Nearshore (littoral) habitats of clear lakes with high water quality are increasingly experiencing unexplained proliferations of filamentous algae that grow on submerged surfaces. These filamentous algal blooms (FABs) are sometimes associated with nutrient pollution in groundwater, but complex changes in climate, nutrient transport, lake hydrodynamics, and food web structure may also facilitate this emerging threat to clear lakes. A coordinated effort among members of the public, managers, and scientists is needed to document the occurrence of FABs, to standardize methods for measuring their severity, to adapt existing data collection networks to include nearshore habitats, and to mitigate and reverse this profound structural change in lake ecosystems. Current models of lake eutrophication do not explain this littoral greening. However, a cohesive response to it is essential for protecting some of the world's most valued lakes and the flora, fauna, and ecosystem services they sustain.
","language":"English","publisher":"American Institute of Biological Sciences","doi":"10.1093/biosci/biab049","usgsCitation":"Vadeboncoeur, Y., Moore, M.V., Stewart, S.D., Chandra, S., Atkins, K., Baron, J., Bouma-Gregson, K., Brothers, S., Francoeur, S., Genzoli, L., Higgins, S.N., Hilt, S., Katona, L., Kelly, D., Oleksy, I., Ozersky, T., Powel, M., Roberts, D., Timoshkin, O., Tromboni, F., Vander Zanden, M.J., Volkova, E., Waters, S., Wood, S.A., and Yamamuro, M., 2021, Blue waters, green bottoms: Benthic filamentous algal blooms are an emerging threat to clear lakes worldwide: BioScience, v. 71, no. 10, p. 1011-1027, https://doi.org/10.1093/biosci/biab049.","productDescription":"17 p.","startPage":"1011","endPage":"1027","ipdsId":"IP-125146","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab049","text":"Publisher Index Page"},{"id":390396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Vadeboncoeur, Yvonne","contributorId":267285,"corporation":false,"usgs":false,"family":"Vadeboncoeur","given":"Yvonne","email":"","affiliations":[{"id":13348,"text":"Wright State University","active":true,"usgs":false}],"preferred":false,"id":824919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Marianne V.","contributorId":267286,"corporation":false,"usgs":false,"family":"Moore","given":"Marianne","email":"","middleInitial":"V.","affiliations":[{"id":55461,"text":"Wellesley College","active":true,"usgs":false}],"preferred":false,"id":824920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Simon D.","contributorId":267287,"corporation":false,"usgs":false,"family":"Stewart","given":"Simon","email":"","middleInitial":"D.","affiliations":[{"id":55462,"text":"Cawthron Institue, New Zealand","active":true,"usgs":false}],"preferred":false,"id":824921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandra, Sudeep","contributorId":267288,"corporation":false,"usgs":false,"family":"Chandra","given":"Sudeep","affiliations":[{"id":32871,"text":"University of Nevada at Reno","active":true,"usgs":false}],"preferred":false,"id":824922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkins, Karen","contributorId":267289,"corporation":false,"usgs":false,"family":"Atkins","given":"Karen","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":824923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":824924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouma-Gregson, Keith","contributorId":267290,"corporation":false,"usgs":false,"family":"Bouma-Gregson","given":"Keith","affiliations":[{"id":12702,"text":"California State Water Resources Control Board","active":true,"usgs":false}],"preferred":false,"id":824925,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brothers, Soren","contributorId":267291,"corporation":false,"usgs":false,"family":"Brothers","given":"Soren","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":824926,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Francoeur, Stephen","contributorId":267292,"corporation":false,"usgs":false,"family":"Francoeur","given":"Stephen","email":"","affiliations":[{"id":55463,"text":"Eastern Michigan University","active":true,"usgs":false}],"preferred":false,"id":824927,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Genzoli, Laurel","contributorId":267293,"corporation":false,"usgs":false,"family":"Genzoli","given":"Laurel","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":824928,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Higgins, Scott N.","contributorId":267294,"corporation":false,"usgs":false,"family":"Higgins","given":"Scott","email":"","middleInitial":"N.","affiliations":[{"id":55464,"text":"IISD Experimental Lakes Area, Canada","active":true,"usgs":false}],"preferred":false,"id":824929,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hilt, Sabine","contributorId":267295,"corporation":false,"usgs":false,"family":"Hilt","given":"Sabine","email":"","affiliations":[{"id":55465,"text":"Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany","active":true,"usgs":false}],"preferred":false,"id":824930,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Katona, Leon R.","contributorId":267333,"corporation":false,"usgs":false,"family":"Katona","given":"Leon R.","affiliations":[],"preferred":false,"id":824997,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Kelly, David","contributorId":267334,"corporation":false,"usgs":false,"family":"Kelly","given":"David","affiliations":[],"preferred":false,"id":824998,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Oleksy, Isabella","contributorId":267296,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella","affiliations":[{"id":33412,"text":"Cary Institute for Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":824931,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ozersky, Ted","contributorId":267297,"corporation":false,"usgs":false,"family":"Ozersky","given":"Ted","affiliations":[{"id":55466,"text":"University of Minnesota, Duluth","active":true,"usgs":false}],"preferred":false,"id":824932,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Powel, Mary","contributorId":267298,"corporation":false,"usgs":false,"family":"Powel","given":"Mary","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":824933,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Roberts, Derek","contributorId":267299,"corporation":false,"usgs":false,"family":"Roberts","given":"Derek","email":"","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":824934,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Timoshkin, Oleg","contributorId":267300,"corporation":false,"usgs":false,"family":"Timoshkin","given":"Oleg","email":"","affiliations":[{"id":55467,"text":"Siberian Branch of the Russian Academy of Sciences’ Limnological Institute","active":true,"usgs":false}],"preferred":false,"id":824935,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Tromboni, Flavia","contributorId":267335,"corporation":false,"usgs":false,"family":"Tromboni","given":"Flavia","email":"","affiliations":[],"preferred":false,"id":824999,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Vander Zanden, M. Jake","contributorId":265448,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"M.","email":"","middleInitial":"Jake","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":825000,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Volkova, Ekaterina","contributorId":267301,"corporation":false,"usgs":false,"family":"Volkova","given":"Ekaterina","email":"","affiliations":[{"id":55467,"text":"Siberian Branch of the Russian Academy of Sciences’ Limnological Institute","active":true,"usgs":false}],"preferred":false,"id":824936,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Waters, Sean","contributorId":267336,"corporation":false,"usgs":false,"family":"Waters","given":"Sean","email":"","affiliations":[],"preferred":false,"id":825001,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wood, Susanna A.","contributorId":267337,"corporation":false,"usgs":false,"family":"Wood","given":"Susanna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":825002,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Yamamuro, Masumi","contributorId":267338,"corporation":false,"usgs":false,"family":"Yamamuro","given":"Masumi","email":"","affiliations":[],"preferred":false,"id":824938,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70223308,"text":"70223308 - 2021 - Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA","interactions":[],"lastModifiedDate":"2021-08-20T12:38:24.433922","indexId":"70223308","displayToPublicDate":"2021-07-07T07:35:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA","docAbstract":"Wildfires of uncharacteristic severity, a consequence of climate changes and accumulated fuels, can cause amplified or novel impacts to archaeological resources. The archaeological record includes physical features associated with human activity; these exist within ecological landscapes and provide a unique long-term perspective on human–environment interactions. The potential for fire-caused damage to archaeological materials is of major concern because these resources are irreplaceable and non-renewable, have social or religious significance for living peoples, and are protected by an extensive body of legislation. Although previous studies have modeled ecological burn severity as a function of environmental setting and climate, the fidelity of these variables as predictors of archaeological fire effects has not been evaluated. This study, focused on prehistoric archaeological sites in a fire-prone and archaeologically rich landscape in the Jemez Mountains of New Mexico, USA, identified the environmental and climate variables that best predict observed fire severity and fire effects to archaeological features and artifacts.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-021-00103-6","usgsCitation":"Friggens, M., Loehman, R.A., Constan, C., and Kneifel, R., 2021, Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA: Fire Ecology, v. 17, 18, 19 p., https://doi.org/10.1186/s42408-021-00103-6.","productDescription":"18, 19 p.","ipdsId":"IP-122913","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":451608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-021-00103-6","text":"Publisher Index Page"},{"id":388219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.9189453125,\n 35.24561909420681\n ],\n [\n -105.1171875,\n 35.24561909420681\n ],\n [\n -105.1171875,\n 36.527294814546245\n ],\n [\n -106.9189453125,\n 36.527294814546245\n ],\n [\n -106.9189453125,\n 35.24561909420681\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2021-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Friggens, Megan","contributorId":219865,"corporation":false,"usgs":false,"family":"Friggens","given":"Megan","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":821684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":821685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Constan, Connie","contributorId":264574,"corporation":false,"usgs":false,"family":"Constan","given":"Connie","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":821686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kneifel, Rebekah","contributorId":264576,"corporation":false,"usgs":false,"family":"Kneifel","given":"Rebekah","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":821687,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221843,"text":"70221843 - 2021 - Twenty-first-century projections of shoreline change along inlet-interrupted coastlines","interactions":[],"lastModifiedDate":"2021-07-12T11:57:03.394157","indexId":"70221843","displayToPublicDate":"2021-07-07T06:55:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8955,"text":"Nature--Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Twenty-first-century projections of shoreline change along inlet-interrupted coastlines","docAbstract":"Sandy coastlines adjacent to tidal inlets are highly dynamic and widespread landforms, where large changes are expected due to climatic and anthropogenic influences. To adequately assess these important changes, both oceanic (e.g., sea-level rise) and terrestrial (e.g., fluvial sediment supply) processes that govern the local sediment budget must be considered. Here, we present novel projections of shoreline change adjacent to 41 tidal inlets around the world, using a probabilistic, reduced complexity, system-based model that considers catchment-estuary-coastal systems in a holistic way. Under the RCP 8.5 scenario, retreat dominates (90% of cases) over the twenty-first century, with projections exceeding 100 m of retreat in two-thirds of cases. However, the remaining systems are projected to accrete under the same scenario, reflecting fluvial influence. This diverse range of response compared to earlier methods implies that erosion hazards at inlet-interrupted coasts have been inadequately characterised to date. The methods used here need to be applied widely to support evidence-based coastal adaptation.
Environmental variation has important effects on host–pathogen interactions, affecting large-scale ecological processes such as the severity and frequency of epidemics. However, less is known about how the environment interacts with host immunity to modulate virus fitness within hosts. Here, we studied the interaction between host immune responses and water temperature on the long-term persistence of a model vertebrate virus, infectious hematopoietic necrosis virus (IHNV) in steelhead trout (Oncorhynchus mykiss). We first used cell culture methods to factor out strong host immune responses, allowing us to test the effect of temperature on viral replication. We found that 15
Batrachochytrium salamandrivorans (Bsal) is a fungal pathogen that can cause the emerging infectious disease Bsal chytridiomycosis in some amphibians and is currently causing dramatic declines in European urodeles. To date, Bsal has not been detected in North America but has the potential to cause severe declines in naïve hosts if introduced. Therefore, it is critical that wildlife managers are prepared with effective management actions to combat the fungus. Research has been initiated to identify strategies; however, managers need guidance to prepare for an outbreak until results are available. We conducted a workshop at the Joint Meeting of The Wildlife Society and American Fisheries Society on 30 September 2019 with participants of a Bsal symposium. Our goals were to describe the expected effects of 11 management actions that could be implemented for Bsal in salamander communities in the northwestern, northeastern, and southeastern United States. Participants expected a variety of proposed management actions to decrease pathogen transmission and increase host survival, but also that the selection of a management action may depend on the specific membership of the amphibian community. Collectively, our assessment will help refine research and modeling priorities in an effort to mitigate the risk of Bsal to native U.S. amphibians.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.1198","usgsCitation":"Bernard, R.F., and Campbell Grant, E.H., 2021, Rapid assessment indicates context-dependent mitigation for amphibian disease risk: Wildlife Society Bulletin, v. 45, no. 23-24, p. 290-299, https://doi.org/10.1002/wsb.1198.","productDescription":"10 p.","startPage":"290","endPage":"299","ipdsId":"IP-118442","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451621,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1198","text":"Publisher Index Page"},{"id":387134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"23-24","noUsgsAuthors":false,"publicationDate":"2021-07-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Bernard, Riley F 0000-0002-1321-3625","orcid":"https://orcid.org/0000-0002-1321-3625","contributorId":238925,"corporation":false,"usgs":false,"family":"Bernard","given":"Riley","email":"","middleInitial":"F","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":819007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819008,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221784,"text":"ofr20211053 - 2021 - Least Bell's Vireos and Southwestern Willow Flycatchers at the San Luis Rey flood risk management project area in San Diego County, California—Breeding activities and habitat use—2020 annual report","interactions":[],"lastModifiedDate":"2021-08-03T12:41:56.22042","indexId":"ofr20211053","displayToPublicDate":"2021-07-06T09:36:49","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":"2021-1053","displayTitle":"Least Bell's Vireos and Southwestern Willow Flycatchers at the San Luis Rey Flood Risk Management Project Area in San Diego County, California: Breeding Activities and Habitat Use—2020 Annual Report","title":"Least Bell's Vireos and Southwestern Willow Flycatchers at the San Luis Rey flood risk management project area in San Diego County, California—Breeding activities and habitat use—2020 annual report","docAbstract":"To investigate whether the cumulative years of treatment had an impact on vireo reproductive effort, we looked at the effects of the Treatment Index on reproductive parameters. Results from generalized linear models indicated that treatment did not have an effect on vireo nesting effort or the number of vireo fledglings per pair produced in 2020.
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The relentless role of invasive species in the extinction of native biota requires predictions of ecosystem vulnerability to inform proactive management strategies. The worldwide invasion and range expansion of predatory northern pike (Esox lucius) has been linked to the decline of native fishes and tools are needed to predict the vulnerability of habitats to invasion over broad geographic scales. To address this need, we coupled an intrinsic potential habitat modelling approach with a Bayesian network to evaluate the vulnerability of five culturally and economically vital species of Pacific salmon (Oncorhynchus spp.) to invasion by northern pike. This study was conducted along 22,875 stream km in the Southcentral region of Alaska, USA. Pink salmon (O. gorbuscha) were the most vulnerable species, with 15.2% (2,458 km) of their calculated extent identified as “highly” vulnerable, followed closely by chum salmon (O. keta, 14.8%; 2,557 km) and coho salmon (O. kisutch, 14.7%; 2,536 km). Moreover, all five Pacific salmon species were highly vulnerable in 1,001 stream km of shared habitat. This simple to implement, adaptable, and cost-effective framework will allow prioritizing habitats for early detection and monitoring of invading northern pike.
Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of 5 mammal species. Specifically, we focused on 2 species of conservation concern (mountain gorilla (Gorilla beringei beringei) and golden money (Cercopithecus mitis kandti)), and 3 species targeted by poachers (black-fronted duiker (Cephalophus nigrifrons), bushbuck (Tragelaphus scriptus), and African buffalo (Syncerus caffer)). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics (this paper) can be combined with information on the relationship between ranger patrols and poaching activity (Moore et al. 2017) to develop models useful for making wise decisions about ranger patrol deployment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2397","usgsCitation":"Moore, J.F., Uzabaho, E., Musana, A., Uwingell, P., Hines, J.E., and Nichols, J.D., 2021, What is the effect of poaching activity on wildlife species?: Ecological Applications, v. 31, no. 7, e02397, 12 p., https://doi.org/10.1002/eap.2397.","productDescription":"e02397, 12 p.","ipdsId":"IP-118381","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","otherGeospatial":"Volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 29.702911376953125,\n -1.3587440869100178\n ],\n [\n 29.663772583007812,\n -1.3882613601346867\n ],\n [\n 29.63081359863281,\n -1.3951257897508238\n ],\n [\n 29.59304809570312,\n -1.3882613601346867\n ],\n [\n 29.561462402343746,\n -1.384829137846475\n ],\n [\n 29.50721740722656,\n -1.4218968729661605\n ],\n [\n 29.49073791503906,\n -1.4383712317629698\n ],\n [\n 29.4927978515625,\n -1.4630825465188169\n ],\n [\n 29.480438232421875,\n -1.4692603328543323\n ],\n [\n 29.485244750976562,\n -1.4788701887242113\n ],\n [\n 29.461898803710938,\n -1.491912069367617\n ],\n [\n 29.439926147460934,\n -1.5269188384985064\n ],\n [\n 29.39804077148437,\n -1.5324100450044358\n ],\n [\n 29.442672729492188,\n -1.568788930117857\n ],\n [\n 29.481124877929688,\n -1.5660433757691457\n ],\n [\n 29.514770507812496,\n -1.5358420419244077\n ],\n [\n 29.519577026367188,\n -1.4891664166873633\n ],\n [\n 29.50996398925781,\n -1.4493540716333067\n ],\n [\n 29.540176391601562,\n -1.422583306939631\n ],\n [\n 29.54635620117188,\n -1.4184647000387454\n ],\n [\n 29.560775756835934,\n -1.408854588797322\n ],\n [\n 29.58549499511719,\n -1.4177782648419572\n ],\n [\n 29.641799926757812,\n -1.4232697407088846\n ],\n [\n 29.671325683593754,\n -1.412973212770802\n ],\n [\n 29.69467163085938,\n -1.4157189580307432\n ],\n [\n 29.70497131347656,\n -1.3875749160752702\n ],\n [\n 29.702911376953125,\n -1.3587440869100178\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":819048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uzabaho, Eustrate","contributorId":260880,"corporation":false,"usgs":false,"family":"Uzabaho","given":"Eustrate","email":"","affiliations":[{"id":52699,"text":"Intl. Gorilla Conservation Programme, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musana, Abel","contributorId":260881,"corporation":false,"usgs":false,"family":"Musana","given":"Abel","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uwingell, Prosper","contributorId":260882,"corporation":false,"usgs":false,"family":"Uwingell","given":"Prosper","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237360,"text":"70237360 - 2021 - Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","interactions":[],"lastModifiedDate":"2022-10-11T16:24:49.683141","indexId":"70237360","displayToPublicDate":"2021-07-01T11:21:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","docAbstract":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR029579","usgsCitation":"Willard, J., Read, J., Appling, A.P., Oliver, S.K., Jia, X., and Kumar, V., 2021, Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning: Water Resources Research, v. 57, no. 7, e2021WR029579, 20 p., https://doi.org/10.1029/2021WR029579.","productDescription":"e2021WR029579, 20 p.","ipdsId":"IP-119147","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451661,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr029579","text":"Publisher Index Page"},{"id":436285,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I00WFR","text":"USGS data release","linkHelpText":"Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)"},{"id":408165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854266,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248236,"text":"70248236 - 2021 - Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","interactions":[],"lastModifiedDate":"2023-09-05T15:18:59.650382","indexId":"70248236","displayToPublicDate":"2021-07-01T10:09:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","title":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","docAbstract":"The Queen Charlotte-Fairweather Fault (QC-FF) system off the coast of British Columbia and southeast Alaska is a highly active dextral strike-slip plate boundary that accommodates ∼50 mm/yr of relative motion between the Pacific and North America plates. Nine MW ≥ 6.7 earthquakes have occurred along the QC-FF system since 1910, including a MS(G-R)8.1 event in 1949. Two recent earthquakes, the October 28, 2012 Haida Gwaii (MW7.8) and January 5, 2013 Craig, Alaska (MW7.5) events, produced postseismic transient deformation that was recorded in the motions of 25 nearby continuous Global Positioning System (cGPS) stations. Here, we use 5+ yr of cGPS measurements to characterize the underlying mechanisms of postseismic deformation and to constrain the viscosity structure of the upper mantle surrounding the QC-FF. We construct forward models of viscoelastic deformation driven by coseismic stress changes from these two earthquakes and explore a large set of laterally heterogeneous viscosity structures that incorporate a relatively weak back-arc domain; we then evaluate each model based on its fit to the postseismic signals in our cGPS data. In determining best-fit model structures, we additionally incorporate the effects of afterslip following the 2012 event. Our results indicate the occurrence of a combination of temporally decaying afterslip and vigorous viscoelastic relaxation of the mantle asthenosphere. In addition, our best-fit viscosity structure (transient viscosity of 1.4–2.0 × 1018 Pa s; steady-state viscosity of 1019 Pa s) is consistent with the range of upper mantle viscosities determined in previous studies of glacial isostatic rebound and postseismic deformation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB021891","usgsCitation":"Guns, K.A., Pollitz, F., Lay, T., and Yue, H., 2021, Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure: Journal of Geophysical Research B: Solid Earth, v. 126, no. 7, e2021JB021891, 20 p., https://doi.org/10.1029/2021JB021891.","productDescription":"e2021JB021891, 20 p.","ipdsId":"IP-127502","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":420483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","city":"Craig","otherGeospatial":"Haida Gwaii Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.04582537099918,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.778120902444044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.70446739512911,\n 53.158925565323756\n ],\n [\n -132.34949768033022,\n 52.76369069159804\n ],\n [\n -131.7793948050469,\n 52.469804491078975\n ],\n [\n -131.66107156678072,\n 52.29252484773727\n ],\n [\n -130.97264545323122,\n 51.88274035718925\n ],\n [\n -130.75751229274715,\n 51.94245878634544\n ],\n [\n -131.7471248309743,\n 53.33271351252088\n ],\n [\n -132.30647104823325,\n 53.16537474581318\n ],\n [\n -132.70446739512911,\n 53.158925565323756\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Guns, Katherine A.","contributorId":329359,"corporation":false,"usgs":false,"family":"Guns","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":16619,"text":"UCSD","active":true,"usgs":false}],"preferred":false,"id":882060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":882061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":328838,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":882062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yue, Han","contributorId":329362,"corporation":false,"usgs":false,"family":"Yue","given":"Han","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":882063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223240,"text":"70223240 - 2021 - National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","interactions":[],"lastModifiedDate":"2021-08-19T15:13:17.0885","indexId":"70223240","displayToPublicDate":"2021-07-01T10:01:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2021/2285","title":"National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC.
To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type.
A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines.
Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.
A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha).
A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval.
The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286888","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., White, R., Pyne, M., Govus, T., and Dieck, J., 2021, National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project: Natural Resource Report 2021/2285, 220 p., https://doi.org/10.36967/nrr-2286888.","productDescription":"220 p.","ipdsId":"IP-120204","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.023681640625,\n 35.594785665487244\n ],\n [\n -83.09783935546875,\n 35.806676609227054\n ],\n [\n -83.40545654296875,\n 35.762114795721\n ],\n [\n -83.88336181640625,\n 35.68853320738875\n ],\n [\n -84.034423828125,\n 35.545635932499415\n ],\n [\n -83.90808105468749,\n 35.43605776486772\n ],\n [\n -83.5565185546875,\n 35.39800594715108\n ],\n [\n -83.30657958984375,\n 35.47409160773029\n ],\n [\n -83.023681640625,\n 35.594785665487244\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":821498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":821499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Govus, Tom","contributorId":264417,"corporation":false,"usgs":false,"family":"Govus","given":"Tom","email":"","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":821500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229733,"text":"70229733 - 2021 - Context-dependent deep learning","interactions":[],"lastModifiedDate":"2022-05-03T15:07:59.274845","indexId":"70229733","displayToPublicDate":"2021-07-01T09:59:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10746,"text":"Modeling and Using Context","active":true,"publicationSubtype":{"id":10}},"title":"Context-dependent deep learning","docAbstract":"Explicitly representing an agent’s context has been shown to have many benefits, which should also apply to machine learning. In this paper, we describe an approach to do this called context-dependent deep learning (CDDL), which is based on earlier work in context-mediated behavior (CMB) that uses contextual schemas (c-schemas) to represent clas-ses of situations along with knowledge useful in them. These are then recalled, and they guide reasoning in the corre-sponding contexts. CDDL stores knowledge about deep neural network structure and weights in c-schemas, which al-lows context-specific learning. Our work is being developed in the domain of seabird detection in aerial images of islands for use by biologists.
","language":"English","publisher":"ISTE OpenScience","doi":"10.21494/ISTE.OP.2021.0690","usgsCitation":"Turner, R.M., Loftin, C., Revello, A., Kline, L.R., Lewis, M., and Yasai-Sekeh, S., 2021, Context-dependent deep learning: Modeling and Using Context, v. 4, 7 p., https://doi.org/10.21494/ISTE.OP.2021.0690.","productDescription":"7 p.","ipdsId":"IP-129640","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21494/iste.op.2021.0690","text":"Publisher Index Page"},{"id":400059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Turner, Roy M.","contributorId":288598,"corporation":false,"usgs":false,"family":"Turner","given":"Roy","email":"","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Revello, Alex","contributorId":288599,"corporation":false,"usgs":false,"family":"Revello","given":"Alex","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kline, Logan R.","contributorId":288606,"corporation":false,"usgs":false,"family":"Kline","given":"Logan","email":"","middleInitial":"R.","affiliations":[{"id":61812,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Meredith","contributorId":288607,"corporation":false,"usgs":false,"family":"Lewis","given":"Meredith","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yasai-Sekeh, Salimeh","contributorId":288602,"corporation":false,"usgs":false,"family":"Yasai-Sekeh","given":"Salimeh","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838132,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256784,"text":"70256784 - 2021 - Assessing recovery of spectacled eiders using a Bayesian decision analysis","interactions":[],"lastModifiedDate":"2024-08-06T14:45:41.909928","indexId":"70256784","displayToPublicDate":"2021-07-01T09:43:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing recovery of spectacled eiders using a Bayesian decision analysis","docAbstract":"Assessing species status and making classification decisions under the Endangered Species Act is a critical step towards effective species conservation. However, classification decisions are liable to two errors: i) failing to classify a species as threatened or endangered that should be classified (underprotection), or ii) classifying a species as threatened or endangered when it is not warranted (overprotection). Recent surveys indicate threatened spectacled eider populations are increasing in western Alaska, prompting the U.S. Fish and Wildlife Service to reconsider the federal listing status. There are multiple criteria set for assessing spectacled eider status, and here we focus on the abundance and decision analysis criteria. We estimated population metrics using state-space models for Alaskan breeding populations of spectacled eiders. We projected abundance over 50 years using posterior estimates of abundance and process variation to estimate the probability of quasi-extinction. The decision analysis maps the risk of quasi-extinction to the loss associated with making a misclassification error (i.e., underprotection) through a loss function. Our results indicate that the Yukon Kuskokwim Delta breeding population in western Alaska has met the recovery criteria but the Arctic Coastal Plain population in northern Alaska has not. The methods employed here provide an example of accounting for uncertainty and incorporating value judgements in such a way that the decision-makers may understand the risk of committing a misclassification error. Incorporating the abundance threshold and decision analysis in the reclassification criteria greatly increases the transparency and defensibility of the classification decision, a critical aspect for making effective decisions about species management and conservation.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0253895","usgsCitation":"Dunham, K., Osnas, E., Frost, C., Fischer, J., and Grand, J.B., 2021, Assessing recovery of spectacled eiders using a Bayesian decision analysis: PLoS ONE, v. 16, no. 7, e0253895, 17 p., https://doi.org/10.1371/journal.pone.0253895.","productDescription":"e0253895, 17 p.","ipdsId":"IP-118460","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451667,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0253895","text":"Publisher Index Page"},{"id":432285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, K.D.","contributorId":287550,"corporation":false,"usgs":false,"family":"Dunham","given":"K.D.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osnas, E.E.","contributorId":341825,"corporation":false,"usgs":false,"family":"Osnas","given":"E.E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frost, C.","contributorId":336910,"corporation":false,"usgs":false,"family":"Frost","given":"C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":908940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fischer, J.B.","contributorId":341826,"corporation":false,"usgs":false,"family":"Fischer","given":"J.B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908942,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223671,"text":"70223671 - 2021 - Tools and technologies for quantifying spread and impacts of invasive species","interactions":[],"lastModifiedDate":"2022-04-13T20:13:00.752506","indexId":"70223671","displayToPublicDate":"2021-07-01T08:53:56","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"11","title":"Tools and technologies for quantifying spread and impacts of invasive species","docAbstract":"The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tectorum) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"U.S. Forest Service","doi":"10.1007/978-3-030-45367-1_11","collaboration":"U.S. Forest Service","usgsCitation":"Reeves, M., Ibanez, I., Blumenthal, D., Chen, G., Guo, Q., Jarnevich, C.S., Koch, J., Sapio, F., Schwartz, M.D., Meentemeyer, R.K., Wylie, B., and Boyte, S.P., 2021, Tools and technologies for quantifying spread and impacts of invasive species, chap. 11 of Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector, p. 243-265, https://doi.org/10.1007/978-3-030-45367-1_11.","productDescription":"23 p.","startPage":"243","endPage":"265","ipdsId":"IP-082001","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451672,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-030-45367-1_11","text":"Publisher Index Page"},{"id":388728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Matt","contributorId":202843,"corporation":false,"usgs":false,"family":"Reeves","given":"Matt","affiliations":[],"preferred":false,"id":822267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ibanez, Ines","contributorId":236833,"corporation":false,"usgs":false,"family":"Ibanez","given":"Ines","affiliations":[],"preferred":false,"id":822274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blumenthal, Dana","contributorId":70686,"corporation":false,"usgs":true,"family":"Blumenthal","given":"Dana","affiliations":[],"preferred":false,"id":822330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Gang","contributorId":265128,"corporation":false,"usgs":false,"family":"Chen","given":"Gang","email":"","affiliations":[{"id":54601,"text":"UNCC","active":true,"usgs":false}],"preferred":false,"id":822271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guo, Qinfeng","contributorId":214263,"corporation":false,"usgs":false,"family":"Guo","given":"Qinfeng","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":822275,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822276,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koch, Jennifer","contributorId":210475,"corporation":false,"usgs":false,"family":"Koch","given":"Jennifer","email":"","affiliations":[{"id":38113,"text":"The University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":822277,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sapio, Frank","contributorId":265129,"corporation":false,"usgs":false,"family":"Sapio","given":"Frank","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":822272,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822278,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":822268,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":822273,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":822331,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70238599,"text":"70238599 - 2021 - Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","interactions":[],"lastModifiedDate":"2022-12-01T14:47:41.476167","indexId":"70238599","displayToPublicDate":"2021-07-01T08:39:03","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","docAbstract":"Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters (Enhydra lutris) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000.
Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery.
Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1–8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested.
This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00270-w","usgsCitation":"Eisaguirre, J., Willliams, P.J., Lu, X., Kissling, M.L., Beatty, W.S., Esslinger, G.G., Womble, J.N., and Hooten, M., 2021, Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator: Movement Ecology, v. 9, 34, 14 p., https://doi.org/10.1186/s40462-021-00270-w.","productDescription":"34, 14 p.","ipdsId":"IP-126602","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00270-w","text":"Publisher Index Page"},{"id":387131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.15234374999997,\n 60.28340847828243\n ],\n [\n -142.470703125,\n 59.60109549032134\n ],\n [\n -135.6591796875,\n 55.50374985927514\n ],\n [\n -131.7919921875,\n 51.781435604431195\n ],\n [\n -130.693359375,\n 52.1874047455997\n ],\n [\n -129.9462890625,\n 55.27911529201561\n ],\n [\n -130.0341796875,\n 56.29215668507645\n ],\n [\n -135.7470703125,\n 59.88893689676585\n ],\n [\n -139.2626953125,\n 60.34869562531862\n ],\n [\n -141.15234374999997,\n 60.28340847828243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph M. 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":260861,"corporation":false,"usgs":false,"family":"Eisaguirre","given":"Joseph M.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":818988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willliams, Perry J.","contributorId":260862,"corporation":false,"usgs":false,"family":"Willliams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":818989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Xinyi","contributorId":260863,"corporation":false,"usgs":false,"family":"Lu","given":"Xinyi","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kissling, Michelle L.","contributorId":172675,"corporation":false,"usgs":false,"family":"Kissling","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":818991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":146301,"corporation":false,"usgs":false,"family":"Beatty","given":"William","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":818992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":818993,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Womble, Jamie N.","contributorId":198631,"corporation":false,"usgs":false,"family":"Womble","given":"Jamie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":818994,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":818995,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221716,"text":"ofr20211048 - 2021 - Literature review for candidate chemical control agents for nonnative crayfish","interactions":[],"lastModifiedDate":"2021-07-01T11:45:35.778315","indexId":"ofr20211048","displayToPublicDate":"2021-06-30T12:02:12","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":"2021-1048","displayTitle":"Literature Review for Candidate Chemical Control Agents for Nonnative Crayfish","title":"Literature review for candidate chemical control agents for nonnative crayfish","docAbstract":"Nonnative crayfish are an immediate and pervasive threat to aquatic environments and their biodiversity. Crayfish control can be achieved by physical methods, water chemistry modification, biological methods, biocidal application, and application of crayfish physiology modifiers. The purpose of this report is to identify suitable candidates for potential control of nonnative crayfish through a comprehensive literature review. This review focuses on control methods, specifically on the available data to support registration of a crayfish pesticide. The literature search resulted in 28,058 documents, which were searched to determine if they contained information on physical, chemical, biological, and (or) biocidal approaches to control crayfish. Pesticides directly toxic to crayfish in this literature review include: pyrethroids (natural pyrethrins and synthetic), fipronil, mirex, antimycin-A, and rotenone. Some chemicals, such as diflubenzuron and emamectin benzoate, alter crayfish physiology resulting in a lower pesticide dose needed to control crayfish. Environmental damage, application rate, exposure duration, nontarget effects, environmental persistence, and registration data gaps were used as criteria to define which pesticides are potentially selective to crayfish, along with which have the greatest amount of data to support registration by the U.S. Environmental Protection Agency.
Synthetic pyrethroids were identified as the most likely candidate to be developed into a crayfish pesticide. A type-2 synthetic pyrethroid, cyfluthrin, has the greatest potential for eradicating nonnative crayfish. Although other invertebrate species will be negatively affected at the concentrations required for crayfish control, compared with other pyrethroids and other potential control chemicals, cyfluthrin offers rapid ecosystem recovery due to being more selective, having fewer effects on native fish, and having a short aquatic persistence. Cyfluthrin also has few data gaps for U.S. Environmental Protection Agency registration purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211048","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Schueller, J.R., Smerud, J.R., Fredricks, K.T., and Putnam, J.G., 2021, Literature review for candidate chemical control agents for nonnative crayfish: U.S. Geological Survey Open-File Report 2021–1048, 32 p., https://doi.org/10.3133/ofr20211048.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-115061","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1048/ofr20211048.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1048"},{"id":386878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1048/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.
Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.
Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.
Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.
• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.
There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.
For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.
Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.
Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:
- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.
This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.
","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223729,"text":"70223729 - 2021 - Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","interactions":[],"lastModifiedDate":"2021-09-16T15:12:14.688708","indexId":"70223729","displayToPublicDate":"2021-06-30T09:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.
As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Diagnostic and feasibility study findings: Water quality improvements for Green Lake, Wisconsin","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Green Lake Association","usgsCitation":"Robertson, D., Siebers, B.J., Ladwig, R., Hamilton, D., Reneau, P., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2021, Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222579,"text":"70222579 - 2021 - Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","interactions":[],"lastModifiedDate":"2021-08-05T13:08:29.47498","indexId":"70222579","displayToPublicDate":"2021-06-30T08:03:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9134,"text":"The Sedimentary Record","active":true,"publicationSubtype":{"id":10}},"title":"Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","docAbstract":"The San Gabriel and Canton faults represent early stages in the development of the San Andreas fault system. However, questions of timing of initiation and magnitude of slip on these structures remain unresolved, with published estimates ranging from 42-75 km and likely starting in the Miocene. This uncertainty in slip history reflects an absence of appropriate piercing points. We attempt to better constrain the slip history on these faults by quantifying the changing proportions of source terranes contributing sediment to the Ventura Basin, California, through the Cenozoic, including refining data for a key piercing point.\nVentura Basin sediments show an increase in detrital zircon U-Pb dates and mineral abundances associated with crystalline sources in the northern San Gabriel Mountains through time, which we interpret to record the basin’s northwest translation by dextral strike-slip faulting. In particular, an Oligocene unit mapped as part of the extra-regional Sespe Formation instead has greater affinity to the Vasquez Formation. Specifically, the presence of a unimodal population of ~1180 Ma zircon, high (57%) plagioclase content, and proximal alluvial fan facies indicate that the basin was adjacent to the San Gabriel anorthosite during deposition of the Vasquez Formation, requiring 35-60 km of slip on the San Gabriel-Canton fault system. Mixture modeling of detrital zircon data supported by automated mineralogy highlights the importance of this piercing point along the San Gabriel-Canton fault system and suggests that fault slip began during the late Oligocene to early Miocene, which is earlier than published models. These two lines of evidence disagree with recent models that estimate >60 km of offset, requiring a reappraisal of the slip history of an early strand of the San Andreas transform zone.","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/sedred.2021.2.3","usgsCitation":"Gilbert, C., Jobe, Z.R., Johnstone, S., and Sharman, G.R., 2021, Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources: The Sedimentary Record, v. 19, no. 2, p. 12-21, https://doi.org/10.2110/sedred.2021.2.3.","productDescription":"10 p.","startPage":"12","endPage":"21","ipdsId":"IP-126612","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/sedred.2021.2.3","text":"Publisher Index Page"},{"id":387714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ventura","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.38568115234374,\n 34.24813554589752\n ],\n [\n -119.1851806640625,\n 34.24813554589752\n ],\n [\n -119.1851806640625,\n 34.44315867450577\n ],\n [\n -119.38568115234374,\n 34.44315867450577\n ],\n [\n -119.38568115234374,\n 34.24813554589752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Clark","contributorId":261777,"corporation":false,"usgs":false,"family":"Gilbert","given":"Clark","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":820621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jobe, Zane R.","contributorId":207547,"corporation":false,"usgs":false,"family":"Jobe","given":"Zane","email":"","middleInitial":"R.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":820622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":820623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":820624,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226983,"text":"70226983 - 2021 - Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients","interactions":[],"lastModifiedDate":"2021-12-23T13:11:01.148018","indexId":"70226983","displayToPublicDate":"2021-06-30T07:07:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients","docAbstract":"Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C4 grasses. The largest declines in big sagebrush, perennial C3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.
Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R2 = 0.55, P < 0.001) for dNBR and poor (R2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR
Natural resource management and supporting research teams need computational fluency in the data and model-rich 21st century. Computational fluency describes the ability of practitioners and scientists to conduct research and represent natural systems within the computer's environment. Advancement in information synthesis for natural resource management requires more sophisticated computational approaches, as well as reproducible, reusable, extensible, and transferable methods. Despite this importance, many new and current natural resource practitioners lack computational fluency and no common set of recommended resources and practices exist for learning these skills. Broadly, attaining computational fluency entails moving beyond the simple use of computers to applying sound computational principles and methods and including computational experts (such as computer scientists) on research teams. Our path for computational fluency includes using open-source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the benefits of basic computer programming to carry out more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.
","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12318","usgsCitation":"Erickson, R.A., Burnett, J.L., Wiltermuth, M.T., Bulliner, E.A., and Hsu, L., 2021, Paths to computational fluency for natural resource educators, researchers, and managers: Natural Resource Modelling, v. 34, no. 3, e12318, 21 p., https://doi.org/10.1111/nrm.12318.","productDescription":"e12318, 21 p.","ipdsId":"IP-124147","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":489797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nrm.12318","text":"Publisher Index Page"},{"id":388550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burnett, Jessica Leigh 0000-0002-0896-5099","orcid":"https://orcid.org/0000-0002-0896-5099","contributorId":248195,"corporation":false,"usgs":true,"family":"Burnett","given":"Jessica","email":"","middleInitial":"Leigh","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":821997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":821998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":822000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}