiple (n = 12) data sets on egg production for the threatened Agassiz’s desert tortoise Gopherus agassizii across its range and used these data to build a range-wide predictive model of annual reproductive output as a function of annual weather variation and individual-level attributes (body size and prior-year reproductive status). Climate variables were more robust predictors of reproductive output than individual-level attributes, with overall reproductive output positively related to prior-year precipitation and an earlier start to the spring activity season, and negatively related to spring temperature extremes (monthly temperature range in March-April). Reproductive output was highest for individuals with larger body sizes that reproduced in the previous year. Expected annual reproductive output from 1990-2018 varied from 2-5 to 6-12 eggs female-1 yr-1 , with a weak decline in expected reproductive output over this time (p = 0.02). Climate-driven environmental variation in expected reproductive output was highly correlated across all 5 Recovery Units for this species (Pearson’s r > 0.9). Overall, our model suggests that climate change could strongly impact the reproductive output of Agassiz’s desert tortoise, and could have a negative population-level effect if precipitation is significantly reduced across the species’ range as predicted under some climate models.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01103","usgsCitation":"Mitchell, C.I., Friend, D., Phillips, L.T., Hunter, E., Lovich, J.E., Agha, M., Puffer, S., Cummings, K.L., Medica, P.A., Esque, T., Nussear, K.E., and Shoemaker, K.T., 2021, ‘Unscrambling’ the drivers of egg production in Agassiz’s desert tortoise: Climate and individual attributes predict reproductive output: Endangered Species Research, v. 44, p. 217-230, https://doi.org/10.3354/esr01103.","productDescription":"14 p.","startPage":"217","endPage":"230","ipdsId":"IP-121127","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453130,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01103","text":"Publisher Index Page"},{"id":436463,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WD6AH","text":"USGS data release","linkHelpText":"Mojave Desert Tortoise (Gopherus agassizii) Morphometrics and Egg Data from Seven Sites across the Mojave, (1997-2002)"},{"id":436462,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97XT7HF","text":"USGS data release","linkHelpText":"Agassiz's desert tortoise and egg data from the Sonoran Desert of California (1997-2000, 2015-2018)"},{"id":384375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Arizona, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 33.211116472416855\n ],\n [\n -112.763671875,\n 33.211116472416855\n ],\n [\n -112.763671875,\n 37.16031654673677\n ],\n [\n -117.158203125,\n 37.16031654673677\n ],\n [\n -117.158203125,\n 33.211116472416855\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Corey I. 0000-0001-9479-7329","orcid":"https://orcid.org/0000-0001-9479-7329","contributorId":255287,"corporation":false,"usgs":false,"family":"Mitchell","given":"Corey","email":"","middleInitial":"I.","affiliations":[{"id":51512,"text":"Department of Geography, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friend, Derek 0000-0002-6909-8769","orcid":"https://orcid.org/0000-0002-6909-8769","contributorId":255288,"corporation":false,"usgs":false,"family":"Friend","given":"Derek","email":"","affiliations":[{"id":51512,"text":"Department of Geography, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":true,"id":812083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phillips, Lauren T. 0000-0003-3110-1755","orcid":"https://orcid.org/0000-0003-3110-1755","contributorId":255289,"corporation":false,"usgs":false,"family":"Phillips","given":"Lauren","email":"","middleInitial":"T.","affiliations":[{"id":51512,"text":"Department of Geography, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunter, Elizabeth A.","contributorId":149399,"corporation":false,"usgs":false,"family":"Hunter","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":812085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":812086,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false},{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":812167,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Puffer, Shellie R. 0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812088,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812089,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Medica, Philip A.","contributorId":55780,"corporation":false,"usgs":true,"family":"Medica","given":"Philip","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812090,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":812168,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":812092,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shoemaker, Kevin T. 0000-0002-3789-3856","orcid":"https://orcid.org/0000-0002-3789-3856","contributorId":255290,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin","email":"","middleInitial":"T.","affiliations":[{"id":51513,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno. 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812093,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70219025,"text":"70219025 - 2021 - Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability","interactions":[],"lastModifiedDate":"2021-04-08T15:14:07.19162","indexId":"70219025","displayToPublicDate":"2021-03-11T07:13:02","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":"Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability","docAbstract":"In coarse resolution hydrological modeling we face the problem of subgrid variability, the effects of which are difficult to express and are often hidden in the parameterization and calibration. We present a numerical experiment with the physically based hydrological model ParFlow‐CLM with which we quantify the effect of subgrid heterogeneities in headwater catchments within the cell size typically used for regional hydrological applications. We simulate homogeneous domains and domains with subgrid heterogeneities in topography or soil thickness for two climates and soil types. The presence of side slope is the main error source, leading to large underestimation of runoff, and marginally also of evapotranspiration. The spatial distribution of soil saturation in the presence of subgrid variability in topography also leads to underestimation of landslide risk. Soil thickness is the second influential subgrid property, affecting soil moisture distribution and surface runoff formation. Results are consistent for the climates and the soil types considered. The topographic wetness index approach is tested as a way to downscale soil moisture simulations within the domain. Although this method is successful in reproducing some spatial variability and patterns, it fails when the coarse grid mean soil saturation is inaccurate or subgrid topography does not represent subsurface flow paths accurately. We conclude that ignoring subgrid variability in topography and soil thickness in coarse‐scale hydrological models may lead locally to underestimation of runoff and slope instability. Users of such models should be aware of these biases and consider ways to include subgrid effects in coarse‐scale hydrological predictions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027326","usgsCitation":"Leonarduzzi, E., Maxwell, R.M., Mirus, B.B., and Molnar, P., 2021, Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability: Water Resources Research, v. 57, no. 4, e2020WR027326, 16 p., https://doi.org/10.1029/2020WR027326.","productDescription":"e2020WR027326, 16 p.","ipdsId":"IP-124808","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453131,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020wr027326","text":"External Repository"},{"id":384495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonarduzzi, E. 0000-0002-6811-9118","orcid":"https://orcid.org/0000-0002-6811-9118","contributorId":255523,"corporation":false,"usgs":false,"family":"Leonarduzzi","given":"E.","email":"","affiliations":[{"id":51571,"text":"Institute of Environmental Engineering, ETH Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":812489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maxwell, R. M.","contributorId":255524,"corporation":false,"usgs":false,"family":"Maxwell","given":"R.","email":"","middleInitial":"M.","affiliations":[{"id":51573,"text":"Integrated Groundwater Modeling Center and Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":812490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":812491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molnar, P. 0000-0001-6437-4931","orcid":"https://orcid.org/0000-0001-6437-4931","contributorId":255525,"corporation":false,"usgs":false,"family":"Molnar","given":"P.","email":"","affiliations":[{"id":51575,"text":"Institute of Environmental Engineering, ETH Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":812492,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219608,"text":"70219608 - 2021 - A chemical and bio‐herbicide mixture increased exotic invaders, both targeted and non‐targeted, across a diversely invaded landscape after fire","interactions":[],"lastModifiedDate":"2021-04-15T12:11:07.23823","indexId":"70219608","displayToPublicDate":"2021-03-11T07:07:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"A chemical and bio‐herbicide mixture increased exotic invaders, both targeted and non‐targeted, across a diversely invaded landscape after fire","docAbstract":"Invasive‐plant treatments often target a single or few species, but many landscapes are diversely invaded. Exotic annual grasses (EAGs) increase wildfires and degrade native perennial plant communities in cold‐desert rangelands, and herbicides are thus sprayed to inhibit EAG germination and establishment. We asked how EAG target and non‐target species responded to an herbicide mixture sprayed over a large, topographically diverse landscape after wildfire. We focused on how whole‐community and natural EAG‐pathogen treatment responses varied over years and physical properties of sites.
Sagebrush steppe of southwest Idaho, USA.
We monitored plant cover and diversity in 41 pairs of plots located inside or outside areas (486 ha total) treated with a combined aerial broadcast spray of pre‐emergent herbicide (imazapic) and weed‐suppressive bacteria (Pseudomonas fluorescens, “MB906”) to target EAGs after wildfires.
EAG cover and exotic species richness were initially less in treated plots but increased to levels similar to or greater than those of untreated plots by the third post‐treatment year. The EAG pathogen Ustilago bullata was not directly affected by the treatment. The treatment increased exotic perennial forb cover in all plots and exotic annual forb cover in cooler/wetter plots but reduced exotic annual forb cover in warmer/drier plots. Cover of the invasive biennial grass Poa bulbosa decreased more across study years in untreated than treated plots. Among natives, the treatment reduced perennial grass cover and annual forb presence but led to marginal increases in perennial forb cover and, on soils with less gravel, increased shrub presence.
A treatment targeting a single plant functional group did not achieve lasting success in these diversely invaded communities. Spraying alone did not release native perennials sufficiently to counteract the simultaneous release of secondary invaders and the return of target invaders. Planting or seeding may also be needed to achieve management goals.
","language":"English","publisher":"Wiley","doi":"10.1111/avsc.12574","usgsCitation":"Lazarus, B., and Germino, M.J., 2021, A chemical and bio‐herbicide mixture increased exotic invaders, both targeted and non‐targeted, across a diversely invaded landscape after fire: Applied Vegetation Science, v. 24, no. 2, e12574, 13 p., https://doi.org/10.1111/avsc.12574.","productDescription":"e12574, 13 p.","ipdsId":"IP-123369","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":436464,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CB7C62","text":"USGS data release","linkHelpText":"Post-fire vegetation cover, plant species diversity, and Ustilago bullata infection rates at Boise River Wildlife Management Area 2018-2019"},{"id":385109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.94970703125,\n 42.924251753870685\n ],\n [\n -114.76318359375,\n 42.924251753870685\n ],\n [\n -114.76318359375,\n 43.96119063892024\n ],\n [\n -115.94970703125,\n 43.96119063892024\n ],\n [\n -115.94970703125,\n 42.924251753870685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lazarus, Brynne E. 0000-0002-6352-486X","orcid":"https://orcid.org/0000-0002-6352-486X","contributorId":242732,"corporation":false,"usgs":true,"family":"Lazarus","given":"Brynne E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":3298,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew","email":"mgermino@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":814298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218822,"text":"70218822 - 2021 - Population density and stream-habitat relations of the Yellowcheek Darter (Nothonotus moorei) among the headwaters of the Little Red River in Arkansas","interactions":[],"lastModifiedDate":"2021-03-16T11:45:53.545635","indexId":"70218822","displayToPublicDate":"2021-03-11T06:37:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Population density and stream-habitat relations of the Yellowcheek Darter (Nothonotus moorei) among the headwaters of the Little Red River in Arkansas","docAbstract":"Nothonotus moorei (Yellowcheek Darter [YCD]) is an endangered species endemic to the headwaters of the Little Red River in north-central Arkansas. Population decline, habitat loss and fragmentation, and threats from land use and seasonal drought necessitate monitoring of population density and distribution to determine ecological and habitat associations. We evaluated YCD density and associated stream-habitat variables from 9 sites in the South Fork, Archey Fork, Middle Fork, and Beech Fork of the Little Red River from March to April 2018. Yellowcheek Darters were present at all 9 sites and 19 of 23 riffles sampled. Densities were generally comparable or higher than reported in previous studies, and we collected YCD at some sites at which they were considered previously extirpated, suggesting a rather stable population and evidence of recolonization of some sites since they were last surveyed. Yellowcheek Darter density was significantly negatively related to substrate embeddedness, and the consistent relation to embeddedness in this study and other studies suggest that this species is vulnerable to sedimentation.
","language":"English","publisher":"BioOne","doi":"10.1656/058.020.0124","usgsCitation":"Driver, L., and Justus, B., 2021, Population density and stream-habitat relations of the Yellowcheek Darter (Nothonotus moorei) among the headwaters of the Little Red River in Arkansas: Southeastern Naturalist, v. 20, no. 1, p. 227-244, https://doi.org/10.1656/058.020.0124.","productDescription":"18 p.","startPage":"227","endPage":"244","ipdsId":"IP-095659","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":384400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Little Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.65869140625,\n 35.37113502280101\n ],\n [\n -91.56005859375,\n 35.37113502280101\n ],\n [\n -91.56005859375,\n 35.96022296929667\n ],\n [\n -92.65869140625,\n 35.96022296929667\n ],\n [\n -92.65869140625,\n 35.37113502280101\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Driver, Lucas 0000-0003-2549-1849","orcid":"https://orcid.org/0000-0003-2549-1849","contributorId":219176,"corporation":false,"usgs":true,"family":"Driver","given":"Lucas","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812290,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Justus, Billy 0000-0002-3458-9656 bjustus@usgs.gov","orcid":"https://orcid.org/0000-0002-3458-9656","contributorId":202148,"corporation":false,"usgs":true,"family":"Justus","given":"Billy","email":"bjustus@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812291,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218752,"text":"ofr20211026 - 2021 - Expected warning times from the ShakeAlert earthquake early warning system for earthquakes in the Pacific Northwest","interactions":[],"lastModifiedDate":"2021-04-07T01:36:23.477755","indexId":"ofr20211026","displayToPublicDate":"2021-03-10T15:49:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1026","displayTitle":"Expected Warning Times from the ShakeAlert® Earthquake Early Warning System for Earthquakes in the Pacific Northwest","title":"Expected warning times from the ShakeAlert earthquake early warning system for earthquakes in the Pacific Northwest","docAbstract":"The ShakeAlert® earthquake early warning system has been live since October 2019 for the testing of public alerting to mobile devices in California and will soon begin testing this modality in Oregon and Washington. The Pacific Northwest presents new challenges and opportunities for ShakeAlert owing to the different types of earthquakes that occur in the Cascadia subduction zone. Many locations in the Pacific Northwest are expected to experience shaking from shallow crustal earthquakes (similar to those in California), earthquakes that occur deep within the subducted slab, and large megathrust earthquakes that occur primarily offshore. The different geometries and maximum magnitudes associated with these types of earthquakes lead to a range of warning times that are possible between when the initial ShakeAlert Message is issued and when a user experiences strong shaking. After an earthquake begins, the strategy of the ShakeAlert system for public alerting is to warn people who are located close enough to the fault that the system estimates they will experience at least weak to moderate shaking. By alerting the public at these low levels of expected shaking, it is possible to provide sufficient warning times for some users to take protective actions before strong shaking begins. In this study, we present an analysis of past ShakeAlert Messages as well as simulations of historical earthquakes and potential future Cascadia earthquakes to quantify the range of warning times that users who experience strong or worse shaking are likely to receive. Additional applications for ShakeAlert involve initiation of automatic protective actions prior to the onset of shaking, such as slowing trains, shutting water supplies, and opening firehouse doors, which are beyond the scope of this paper. Users in the Pacific Northwest should expect that the majority of alerts they receive will be from shallow crustal and intraslab earthquakes. In these cases, users will only have a few seconds of warning before strong shaking begins. This remains true even during infrequent, offshore great (magnitude ≥8) megathrust earthquakes, where warning times will generally range from seconds to tens of seconds, depending on the user’s location and the intensity of predicted shaking that a user chooses to be alerted for, with the longest warning times of 50–80 seconds possible only for users located at considerable distance from the epicenter. ShakeAlert thus requires short, readily understood alerts stating that earthquake shaking is imminent and suggesting protective actions users should take. Extensive education and outreach efforts that emphasize the need to take actions quickly will be required for ShakeAlert to successfully reduce injuries and losses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211026","usgsCitation":"McGuire, J.J., Smith, D.E., Frankel, A.D., Wirth, E.A., McBride, S.K., and de Groot, R.M., 2021, Expected warning times from the ShakeAlert earthquake early warning system for earthquakes in the Pacific Northwest (ver. 1.1, March 24, 2021): U.S. Geological Survey Open-File Report 2021–1026, 37 p., https://doi.org/10.3133/ofr20211026.","productDescription":"v, 37 p.","onlineOnly":"Y","ipdsId":"IP-125131","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":384638,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1026/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":384281,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1026/ofr20211026_v1.1.pdf","text":"Report","size":"12 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\"}}]}","edition":"Version 1.0: Marhc 10, 2021; Version 1.1: March 24, 2021","contact":"Earthquake Science Center—Menlo Park, Calif. Office
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The sagebrush (Artemisia spp.) biome, its wildlife, and the services and benefits it provides people and local communities are at risk. Development in the sagebrush biome, for many purposes, has resulted in multiple and often cumulative negative impacts. These impacts, ranging from simple habitat loss to complex, interactive changes in ecosystem function, continue to accelerate even as the need grows for the resources provided by this biome. This “Sagebrush Conservation Strategy—Challenges to Sagebrush Conservation,” is an overview and assessment of the challenges facing land managers and landowners in conserving sagebrush ecosystems. This strategy is intended to provide guidance so that the unparalleled collaborative efforts to conserve the iconic greater sage-grouse (Centrocercus urophasianus) by State and Federal agencies, Tribes, academia, nongovernmental organizations, and stakeholders can be expanded to the entire sagebrush biome to benefit the people and wildlife that depend on this ecosystem. This report is organized into 3 parts.
“Part I. Importance of the Sagebrush Biome to People and Wildlife” introduces the biome and a subset of the more than 350 species of plants and animals associated with sagebrush for which there is some level of conservation concern. These include several sagebrush obligates that have been petitioned for listing under the Endangered Species Act of 1973 (16 U.S.C. 1531 et seq.), including greater sage-grouse, Gunnison sage-grouse (C. minimus; listed as threatened), and pygmy rabbit (Brachylagus idahoensis). Other sagebrush-dependent species, such as pronghorn (Antilocapra americana) and mule deer (Odocoileus hemionus), have experienced significant population declines.
“Part II. Change Agents in the Sagebrush Biome—Extent, Impacts, and Effort to Address Them” is an overview of the variety of change agents that are causing the continued loss and degradation of sagebrush. Topics covered include altered fire regimes, invasive plant species, conifer expansion, overabundant free-roaming equids, and human land uses, including energy development, cropland conversion, infrastructure, and improper livestock grazing. Climate changes, including warmer temperatures and altered amounts and timing of precipitation, have and will likely increasingly compound negative effects to sagebrush ecosystems from all these threats.
“Part III. Current Conservation Paradigm and Other Conservation Needs for Sagebrush” begins with an overview of how sage-grouse conservation, and the associated efforts and collaborations, may be able to address threats to and restoring degraded sagebrush and habitat for other sagebrush-dependent and -associated species. Meeting conservation goals for sage-grouse, mule deer, pygmy rabbits, and other sagebrush-associated wildlife will require extensive restoration of sagebrush communities already converted or degraded by the change agents outlined in Part II of this report. Concepts, considerations, techniques for restoration, and adaptive management and monitoring are discussed to help set the stage for potential strategies to improve conditions throughout the sagebrush biome. Communication, outreach, and engagement can enhance grassroots conservation efforts and build the next generation of managers, practitioners, scientists, and communicators who will care for the sagebrush ecosystem and stimulate or sustain public participation in sagebrush conservation issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201125","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies, the Bureau of Land Management, and the U.S. Fish and Wildlife Service","usgsCitation":"Remington, T.E., Deibert, P.A., Hanser, S.E., Davis, D.M., Robb, L.A., and Welty, J.L., 2021, Sagebrush conservation strategy—Challenges to sagebrush conservation: U.S. Geological Survey Open-File Report 2020–1125, 327 p., https://doi.org/10.3133/ofr20201125.","productDescription":"xxxiv, 327 p.","onlineOnly":"N","ipdsId":"IP-112519","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":384279,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1125/ofr20201125.pdf","text":"Report","size":"35.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1125"},{"id":384278,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1125/coverthb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nebraska, Nevada, New Mexico, North Dakota, South Dakota, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.0703125,\n 46.31658418182218\n ],\n [\n -119.00390625,\n 48.04870994288686\n ],\n [\n -120.84960937499999,\n 48.86471476180277\n ],\n [\n -121.640625,\n 46.6795944656402\n ],\n [\n -121.81640624999999,\n 44.15068115978094\n ],\n [\n -121.640625,\n 41.77131167976407\n ],\n [\n -121.201171875,\n 39.977120098439634\n ],\n [\n -119.88281249999999,\n 38.685509760012\n ],\n [\n -118.564453125,\n 36.66841891894786\n ],\n [\n -116.630859375,\n 34.52466147177172\n ],\n [\n -115.224609375,\n 35.60371874069731\n ],\n [\n -114.345703125,\n 36.73888412439431\n ],\n [\n -112.5,\n 36.1733569352216\n ],\n [\n -110.74218749999999,\n 34.88593094075317\n ],\n [\n -108.10546875,\n 33.50475906922609\n ],\n [\n -105.46875,\n 34.016241889667015\n ],\n [\n -103.798828125,\n 39.095962936305476\n ],\n [\n -104.67773437499999,\n 39.842286020743394\n ],\n [\n -104.501953125,\n 41.244772343082076\n ],\n [\n -103.53515625,\n 41.96765920367816\n ],\n [\n -103.18359375,\n 42.293564192170095\n ],\n [\n -103.71093749999999,\n 42.94033923363181\n ],\n [\n -104.4140625,\n 43.89789239125797\n ],\n [\n -104.4140625,\n 44.902577996288876\n ],\n [\n -101.865234375,\n 45.27488643704891\n ],\n [\n -101.77734374999999,\n 46.13417004624326\n ],\n [\n -101.953125,\n 47.45780853075031\n ],\n [\n -102.12890625,\n 48.3416461723746\n ],\n [\n -104.150390625,\n 47.69497434186282\n ],\n [\n -105.556640625,\n 47.635783590864854\n ],\n [\n -106.787109375,\n 47.69497434186282\n ],\n [\n -106.5234375,\n 48.516604348867475\n ],\n [\n -107.138671875,\n 48.86471476180277\n ],\n [\n -111.796875,\n 48.86471476180277\n ],\n [\n -112.06054687499999,\n 47.338822694822\n ],\n [\n -112.67578124999999,\n 47.21956811231547\n ],\n [\n -113.5546875,\n 48.10743118848039\n ],\n [\n -114.43359375,\n 47.989921667414194\n ],\n [\n -113.73046875,\n 46.31658418182218\n ],\n [\n -114.60937499999999,\n 46.255846818480315\n ],\n [\n -117.0703125,\n 46.31658418182218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
The impacts of wind power development on bat and bird populations are commonly assessed by estimating the number of fatalities at wind power facilities through post-construction monitoring (PCM) studies. Standard methodology involves periodic carcass searches on plots beneath turbines (Strickland et al. 2011, US Fish and Wildlife Service 2012). The resulting counts are adjusted to compensate for bias due to imperfect carcass detection by searchers, removal of carcasses by scavengers or other processes (Korner-Nievergelt et al. 2011), and carcasses that may have fallen outside of searched areas. To account for the bias in counts due to imperfect detection and carcass removal, investigators typically conduct bias trial experiments to inform models of carcass detection probability. Many different estimators have been proposed that combine information about the bias trial experiments to estimate a detection probability for carcasses (g) and ultimately obtain an estimate of total mortality (M). The two estimators that have seen the most widespread use in North America recently are the Huso (Huso 2011, Huso et al. 2012) and Shoenfeld (Shoenfeld 2004; also called the Erickson estimator) estimators. GenEst (Dalthorp et al. 2018a, 2018b, 2018c) is the newest statistical estimator to become available and was designed to improve upon the Huso and Shoenfeld estimators by generalizing the key assumptions in both, and to improve comparability among new PCM studies. In addition to relaxing some of the assumptions inherent to the Huso and Shoenfeld estimators, GenEst uses a parametric bootstrap applied to a novel approach to variance estimation (Madsen et al. 2019).
The current study was undertaken to document the performance of GenEst relative to the Huso and Shoenfeld estimators. We took a simulation approach to the study because simulation data provides the basis to compare mortality estimators under conditions where the “truth” is known. The estimators were compared on three metrics: 1) bias—the tendency of an estimator to over- or under-estimate actual mortality, 2) precision—the ability of an estimator to constrain an estimate to a narrow range (measured here as the width of a 90% confidence interval [CI] around the point estimate divided by the true, known mortality), and 3) CI coverage—the probability a CI with a specified level of confidence actually includes the true level of mortality.
Although our simulations were conceived and designed—and are discussed—with respect to wind power facilities, it is important to note that the estimators and results discussed here are relevant to any post-construction fatality monitoring study that may occur (such as at solar facilities) where detection is imperfect. Although our study treats the problem of mortality estimation when detection is imperfect, it is also important to note that all of the estimators considered here are Horvitz-Thompson (Horvitz and Thompson 1952) style estimators, that is, none are designed to estimate the mortality of rare species as might be necessary under an Incidental Take Permit. The Evidence of Absence estimator (Dalthorp et al. 2017) is still the most appropriate statistical tool for rare event estimation.
The simulations cover a broad range of conditions that may occur in field studies and complete results are presented without commentary in the appendix. The main body of this report does not provide a comprehensive treatment of our results; rather, we try to identify some of the more important differences among the estimators and some conditions under which reliable mortality estimates are especially challenging.
","language":"English","publisher":"American Wind Wildlife Institute","usgsCitation":"Rabie, P., Riser-Espinoza, D., Studyvin, J., Dalthorp, D., and Huso, M., 2021, Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators: AWWI Technical Report, 29 p.","productDescription":"29 p.","ipdsId":"IP-119710","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":385197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385196,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://awwi.org/resources/genest/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rabie, Paul","contributorId":248699,"corporation":false,"usgs":false,"family":"Rabie","given":"Paul","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riser-Espinoza, Daniel","contributorId":248700,"corporation":false,"usgs":false,"family":"Riser-Espinoza","given":"Daniel","email":"","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Studyvin, Jared","contributorId":248701,"corporation":false,"usgs":false,"family":"Studyvin","given":"Jared","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalthorp, Daniel 0000-0002-4815-6309 ddalthorp@usgs.gov","orcid":"https://orcid.org/0000-0002-4815-6309","contributorId":4902,"corporation":false,"usgs":true,"family":"Dalthorp","given":"Daniel","email":"ddalthorp@usgs.gov","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":809638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":809639,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220298,"text":"70220298 - 2021 - Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management","interactions":[],"lastModifiedDate":"2021-05-03T15:39:09.580626","indexId":"70220298","displayToPublicDate":"2021-03-10T10:33:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management","docAbstract":"The St. Clair-Detroit River System contains a world-class Great Lakes muskellunge (Esox masquinongy) fishery that has avoided the declines observed in many Great Lakes muskellunge populations. Muskellunge are an upper trophic level predator, and therefore a naturally low-density species. Limited fishery-independent data exist on which to base management decisions. To remedy this, we initiated an acoustic telemetry study in May of 2016, in collaboration with the Great Lakes Acoustic Telemetry Observation System. Our objective was to describe patterns of movement of muskellunge in this large and open system to better understand their spatial ecology. We acoustically tagged 133 muskellunge in the Detroit River and Lake St. Clair, and movements of 58 fish that passed our data quality control screens were analyzed. We utilized mixed modelling to assess the effects of sex, length, release location, and season on daily movement rates. We found that movement rates only differed among seasons, with highest movement rates occurring in the fall and lowest movement rates in the winter. Muskellunge tagged at different locations exhibited distinct residency patterns, and fish frequently crossed jurisdictional and waterbody boundaries. Ultimately our study highlights the scope and patterns of muskellunge movement in a large, unimpounded system and demonstrates that management of these fish would benefit from consideration of their full distribution covering multiple management jurisdictions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.12.006","usgsCitation":"Hessenauer, J., Harris, C., Marklevitz, S., Faust, M.D., Thorn, M.W., Utrup, B., and Hondorp, D.W., 2021, Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management: Journal of Great Lakes Research, v. 47, no. 2, p. 475-485, https://doi.org/10.1016/j.jglr.2020.12.006.","productDescription":"11 p.","startPage":"475","endPage":"485","ipdsId":"IP-121684","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St. Clair – Detroit River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.16650390625,\n 42.039094188385945\n ],\n [\n -82.99072265625,\n 42.285437007491545\n ],\n [\n -82.37548828125,\n 42.28746890196628\n ],\n [\n -82.42218017578125,\n 42.5733097370664\n ],\n [\n -82.38372802734375,\n 43.022721607058344\n ],\n [\n -82.496337890625,\n 43.022721607058344\n ],\n [\n -82.63641357421875,\n 42.7349091465156\n ],\n [\n -82.95501708984375,\n 42.64608143458068\n ],\n [\n -83.045654296875,\n 42.49235259142821\n ],\n [\n -83.232421875,\n 42.28340504748081\n ],\n [\n -83.22418212890625,\n 42.071723466810774\n ],\n [\n -83.16650390625,\n 42.039094188385945\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hessenauer, Jan-Michael","contributorId":257795,"corporation":false,"usgs":false,"family":"Hessenauer","given":"Jan-Michael","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Cleyo","contributorId":257796,"corporation":false,"usgs":false,"family":"Harris","given":"Cleyo","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marklevitz, Stephen","contributorId":257797,"corporation":false,"usgs":false,"family":"Marklevitz","given":"Stephen","email":"","affiliations":[{"id":52125,"text":"Ontario Ministry of Natural Resources & Forestry","active":true,"usgs":false}],"preferred":false,"id":815042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faust, Matthew D.","contributorId":257798,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorn, Michael W.","contributorId":257799,"corporation":false,"usgs":false,"family":"Thorn","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":52125,"text":"Ontario Ministry of Natural Resources & Forestry","active":true,"usgs":false}],"preferred":false,"id":815044,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Utrup, Brad","contributorId":257800,"corporation":false,"usgs":false,"family":"Utrup","given":"Brad","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815045,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815046,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219076,"text":"70219076 - 2021 - Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams","interactions":[],"lastModifiedDate":"2021-05-27T13:21:52.551307","indexId":"70219076","displayToPublicDate":"2021-03-10T10:18:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams","docAbstract":"Improved analytical methods can quantify hundreds of pesticide transformation products (TPs), but understanding of TP occurrence and potential toxicity in aquatic ecosystems remains limited. We quantified 108 parent pesticides and 116 TPs in more than 3 700 samples from 442 small streams in mostly urban basins across five major regions of the United States. TPs were detected nearly as frequently as parents (90 and 95% of streams, respectively); 102 TPs were detected at least once and 28 were detected in >20% samples in at least one region—TPs of 9 herbicides, 2 fungicides (chlorothalonil and thiophanate-methyl), and 1 insecticide (fipronil) were the most frequently detected. TPs occurred commonly during baseflow conditions, indicating chronic environmental TP exposures to aquatic organisms and the likely importance of groundwater as a TP source. Hazard quotients based on acute aquatic-life benchmarks for invertebrates and nonvascular plants and vertebrate-centric molecular endpoints (sublethal effects) quantify the range of the potential contribution of TPs to environmental risk and highlight several TP exposure–response data gaps. A precautionary approach using equimolar substitution of parent benchmarks or endpoints for missing TP benchmarks indicates that potential aquatic effects of pesticide TPs could be underestimated by an order of magnitude or more.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06625","usgsCitation":"Mahler, B., Nowell, L.H., Sandstrom, M.W., Bradley, P., Romanok, K., Konrad, C., and Van Metre, P., 2021, Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams: Environmental Science and Technology, v. 55, no. 8, p. 4740-4752, https://doi.org/10.1021/acs.est.0c06625.","productDescription":"13 p.","startPage":"4740","endPage":"4752","ipdsId":"IP-122426","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science 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bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":812673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":812674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Konrad, Christopher 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":220231,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812677,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Metre, Peter 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":255624,"corporation":false,"usgs":false,"family":"Van Metre","given":"Peter","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":812678,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70222351,"text":"70222351 - 2021 - Commentary: The role of geodetic algorithms for earthquake early warning in Cascadia","interactions":[],"lastModifiedDate":"2021-07-22T13:59:59.372252","indexId":"70222351","displayToPublicDate":"2021-03-10T08:55:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Commentary: The role of geodetic algorithms for earthquake early warning in Cascadia","docAbstract":"The ShakeAlert earthquake early warning (EEW) system issues public alerts in California and will soon extend to Oregon and Washington. The Cascadia subduction zone presents significant new challenges and opportunities for EEW. Initial publications suggested that EEW algorithms based on Global Navigation Satellite System (GNSS) data could provide improved warning for intraslab events and dramatically improved warning for offshore megathrust events, both of which contribute significantly to hazard in Cascadia. We find that some expectations in these publications were unrealistic, and we demonstrate that in general geodetic algorithms would not produce timely warnings for intraslab events nor warning times of two minutes or more for severe shaking from megathrust earthquakes. Nonetheless, lessons from recent earthquakes in Japan and California, for which alerts from seismic algorithms suffered from magnitude saturation and high data latencies, demonstrate the urgent need for rigorous testing of geodetic EEW as a potential complement to seismic EEW.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL092324","usgsCitation":"McGuire, J., Minson, S.E., Murray, J.R., and Brooks, B.A., 2021, Commentary: The role of geodetic algorithms for earthquake early warning in Cascadia: Geophysical Research Letters, v. 48, no. 6, e2020GL092324, 8 p., https://doi.org/10.1029/2020GL092324.","productDescription":"e2020GL092324, 8 p.","ipdsId":"IP-124207","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":453140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/246a522b3bb74e70a3b0d13a73e6fca6","text":"Publisher Index Page"},{"id":387379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.84765625,\n 37.16031654673677\n ],\n [\n -119.88281249999999,\n 37.16031654673677\n ],\n [\n -119.88281249999999,\n 48.748945343432936\n ],\n [\n -128.84765625,\n 48.748945343432936\n ],\n [\n -128.84765625,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819731,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219129,"text":"70219129 - 2021 - Partitioning and transformation of organic and inorganic phosphorus among dissolved, colloidal and particulate phases in a hypereutrophic freshwater estuary","interactions":[],"lastModifiedDate":"2021-03-25T13:25:59.68269","indexId":"70219129","displayToPublicDate":"2021-03-10T08:24:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Partitioning and transformation of organic and inorganic phosphorus among dissolved, colloidal and particulate phases in a hypereutrophic freshwater estuary","docAbstract":"Phosphorus (P) loadings to the Great Lakes have been regulated for decades, but re-eutrophication and seasonal hypoxia have recently been increasingly reported. It is of paramount importance to better understand the fate, transformation, and biogeochemical cycling processes of different P species across the river-lake interface. We report here results on chemical speciation of P in the seasonally hypoxic Fox River-Green Bay system and variations in sources and partitioning of P species along the aquatic continuum. During midsummer when productivity is generally high, phosphate and dissolved organic P (DOP) were the major species in river water while particulate-organic-P predominated in open bay waters, showing a dynamic change in the chemical speciation of P along the river-bay transect with active transformations between inorganic and organic P and between colloidal and particulate phases. Colloidal organic P (COP, >1 kDa) comprised 33‒65% of the bulk DOP, while colloidal inorganic P was generally insignificant and undetectable especially in open bay water. Sources of COP changed from mainly allochthonous in the Fox River, having mostly smaller sized colloids (1–3 kDa) and a lower organic carbon to phosphorus (C/P) ratio, to predominantly autochthonous in open bay waters with larger sized colloids (>10 kDa) and a higher organic C/P ratio. The observed high apparent distribution coefficients (Kd) of P between dissolved and particulate phases and high-abundant autochthonous colloidal and particulate organic P in the hypereutrophic environment suggest that, in addition to phosphate, colloidal/particulate organic P may play a critical role in the biogeochemical cycling of P and the development of seasonal hypoxia.
The Argentine Black and White Tegu (Salvator merianae, formerly Tupinambis merianae) is a large lizard from South America. Now established and invasive in southern Florida, and it poses threats to populations of many native species. Models suggest much of the southern United States may contain suitable temperature regimes for this species, yet there is considerable uncertainty regarding either the potential for range expansion northward out of tropical and subtropical zones or the potential for the species establishing elsewhere following additional independent introductions. We evaluated survival, body temperature, duration and timing of winter dormancy, and health of wild-caught tegus from southern Florida held in semi-natural enclosures for over a year in Auburn, Alabama (> 900 km northwest of capture location). Nine of twelve lizards emerged from winter dormancy and seven survived the greater-than-one-year duration of the study. Average length of dormancy (176 d) was greater than that reported in the native range or for invasive populations in southern Florida and females remained dormant longer than males. Tegus grew rapidly throughout the study and the presence of sperm in the testes of males and previtellogenic or early vitellogenic follicles in female ovaries at the end of our study suggest the animals would have been capable of reproduction the following spring. The survival and overall health of the majority of adult tegus in our study suggests weather and climate patterns are unlikely to prevent survival following introduction in many areas of the United States far from their current invasive range.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0245877","usgsCitation":"Goetz, S.M., Steen, D.A., Miller, M.A., Guyer, C., Kottwitz, J., Roberts, J.F., Blankenship, E., Pearson, P.R., Warner, D., and Reed, R., 2021, Argentine Black and White Tegu (Salvator merianae) can survive the winter under semi-natural conditions well beyond their current invasive range: PLoS ONE, v. 16, no. 3, e0245877, 17 p., https://doi.org/10.1371/journal.pone.0245877.","productDescription":"e0245877, 17 p.","ipdsId":"IP-120439","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453143,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245877","text":"Publisher Index Page"},{"id":436466,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YLV6IF","text":"USGS data release","linkHelpText":"Temperature data for tegu lizard study in Auburn, 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experienced epizootics caused by ingestion of prey that accumulated a botulinum neurotoxin produced by the anaerobic bacterium Clostridium botulinum (avian botulism; Type C). Waterbird carcasses, necrophagous flies, and their larvae initiate and spread avian botulism, a food-borne paralytic disease lethal to waterbirds. Each new carcass has potential to develop toxin-accumulating necrophagous vectors amplifying outbreaks and killing hundreds of endangered waterbirds. Early carcass removal is an effective mitigation strategy for preventing avian intoxication, toxin concentration in necrophagous and secondary food webs, and reducing the magnitude of epizootics. However, rapid detection of carcasses can be problematic and labor intensive. Therefore, we tested a new method using scent detection canines for avian botulism surveillance on Kaua'i Island. During operational surveillance and a randomized double-blind field trial, trained detector canines with experienced field handlers improved carcass detection probability, especially in dense vegetation. Detector canines could be combined with conventional surveillance to optimize search strategies for carcass removal and are a useful tool to reduce risks of the initiation and propagation of avian botulism.
","language":"English","publisher":"Wiley","doi":"10.1111/csp2.397","usgsCitation":"Reynolds, M.H., Johnson, K.N., Schvaneveldt, E., Dewy, D.L., Uyehara, K.J., and Hess, S.C., 2021, Efficacy of detection canines for avian botulism surveillance and mitigation: Conservation Science and Practice, v. 3, no. 6, e397, 18 p., https://doi.org/10.1111/csp2.397.","productDescription":"e397, 18 p.","ipdsId":"IP-114784","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":488913,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.397","text":"Publisher Index Page"},{"id":436467,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C4N47X","text":"USGS data release","linkHelpText":"Kaua'i Avian Botulism Surveillance Using Detector Canines 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\"}}]}","volume":"3","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Reynolds, Michelle H 0000-0001-7253-8158","orcid":"https://orcid.org/0000-0001-7253-8158","contributorId":245720,"corporation":false,"usgs":false,"family":"Reynolds","given":"Michelle","email":"","middleInitial":"H","affiliations":[{"id":49297,"text":"USGS PIERC (formerly)","active":true,"usgs":false}],"preferred":false,"id":818711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Kyoko N","contributorId":260779,"corporation":false,"usgs":false,"family":"Johnson","given":"Kyoko","email":"","middleInitial":"N","affiliations":[{"id":52675,"text":"Country Canines, LLC","active":true,"usgs":false}],"preferred":false,"id":818716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schvaneveldt, Eleni","contributorId":260778,"corporation":false,"usgs":false,"family":"Schvaneveldt","given":"Eleni","email":"","affiliations":[{"id":39456,"text":"USGS-PIERC (formerly)","active":true,"usgs":false}],"preferred":false,"id":818715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewy, Dan L","contributorId":260777,"corporation":false,"usgs":false,"family":"Dewy","given":"Dan","email":"","middleInitial":"L","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":818714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Uyehara, Kim J","contributorId":260776,"corporation":false,"usgs":false,"family":"Uyehara","given":"Kim","email":"","middleInitial":"J","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":818712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hess, Steven C.","contributorId":176679,"corporation":false,"usgs":false,"family":"Hess","given":"Steven","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":818713,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220157,"text":"70220157 - 2021 - Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass","interactions":[],"lastModifiedDate":"2021-04-22T14:31:36.304212","indexId":"70220157","displayToPublicDate":"2021-03-09T09:29:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":861,"text":"Aquatic Botany","active":true,"publicationSubtype":{"id":10}},"title":"Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass","docAbstract":"Levels of submersed aquatic vegetation (SAV) are commonly assessed using a modified garden rake. However, the utility of the rake sampling method relative to methods that are typically viewed as more definitive (and expensive) such as snorkeling and coring remains a matter of debate. This study explores whether probabilities of species detections for four SAV species varied among sampling units in a rake-biomass study and, if so, whether such variation reflected variation in species abundance. Variation in detection probabilities, when unaddressed, may yield biased estimators of percent frequency of occurrence (“occupancy”) and of occurrence-habitat associations. Biomass-driven variation in detection probabilities is important because such variation may not be explainable using covariates typically measured when sampling using the rake method. This study found substantial among-unit variation in detection probabilities, with majorities of that variation on the logit or modeling scale being associated with biomass but not with the non-biomass covariates substrate type, water depth and day of study. The study closes by exploring sampling protocols and modeling methods that may yield improved SAV occupancy estimates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquabot.2021.103375","usgsCitation":"Gray, B.R., 2021, Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass: Aquatic Botany, v. 171, 103375, 7 p., https://doi.org/10.1016/j.aquabot.2021.103375.","productDescription":"103375, 7 p.","ipdsId":"IP-123221","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436468,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM11FY","text":"USGS data release","linkHelpText":"SAS Code: Estimating probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass."},{"id":385277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"171","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":814599,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237781,"text":"70237781 - 2021 - Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","interactions":[],"lastModifiedDate":"2022-10-24T14:38:38.267353","indexId":"70237781","displayToPublicDate":"2021-03-09T09:28:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","title":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","docAbstract":"African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109499","usgsCitation":"Diaz, S.G., DeAngelis, D.L., Gaines, M.S., Purdon, A., Mole, M.A., and van Aarde, R.J., 2021, Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement: Ecological Modelling, v. 447, 109499, 27 p., https://doi.org/10.1016/j.ecolmodel.2021.109499.","productDescription":"109499, 27 p.","ipdsId":"IP-124073","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Botswana, Mozambique, South Africa","otherGeospatial":"Chobe National Park, Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.26908811865576,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -17.811236528794907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.337765815705552,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -22.153479707969097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"447","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Stephanie G.","contributorId":212228,"corporation":false,"usgs":false,"family":"Diaz","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":855618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaines, Michael S.","contributorId":298435,"corporation":false,"usgs":false,"family":"Gaines","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Purdon, Andrew","contributorId":298436,"corporation":false,"usgs":false,"family":"Purdon","given":"Andrew","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mole, Michael A.","contributorId":298438,"corporation":false,"usgs":false,"family":"Mole","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Aarde, Rudi J.","contributorId":298440,"corporation":false,"usgs":false,"family":"van Aarde","given":"Rudi","email":"","middleInitial":"J.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855622,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219424,"text":"70219424 - 2021 - UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","interactions":[],"lastModifiedDate":"2021-04-05T13:40:13.157647","indexId":"70219424","displayToPublicDate":"2021-03-09T08:18:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","docAbstract":"Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13051036","usgsCitation":"Belmonte, A., Sankey, T.T., Biedermann, J., Bradford, J., Goetz, S.J., and Kolb, T., 2021, UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence: Remote Sensing, v. 13, no. 5, 1036, 20 p., https://doi.org/10.3390/rs13051036.","productDescription":"1036, 20 p.","ipdsId":"IP-126824","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13051036","text":"Publisher Index Page"},{"id":384871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.78863525390625,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 34.5235300339023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":813495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":813496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biedermann, Joel","contributorId":256936,"corporation":false,"usgs":false,"family":"Biedermann","given":"Joel","email":"","affiliations":[{"id":51904,"text":"USDA Agricultural Research Service Southwest Watershed Research Center, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":813497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott J 0000-0002-6326-4308","orcid":"https://orcid.org/0000-0002-6326-4308","contributorId":210734,"corporation":false,"usgs":false,"family":"Goetz","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":813499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolb, Thomas","contributorId":174381,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","affiliations":[],"preferred":false,"id":813500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221171,"text":"70221171 - 2021 - Characterizing stress orientations in southern Kansas","interactions":[],"lastModifiedDate":"2021-06-04T12:44:46.202587","indexId":"70221171","displayToPublicDate":"2021-03-09T07:38:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing stress orientations in southern Kansas","docAbstract":"Induced seismicity predominantly occurs along faults that are optimally oriented to the local principal compressive stress direction, and the characterization of these stress orientations is an important component of understanding seismic hazards. The seismicity rate in southern Kansas rapidly increased in 2013 primarily due to the disposal of large volumes of wastewater into the Arbuckle Group. Previously, local stress orientations in this area were poorly constrained, which limited our understanding of the complex faulting and diverse earthquake mechanisms in this region. We use shear‐wave splitting and focal mechanism inversion techniques to create multiple, independent estimates of maximum horizontal stress directions (
Montane meadows are areas of high biodiversity and provide many important ecosystem services; however, degradation of 40–60% of these habitats in the Sierra Nevada region of California has left many of these areas impaired. The “pond-and-plug” meadow-restoration technique is 1 type of treatment implemented to restore montane meadows. The objectives of this technique are to re-water the meadow and promote downstream flow by increasing the water-table elevation and providing additional water storage that will promote the growth of mesic and hydric vegetation that maintains and stabilizes stream channels. However, aquatic habitat and the composition and functioning of aquatic communities in these systems post-treatment are poorly documented or understood. We evaluated: (1) fish habitat, community composition, and relative abundance among recently created ponds spanning the range of pond habitats; (2) seasonal movement and survival of fish within and among ponds; and (3) food web structure in ponds. We documented over-summer and winter survival in the fish community and short-distance movement by 1 species occupying the ponds. Mark-recapture data suggest that all fish species present are capable of surviving both summer and winter conditions when pond conditions could be most limiting. Food web structure among intensively sampled ponds was similar, with overlapping isotopic niche width for dominant taxa. However, basal resource diversity (BRD) varied among ponds, with those having higher macrophyte cover also showing greater BRD. Our findings suggest that pond-and-plug techniques can provide habitat for native fishes that are able to tolerate departures from the species thermal and dissolved oxygen optima. Future meadow treatments could benefit from short-term restoration techniques such as pond-and-plug to allow for longer-term processes to influence meadow condition over time.
Tropical mountains harbor globally significant levels of biodiversity and endemism. Climate change threatens many tropical montane species, yet little research has assessed the effects of climate change on the demographic rates of tropical species, particularly in the Afrotropics. Here, we report on the demographic rates of 21 Afrotropical bird species over 30 years in montane forests in Tanzania. We used mark–recapture analyses to model rates of population growth, recruitment, and apparent survival as functions of annual mean temperature and annual precipitation. For over one‐half of focal species, decreasing population growth rates were associated with increasing temperature. Due to the trend in temperature over time, we substituted a time covariate for the temperature covariate in top‐ranked population growth rate models. Temperature was a better explanatory covariate than time for 6 of the 12 species, or 29% of all focal species. Population growth rates were also lower for species found further below their elevational midpoint and for smaller‐bodied species. Changes in population growth rates were more closely tied to changes in recruitment than to changes in apparent survival. There were no consistent associations between demographic rates and precipitation. This study demonstrates temperature‐associated demographic impacts for 6 (29%) of 21 focal species in an Afrotropical understory bird community and highlights the need to incorporate the impacts of climate change on demographic rates into conservation planning across the tropics.
We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR1-2, to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI's performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105030","usgsCitation":"Rad, A.M., Kreitler, J.R., and Sadegh, M., 2021, Augmented normalized difference water index for improved monitoring of surface water: Environmental Modeling and Software, v. 140, 105030, 15 p., https://doi.org/10.1016/j.envsoft.2021.105030.","productDescription":"105030, 15 p.","ipdsId":"IP-121583","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":453156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105030","text":"Publisher Index Page"},{"id":385213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rad, Arash Modaresi","contributorId":257536,"corporation":false,"usgs":false,"family":"Rad","given":"Arash","email":"","middleInitial":"Modaresi","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":814522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadegh, Mojitaba","contributorId":257538,"corporation":false,"usgs":false,"family":"Sadegh","given":"Mojitaba","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814523,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255557,"text":"70255557 - 2021 - Global Changes in 20-year, 50-year and 100-year River Floods","interactions":[],"lastModifiedDate":"2024-06-24T11:35:50.509067","indexId":"70255557","displayToPublicDate":"2021-03-09T06:30:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Global Changes in 20-year, 50-year and 100-year River Floods","docAbstract":"Concepts like the 100-year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20-, 50-, and 100-year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20-, 50-, and 100-year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20-/50-year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100-year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.
Bird conservation as an endeavor engages a broad range of partners and a coordinated effort across State and Federal agencies, nongovernment organizations, universities and, at times, international partnerships. To understand information needs and respond to the many challenges in bird conservation, U.S. Geological Survey (USGS) scientists participate in Flyway committees, on Joint Venture boards and working groups, in professional organizations, and in other conservation partnerships. These activities connect USGS scientists to conservation partners with whom they work to address substantial challenges. More than one hundred USGS scientists are dedicated to the scientific study of migratory birds.
This report presents the current (2021) representative breadth of activities of USGS scientists supporting the conservation and management of migratory birds. Ninety USGS scientists contributed to the project descriptions and other information detailing the work of the USGS. The science herein is organized and presented thematically by research strengths and by management topics. The report emphasizes the geographic framework of the North American Flyway councils through which USGS engages regularly with Federal and State government agencies and others who are responsible for managing migratory bird populations.
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U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.
Multiple studies have reported widespread browning of Northern Hemisphere lakes. Most examples are from boreal lakes that have experienced limited human influence, and browning has alternatively been attributed to changes in atmospheric deposition, climate, and land use. To determine the extent and possible causes of browning across a more geographically diverse region, we examined watercolor and dissolved organic carbon (DOC) time series in hundreds of northeastern U.S. lakes. The majority of lakes have increased in both DOC and color, but there were neither coherent spatial patterns in trends nor relationships with previously reported drivers. Color trends were more variable than DOC trends, and DOC and color trends were not strongly correlated, indicating a cause other than or in addition to increased loading of terrestrial carbon. Browning may be pronounced in regions where climate and atmospheric deposition are dominant drivers but muted in more human-dominated landscapes with a limited extent of organic soils where other disturbances predominate.