The >2,570,000-ha Flint Hills ecoregion of Kansas, USA, harbors the largest remaining contiguous tract of tallgrass prairie in North America, a unique system, as the remainder of North America's tallgrass prairie has succumbed to development and conversion. Consequently, the loss and degradation of tallgrass prairie has reduced populations of many North American prairie-obligate species including the regal fritillary (Speyeria idalia) butterfly. Population abundance and occupied range of regal fritillary have declined >99%, restricting many populations to isolated, remnant patches of tallgrass prairie. Such extensive decline has resulted in consideration of the regal fritillary for protection under the Endangered Species Act. Although it is widely accepted that management practices such as fire, grazing, and haying are necessary to maintain prairie ecosystems, reported responses by regal fritillary to these management regimes have been ambiguous. We tested effects of prescribed fire across short, moderate, and long fire-return intervals as well as grazing and haying management treatments on regal fritillary density. We also tested the relative influence of habitat characteristics created by these management regimes by measuring density of an obligate host plant (Viola spp.) and canopy cover of woody vegetation, grasses, forbs/ferns, bare ground, and litter. We found density was at least 1.6 times greater in sites burned with a moderate fire-return interval vs. sites burned with short and long fire-return intervals. Overall management regardless of fire-return interval did not have an effect on density. Percent cover of grass had the strongest positive association, while percent cover of woody vegetation had the greatest negative effect on density. Our results indicate that patch-burning is a viable and perhaps even ideal management strategy for regal fritillary in tallgrass prairie landscapes. Additionally, these results elucidate the importance of fire, particularly when applied at moderate-return intervals to regal fritillary, and corroborate a growing suite of studies that suggest fire is perhaps not as detrimental to populations of regal fritillary as previously believed.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2845","usgsCitation":"McCullough, K., Albanese, G., Haukos, D.A., Ricketts, A., and Stratton, S., 2019, Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie: Ecosphere, v. 10, no. 8, e02845, 18 p., https://doi.org/10.1002/ecs2.2845.","productDescription":"e02845, 18 p.","ipdsId":"IP-094927","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2845","text":"Publisher Index Page"},{"id":395312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Flint Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.18505859374999,\n 37.020098201368114\n ],\n [\n -96.3720703125,\n 36.96744946416934\n ],\n [\n -95.47119140625,\n 36.932330061503144\n ],\n [\n -95.47119140625,\n 39.80853604144591\n ],\n [\n -97.1630859375,\n 39.80853604144591\n ],\n [\n -97.18505859374999,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, K.","contributorId":273122,"corporation":false,"usgs":false,"family":"McCullough","given":"K.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albanese, G.","contributorId":67722,"corporation":false,"usgs":true,"family":"Albanese","given":"G.","email":"","affiliations":[],"preferred":false,"id":832759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricketts, A.M.","contributorId":273124,"corporation":false,"usgs":false,"family":"Ricketts","given":"A.M.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stratton, S.","contributorId":273125,"corporation":false,"usgs":false,"family":"Stratton","given":"S.","email":"","affiliations":[{"id":56426,"text":"fort riley","active":true,"usgs":false}],"preferred":false,"id":832761,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206000,"text":"70206000 - 2019 - Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","interactions":[],"lastModifiedDate":"2019-10-17T07:00:50","indexId":"70206000","displayToPublicDate":"2019-08-13T14:44:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","docAbstract":"The 9 month long 2011-2012 eruption of Cordon Caulle (Southern Andes, Chile) is the best instrumentally recorded rhyolitic eruption to date and the first time that the effusion of a rhyolitic flow has been observed in detail. We use Interferometric Synthetic Aperture Radar (InSAR), with time-lapse DEMs and numerical models to study the dynamics of coupled magma reservoir deflation and lava effusion. InSAR recorded ~2.2-2.5 m of deflation after the first three days of the eruption, which can be modeled using a spheroidal magma reservoir at a depth of ∼5 km, ∼20 km long, and with a pressure drop of ~20-30 MPa. The source is elongated in the NW-SE direction and its large dimensions imply a large plumbing system spanning neighboring volcanoes and active throughout the eruption, with a slight change halfway through the effusive phase. TanDEM-X DEMs record the extrusion of both the rhyolitic lava flow and the intrusion of a shallow laccolith around the eruptive vent, with a total volume of ~1.2 km3 DRE. The laccolith was emplaced during the first month of the eruption, during both the eruption explosive and effusive stages. Both the reservoir pressure drop and the extruded volume time series follow quasi-exponential trends, and can be explained by a model that couples the reservoir pres- sure decrease, time- and pressured ependent variations in the magma properties inside of the reservoir, and conduit flow. This model predicts both the temporal evolution and amplitude of both time series, and a magma compressibility of ∼10^−10 Pa−1, half the compressibility of the magma of the sub-Plinian explosive phase. Further, we estimate that the reservoir contained 1-3 wt.% dissolved H2O at the onset of lava effusion, with no exsolved CO2 and H2O in the reservoir throughout the eruption. This is in accord with a magma that was significantly degassed after the explosive phase. These remaining volatiles might have been responsible for magma fragmentation, consistent with the hybrid explosive and effusive style observed during the waning of the eruption.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.115736","usgsCitation":"Delgado, F., Julia Kubanek, Anderson, K.R., Paul Lundgren, and Pritchard, M.E., 2019, Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption: Earth and Planetary Science Letters, v. 524, p. 1-14, https://doi.org/10.1016/j.epsl.2019.115736.","productDescription":"115736, 14p.","startPage":"1","endPage":"14","ipdsId":"IP-102193","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2019.115736","text":"Publisher Index Page"},{"id":368337,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0012821X19304285"},{"id":368348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Cordón Caulle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.59765625,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"524","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Delgado, Francisco","contributorId":219817,"corporation":false,"usgs":false,"family":"Delgado","given":"Francisco","email":"","affiliations":[{"id":40075,"text":"Institute de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":773252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Julia Kubanek","contributorId":219818,"corporation":false,"usgs":false,"family":"Julia Kubanek","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":773253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":773251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul Lundgren","contributorId":219819,"corporation":false,"usgs":false,"family":"Paul Lundgren","affiliations":[{"id":32904,"text":"Jet Propulsion Lab, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":773254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pritchard, Matthew E.","contributorId":219820,"corporation":false,"usgs":false,"family":"Pritchard","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":773255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208120,"text":"70208120 - 2019 - Global positioning system tracking devices can decrease Greater Sage-Grouse survival","interactions":[],"lastModifiedDate":"2020-01-29T16:27:32","indexId":"70208120","displayToPublicDate":"2019-08-13T13:06:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Global positioning system tracking devices can decrease Greater Sage-Grouse survival","docAbstract":"Reliable demographic estimates hinge on the assumption that marking animals does not alter their behavior, reproduction, or survival. Violations can bias inference and are especially egregious for species of high conservation concern. Global positioning system (GPS) devices represent a recent technological advancement that has contributed greatly to avian ecological studies compared with traditionally used very high frequency (VHF) radio transmitters, but may affect demographic rates differently than VHF transmitters. We compared survival between VHF (necklace attachment) and GPS (rump-mounted attachment) devices from >1,100 Greater Sage-Grouse (Centrocercus urophasianus), a species of high conservation concern, across multiple populations within California and Nevada. We found lower survival for GPS-marked compared to VHF-marked sage-grouse across most sex, age, and seasonal comparisons. Estimates of annual survival for GPS-marked sage-grouse were 0.55–0.86 times that of VHF-marked birds with considerable variation among sex and age classes. Differences in survival could be attributed to features associated with GPS devices, including greater weight, position of attachment (e.g., rump-mount harness), and a semi-reflective solar panel. In a post hoc analysis, we evaluated additive and interactive effects between device type (GPS vs. VHF) and transmitter mass as a proportion of body mass (PBM). While the device type effect alone was the best model, the PBM interaction also had support. For GPS devices, survival decreased with increasing PBM, whereas PBM effects were not found for VHF. We attributed differences in PBM effect to placement of transmitters on sage-grouse, as weight of GPS devices was positioned rearward. This information can help managers and researchers weigh costs and benefits of GPS-based monitoring. Our results indicate demographic data collected from GPS devices should be interpreted with caution, and use of these devices should be tailored to specific ecological questions. Future research aimed at investigating behavioral impacts and GPS designs that reduce adverse impacts on survival would be beneficial.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/condor/duz032","usgsCitation":"Severson, J.P., Coates, P.S., Prochazka, B.G., Ricca, M.A., Casazza, M.L., and Delahunty, D.J., 2019, Global positioning system tracking devices can decrease Greater Sage-Grouse survival: The Condor, v. 121, no. 3, duz032, 15 p., https://doi.org/10.1093/condor/duz032.","productDescription":"duz032, 15 p.","ipdsId":"IP-106346","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":460307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz032","text":"Publisher Index Page"},{"id":371649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, 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\"}}]}","volume":"121","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delahunty, David J","contributorId":221820,"corporation":false,"usgs":false,"family":"Delahunty","given":"David","email":"","middleInitial":"J","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":780564,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204877,"text":"70204877 - 2019 - A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","interactions":[],"lastModifiedDate":"2019-08-21T10:31:44","indexId":"70204877","displayToPublicDate":"2019-08-13T10:22:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","docAbstract":"Worldwide, eutrophication is threatening lake ecosystems. To support lake management numerous eutrophication models have been developed. Diverse research questions in a wide range of lake ecosystems are addressed by these models. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-a relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in a comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133887","usgsCitation":"Chang, M., Teurlincx, S., DeAngelis, D.L., Janse, J.H., Troost, T.A., van Wijk, D., Mooij, W.M., and Janssen, A., 2019, A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge: Science of the Total Environment, v. 695, 133887, 11 p., https://doi.org/10.1016/j.scitotenv.2019.133887.","productDescription":"133887, 11 p.","ipdsId":"IP-104765","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.133887","text":"Publisher Index Page"},{"id":366781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"695","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Manqi","contributorId":218274,"corporation":false,"usgs":false,"family":"Chang","given":"Manqi","email":"","affiliations":[],"preferred":false,"id":768853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teurlincx, Sven","contributorId":218275,"corporation":false,"usgs":false,"family":"Teurlincx","given":"Sven","email":"","affiliations":[],"preferred":false,"id":768854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":768855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janse, Jan H.","contributorId":215555,"corporation":false,"usgs":false,"family":"Janse","given":"Jan","email":"","middleInitial":"H.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Troost, Tineke A.","contributorId":218276,"corporation":false,"usgs":false,"family":"Troost","given":"Tineke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Wijk, Dianneke","contributorId":215557,"corporation":false,"usgs":false,"family":"van Wijk","given":"Dianneke","email":"","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mooij, Wolf M.","contributorId":215556,"corporation":false,"usgs":false,"family":"Mooij","given":"Wolf","email":"","middleInitial":"M.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Janssen, Annette B. G.","contributorId":200076,"corporation":false,"usgs":false,"family":"Janssen","given":"Annette B. G.","affiliations":[],"preferred":false,"id":768860,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70205307,"text":"70205307 - 2019 - Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface","interactions":[],"lastModifiedDate":"2019-09-13T14:38:35","indexId":"70205307","displayToPublicDate":"2019-08-12T14:35:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3047,"text":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface","docAbstract":"Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0343","usgsCitation":"Manlove, K., Sam, L., Borremans, B., Cassirer, E.F., Miller, R.S., Pepin, K., Besser, T.E., and Cross, P., 2019, Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 374, no. 1782, 20180343, https://doi.org/10.1098/rstb.2018.0343.","productDescription":"20180343","ipdsId":"IP-103606","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467372,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711312","text":"Publisher Index Page"},{"id":367417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Manlove, K.R. 0000-0002-7200-5236","orcid":"https://orcid.org/0000-0002-7200-5236","contributorId":218981,"corporation":false,"usgs":false,"family":"Manlove","given":"K.R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":770820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sam, L.","contributorId":218982,"corporation":false,"usgs":false,"family":"Sam","given":"L.","email":"","affiliations":[],"preferred":false,"id":770821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borremans, B. 0000-0002-7779-4107","orcid":"https://orcid.org/0000-0002-7779-4107","contributorId":218983,"corporation":false,"usgs":false,"family":"Borremans","given":"B.","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":770822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cassirer, E. Frances","contributorId":198303,"corporation":false,"usgs":false,"family":"Cassirer","given":"E.","email":"","middleInitial":"Frances","affiliations":[],"preferred":false,"id":770826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, R. S.","contributorId":172739,"corporation":false,"usgs":false,"family":"Miller","given":"R.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":770823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pepin, K. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":218984,"corporation":false,"usgs":false,"family":"Pepin","given":"K.","email":"","affiliations":[{"id":39647,"text":"USDA-APHIS","active":true,"usgs":false}],"preferred":false,"id":770824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Besser, T. E. 0000-0003-0449-1989","orcid":"https://orcid.org/0000-0003-0449-1989","contributorId":215110,"corporation":false,"usgs":false,"family":"Besser","given":"T.","email":"","middleInitial":"E.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":770825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cross, Paul","contributorId":218980,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770819,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70205208,"text":"70205208 - 2019 - Confronting models with data: The challenges of estimating disease spillover","interactions":[],"lastModifiedDate":"2019-09-06T10:33:08","indexId":"70205208","displayToPublicDate":"2019-08-12T10:29:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Confronting models with data: The challenges of estimating disease spillover","docAbstract":"For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution, and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife-livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines.","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0435","usgsCitation":"Cross, P.C., Prosser, D., Ramey, A.M., Hanks, E.M., and Pepin, K., 2019, Confronting models with data: The challenges of estimating disease spillover: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 374, no. 1782, 20180435, https://doi.org/10.1098/rstb.2018.0435.","productDescription":"20180435","ipdsId":"IP-103613","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711303","text":"Publisher Index Page"},{"id":367254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":770371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M. 0000-0003-0345-7164","orcid":"https://orcid.org/0000-0003-0345-7164","contributorId":210840,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":770372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":770373,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204739,"text":"70204739 - 2019 - Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","interactions":[],"lastModifiedDate":"2019-08-15T09:49:41","indexId":"70204739","displayToPublicDate":"2019-08-12T09:46:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","docAbstract":"Fire science emphasizes that mitigation actions on residential property, including structural hardening and maintaining defensible space, can reduce the risk of wildfire at a home. Accordingly, a rich body of social science literature investigates the determinants of wildfire risk mitigation behaviors of residents living in fire-prone areas. Here, we investigate relationships among wildfire hazards, residents’ risk perceptions, and conditions associated with mitigation actions using a combination of simulated wildfire conditions, household survey responses, and professionally assessed parcel characteristic data. We estimate a simultaneous model of these data that accounts for potential direct feedbacks between risk perceptions and parcel-level conditions. We also compare the use of self-reported versus assessed parcel-level data for estimating these relationships. Our analysis relies on paired survey and assessment data for approximately 2000 homes in western Colorado. Our simultaneous model demonstrates dual-directional interactions between risk perceptions and conditions associated with mitigation actions, with important implications for inference from simpler approaches. In addition to improving general understanding of decision-making about risk and natural hazards, our findings can support the effectiveness of publicly supported programs intended to encourage mitigation to reduce society’s overall wildfire risk.","language":"English","publisher":"MDPI","doi":"10.3390/fire2030046","usgsCitation":"Meldrum, J., Brenkert-Smith, H., Champ, P.A., Gomez, J., Falk, L.C., and Barth, C.M., 2019, Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling: Fire, v. 2, no. 3, 46, 18 p., https://doi.org/10.3390/fire2030046.","productDescription":"46, 18 p.","ipdsId":"IP-109622","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire2030046","text":"Publisher Index Page"},{"id":366559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":768256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":768257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomez, Jamie","contributorId":218078,"corporation":false,"usgs":false,"family":"Gomez","given":"Jamie","email":"","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Lilia C.","contributorId":210655,"corporation":false,"usgs":false,"family":"Falk","given":"Lilia","email":"","middleInitial":"C.","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":768261,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203978,"text":"sir20195063 - 2019 - Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","interactions":[],"lastModifiedDate":"2024-01-22T22:05:30.263439","indexId":"sir20195063","displayToPublicDate":"2019-08-12T06:05:02","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5063","displayTitle":"Estimating Potential Wetland Extent along Selected River Reaches in Indiana using Streamflow Statistics and Flood-Inundation Mapping Techniques","title":"Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","docAbstract":"In this study potential wetland extents were estimated for 12 river reaches covering about 750 river miles in Indiana and parts of Illinois and Ohio. The study was completed by the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service. This study follows and adds to the work completed in a pilot study and determines that potential wetland extents can be estimated using streamflow statistics, streamgage data, and flood-inundation mapping techniques.
The study was designed to assist in the Agricultural Conservation Easement Program. The Agricultural Conservation Easement Program is a voluntary program administered by the Natural Resources Conservation Service that provides technical and financial assistance to private landowners and Tribes to restore, protect, and enhance wetlands in exchange for retiring eligible land from agriculture. For a site to be eligible for wetland restoration, it should be in a zone with sustained or frequent flooding. This study calculated the flows that lasted for a period of 7 consecutive days on average at least once every 2 years (a value termed the “7MQ2”) for all the U.S. Geological Survey streamgages within the selected river reaches. These 7MQ2 flows were related to the stage-discharge tables for each streamgage, and a corresponding water-surface elevation was determined. Maps of estimated wetland extent were prepared using the 7MQ2 inundation elevation data in conjunction with bare-earth land-surface elevation data made publicly available through the online geospatial data clearinghouses of Indiana, Illinois, and Ohio. Flood-inundation mapping techniques were applied with the aid of geographic information system software to generate water-surface planes that represent inundation elevations associated with the 7MQ2 streamflow. Land-surface elevation data from high-resolution digital elevation models were subtracted from the water-surface planes to produce maps of wetland extent. The 12 map products, including datasets and geoprocessing tools, produced from this study will aid the National Resources Conservation Service and its partners with the onsite inundation-zone verification in agricultural land for potential restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195063","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service","usgsCitation":"Fowler, K.K., Sperl, B.J., and Kim, M.H., 2019, Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques: U.S. Geological Survey Scientific Investigations Report 2019–5063, 12 p., https://doi.org/10.3133/sir20195063.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097069","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":366436,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXDJ8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets related to wetland extent maps for 12 stream reaches covering approximately 750 river miles in Indiana"},{"id":424708,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108893.htm","linkFileType":{"id":5,"text":"html"},"description":"108893"},{"id":424707,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108892.htm","linkFileType":{"id":5,"text":"html"},"description":"108892"},{"id":366472,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://wim.usgs.gov/geonarrative/indianawetlands/","text":"USGS story map","linkHelpText":"– Geo-narrative"},{"id":366438,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5063/coverthb2.jpg"},{"id":366435,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5063/sir20195063.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5063"}],"country":"United States","state":"Illinois, Indiana, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.582763671875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 37.21283151445594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Scenario planning is a useful tool for identifying key vulnerabilities of ecological systems to changing climates, informed by the potential outcomes for a set of divergent, plausible, and relevant climate scenarios. We evaluated potential vulnerabilities of grassland communities to changing climate in the Southern Great Plains (SGP) and the Landscape Conservation Design pilot area (LCD) for the U.S. Fish and Wildlife Service, Science Applications Program, Great Plains Landscape Conservation Cooperative. Four climate scenarios (warm-dry, warm-wet, hot-dry, and hot-wet) from atmospheric-ocean general circulation models were selected to represent a suite of plausible future climatic conditions. For each scenario, and for contemporary climatic conditions, we predicted the spatial patterns of relative productivity for indicator grass species using statistical models of relative above-ground net primary productivity (hereafter, productivity) based on temperature, precipitation, and soil texture (percent sand, silt, or clay).
Two indicator grass species were selected to represent each of four focal grassland communities: semi-desert grasslands, shortgrass prairie, mixed-grass prairie, and tallgrass prairie. Changes in spatial patterning of bioclimatic conditions conducive for each indicator species as predicted for each climate scenario relative to current land use were used to evaluate potential vulnerability and conservation opportunities for grassland communities. Specifically, the following questions were addressed for each focal grassland community: (1) Where is the productivity of each species predicted to increase, decrease, or remain stable relative to estimated contemporary productivity for the SGP and LCD pilot area, (2) where is the productivity of the two indicator species for each community predicted to increase, decrease, or remain stable, (3) which grassland communities are most vulnerable to changes in composition and vertical structure, (4) how do current land-use patterns contribute to potential vulnerabilities of grassland communities for the climate scenarios evaluated, and (5) how can managers use the vulnerabilities identified to evaluate conservation opportunities in the SGP and LCD?
Current land-use patterns, in combination with the potential effects of a changing climate, pose greater risks to mixed-grass and tallgrass prairies of the SGP compared to semi-desert grasslands and shortgrass prairie. For most climate scenarios evaluated, bioclimatic conditions conducive to the taller species were predicted to contract within some or all the current distribution of mixed-grass and tallgrass prairies within the SGP. An increase in precipitation, however, could potentially ameliorate the negative effects of increasing temperatures as evidenced by higher productivity for the hot-wet scenario compared to the other scenarios for the most vulnerable species. Compounding their greater vulnerability to increasing temperatures coupled with decreasing precipitation, the mixed-grass and tallgrass prairies have been greatly fragmented and converted, primarily by agriculture. In contrast, the climate scenarios evaluated are generally conducive to stable or increasing productivity of indicator species for semi-desert grasslands and shortgrass prairie. In addition, conversion and fragmentation of semi-desert grasslands and shortgrass prairie were relatively low. These results suggest that the synergistic effects of land use and changing climatic conditions could have the greatest effects on the composition and structure of mixed-grass and tallgrass prairies in the SGP. ScienceBase data release files that support this report are available at https://doi.org/10.5066/P9DGJHEP
(Manier and others, 2019).
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
The loss of species diversity and plant community structure throughout the temperate deciduous forests of North America have often been attributed to overbrowsing by white-tailed deer (Odocoileus virginanus). Slow species recovery following removal from browsing, or reduction in deer density, has been termed a legacy effect of past deer herbivory. However, vegetation legacy effects have also coincided with changes to soil chemistry throughout the north-eastern USA. In this paper, we assess the viability of soil chemistry (i.e. pH, extractable nutrients and extractable metals) and other factors (topography, light, overstory basal area and location) as alternative explanations for a lack of vegetation recovery. We compared the relative effects of soil chemistry, site conditions and short-term (1–2 year) deer exclusion on single-species occupancy probabilities of 10 plant taxa common to oak-hickory forests in central Pennsylvania. We found detection for all modelled species was constant and high (
The eastern North American white oaks, a complex of approximately 16 potentially interbreeding species, have become a classic model for studying the genetic nature of species in a syngameon. Genetic work over the past two decades has demonstrated the reality of oak species, but gene flow between sympatric oaks raises the question of whether there are conserved regions of the genome that define oak species. Does gene flow homogenize the entire genome? Do the regions of the genome that distinguish a species in one part of its range differ from the regions that distinguish it in other parts of its range, where it grows in sympatry with
different species? Or are there regions of the genome that are relatively conserved across species ranges? In this study, we revisit seven species of the eastern North American white oak syngameon using a set of 80 single-nucleotide polymorphisms (SNPs) selected in a previous study because they show differences among, and consistency within, the species. We test the hypothesis that there exist segments of the genome that do not become homogenized by repeated introgression, but retain distinct alleles characteristic of each species. We undertake a range-wide sampling to investigate whether SNPs that appeared to be fixed based on a relatively small sample in our previous work are fixed or nearly fixed across the range of the species. Each of the seven species remains genetically distinct across its range, given our diagnostic set of markers, with relatively few individuals exhibiting admixture of multiple species. SNPs map back to all 12 Quercus linkage groups (chromosomes) and are separated from each other by an average of 7.47 million bp (± 8.74 million bp, SD), but are significantly clustered relative to a random null distribution, suggesting that our SNP toolkit reflects genome-wide patterns of divergence while potentially being concentrated in regions of the genome that reflect a higher-than-average history of among-species divergence. This application of a DNA toolkit designed for the simple problem of identifying species in the field has two important implications. First, the eastern North American white oak syngameon is composed of entities that most taxonomists would consider “good species.” Second, and more fundamentally, species in the syngameon are genetically coherent because characteristic portions of the genome remain divergent despite a history of introgression. Understanding the conditions under which some loci diverge while others introgress is key to understanding the origins and maintenance of global tree diversity.
Quagga (Dreissena rostriformis bugensis) and zebra (D. polymorpha) mussels are broadcast spawners that produce planktonic, free swimming veligers, a life history strategy dissimilar to native North American freshwater bivalves. Dreissenid veligers require highly nutritious food to grow and survive, and thus may be susceptible to increased mortality rates during harsh environmental conditions like cyanobacteria blooms. However, the impact of cyanobacteria and one of the toxins they can produce (microcystin) has not been evaluated in dreissenid veligers. Therefore, we exposed dreissenid veligers to eleven distinct cultures (isolates) of cyanobacteria representing Anabaena, Aphanizomenon, Dolichospermum, Microcystis, and Planktothrixspecies and the cyanotoxin microcystin to determine the lethality of cyanobacteria on dreissenid veligers. Six-day laboratory bioassays were performed in microplates using dreissenid veligers collected from the Detroit River, Michigan, USA. Veligers were exposed to increasing concentrations of cyanobacteria and microcystin using the green algae Chlorella minutissima as a control. Based on dose response curves formulated from a Probit model, the LC50 values for cyanobacteria used in this study range between 15.06 and 135.06 μg/L chlorophyll-a, with the LC50 for microcystin-LR at 13.03 μg/L. Because LC50 values were within ranges observed in natural waterbodies, it is possible that dreissenid recruitment may be suppressed when veliger abundances overlap with seasonal cyanobacteria blooms. Thus, the toxicity of cyanobacteria to dreissenid veligers may be useful to include in models forecasting dreissenid mussel abundance and spread.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoenv.2019.109426","usgsCitation":"Boegehold, A.G., Johnson, N., and Kashian, D.R., 2019, Bloom forming cyanobacteria can adversely affect zebra and quagga mussel veligers: Ecotoxicology and Environmental Safety, v. 182, Article 109426, https://doi.org/10.1016/j.ecoenv.2019.109426.","productDescription":"Article 109426","ipdsId":"IP-109520","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoenv.2019.109426","text":"Publisher Index Page"},{"id":366365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"182","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boegehold, Anna G.","contributorId":205600,"corporation":false,"usgs":false,"family":"Boegehold","given":"Anna","email":"","middleInitial":"G.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":767856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":767855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kashian, Donna R.","contributorId":205602,"corporation":false,"usgs":false,"family":"Kashian","given":"Donna","email":"","middleInitial":"R.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":767857,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263893,"text":"70263893 - 2019 - High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes","interactions":[],"lastModifiedDate":"2025-02-27T15:12:24.556905","indexId":"70263893","displayToPublicDate":"2019-08-08T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20190,"text":"Pure & Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes","docAbstract":"The 2008 MW7.9 Wenchuan and the 2013 MW6.6 Lushan earthquakes, which both occurred on the Longmen Shan thrust belt, show some interesting similarities and differences. Whereas the Wenchuan earthquake entailed a rupture zone that extended about 300 km northeastward, with fault slip extending to the surface, the Lushan earthquake was the result of a buried and much more compact zone of rupture. The high-frequency ground motions, however, for these two earthquakes, as measured by the peak ground acceleration, were evidently influenced by neither the extent of rupture nor the presence or absence of surface rupture. The source parameters for these two earthquakes tend to confirm the idea that high-frequency ground motion is controlled by stress changes in the rupture zone that give rise to the radiated ground acceleration. The apparent stresses for the Wenchuan and Lushan earthquakes are about 0.5 and 0.75 MPa, respectively, and the stress drops, in the same order, are about 2.5 and 3.5 MPa. The ratios of average stress drop to apparent stress are in the range 4.5–5 for both events, consistent with expectations based on the Brune (J Geophys Res 75(26):4997–5009, 1970) source model.
","language":"English","publisher":"Springer","doi":"10.1007/s00024-019-02291-4","usgsCitation":"Meng, L., Zang, Y., and Zhou, L., 2019, High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes: Pure & Applied Geophysics, v. 177, p. 81-93, https://doi.org/10.1007/s00024-019-02291-4.","productDescription":"13 p.","startPage":"81","endPage":"93","ipdsId":"IP-108067","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Lushan, 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Lingyuan","contributorId":351564,"corporation":false,"usgs":false,"family":"Meng","given":"Lingyuan","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zang, Yang","contributorId":351563,"corporation":false,"usgs":false,"family":"Zang","given":"Yang","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Longquan","contributorId":351562,"corporation":false,"usgs":false,"family":"Zhou","given":"Longquan","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928932,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204693,"text":"70204693 - 2019 - Estimation of base flow by optimal hydrograph separation for the conterminous United States and implications for national-extent hydrologic models","interactions":[],"lastModifiedDate":"2019-08-09T12:01:26","indexId":"70204693","displayToPublicDate":"2019-08-07T11:53:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of base flow by optimal hydrograph separation for the conterminous United States and implications for national-extent hydrologic models","docAbstract":"Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow, added to gwres_flow, produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.
","language":"English","publisher":"MDPI","doi":"10.3390/w11081629","usgsCitation":"Foks, S., Raffensperger, J.P., Penn, C.A., and Driscoll, J.M., 2019, Estimation of base flow by optimal hydrograph separation for the conterminous United States and implications for national-extent hydrologic models: Water, v. 11, no. 8, 1629, 25 p., https://doi.org/10.3390/w11081629.","productDescription":"1629, 25 p.","ipdsId":"IP-104087","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467387,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11081629","text":"Publisher Index Page"},{"id":437370,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XF3C11","text":"USGS data 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Center","active":true,"usgs":true}],"preferred":true,"id":768087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Penn, Colin A. 0000-0002-5195-2744","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":203851,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research 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conditions is not well understood for marine predators, especially sea turtles. We tagged loggerhead turtles (Caretta caretta) with satellite-linked depth loggers in the Gulf of Mexico, where there is a minimal amount of dive data for this species. We tested for associations between four measurements of dive behavior (total daily dive frequency, frequency of dives to the bottom, frequency of long dives and time-at-depth) and both oceanographic conditions (sea surface temperature [SST], net primary productivity [NPP]) and behavioral mode (inter-nesting, migration, or foraging). From 2011–2013 we obtained 26 tracks from 25 adult female loggerheads tagged after nesting in the Gulf of Mexico. All turtles remained in the Gulf of Mexico and spent about 10% of their time at the surface (10% during inter-nesting, 14% during migration, 9% during foraging). Mean total dive frequency was 41.9 times per day. Most dives were ≤ 25 m and between 30–40 min. During inter-nesting and foraging, turtles dived to the bottom 95% of days. SST was an important explanatory variable for all dive patterns; higher SST was associated with more dives per day, more long dives and more dives to the seafloor. Increases in NPP were associated with more long dives and more dives to the bottom, while lower NPP resulted in an increased frequency of overall diving. Longer dives occurred more frequently during migration and a higher proportion of dives reached the seafloor during foraging when SST and NPP were higher. Our study stresses the importance of the interplay between SST and foraging resources for influencing dive behavior.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0220372","usgsCitation":"Iverson, A., Fujisaki, I., Lamont, M.M., and Hart, K., 2019, Loggerhead sea turtle (Caretta caretta) diving changes with productivity, behavioral mode, and sea surface temperature: PLoS ONE, v. 14, no. 8, e0220372, 19 p., https://doi.org/10.1371/journal.pone.0220372.","productDescription":"e0220372, 19 p.","ipdsId":"IP-101494","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467388,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0220372","text":"Publisher Index Page"},{"id":437371,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PY9YBZ","text":"USGS data release","linkHelpText":"Dive data for loggerhead sea turtles in the Gulf of Mexico, 2011-2013"},{"id":366856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cuba, Mexico, United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.001953125,\n 27.430289738862594\n ],\n [\n -82.99072265625,\n 29.592565403314087\n ],\n [\n -84.35302734375,\n 30.259067203213018\n ],\n [\n -85.23193359375,\n 30.031055426540206\n ],\n [\n -87.14355468749999,\n 30.694611546632277\n ],\n [\n -88.57177734375,\n 30.751277776257812\n ],\n [\n -90.263671875,\n 29.80251790576445\n ],\n [\n -91.34033203125,\n 29.57345707301757\n ],\n [\n -91.93359375,\n 29.973970240516614\n ],\n [\n -92.8125,\n 29.80251790576445\n ],\n [\n -94.28466796874999,\n 29.84064389983441\n ],\n [\n -97.36083984375,\n 28.14950321154457\n ],\n [\n -97.62451171875,\n 26.62781822639305\n ],\n [\n -97.40478515625,\n 25.720735134412106\n ],\n [\n -97.88818359375,\n 25.363882272740256\n ],\n [\n -98.0859375,\n 22.39071391683855\n ],\n [\n -96.328125,\n 18.8543103618898\n ],\n [\n -94.59228515625,\n 18.06231230454674\n ],\n [\n -92.17529296875,\n 18.47960905583197\n ],\n [\n -91.56005859375,\n 18.291949733550336\n ],\n [\n -91.01074218749999,\n 18.812717856407776\n ],\n [\n -90.3515625,\n 19.9526963975442\n ],\n [\n -90.19775390625,\n 20.96143961409684\n ],\n [\n -87.1875,\n 21.166483858206583\n ],\n [\n -87.69287109375,\n 19.828725387681168\n ],\n [\n -87.86865234374999,\n 19.539084135509334\n ],\n [\n -87.9345703125,\n 18.437924653474408\n ],\n [\n -78.5302734375,\n 22.28909641872304\n ],\n [\n -82.001953125,\n 27.430289738862594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Iverson, Autumn 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":218322,"corporation":false,"usgs":true,"family":"Iverson","given":"Autumn","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":769006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":769007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamont, Margaret M. 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":218323,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218324,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769009,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204633,"text":"70204633 - 2019 - Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2019-08-12T09:30:33","indexId":"70204633","displayToPublicDate":"2019-08-07T09:01:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem","docAbstract":"Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. Understanding climatic correlates associated with spread is needed to successfully manage impacts from forest pathogens. In the Greater Yellowstone Ecosystem since 2000 mountain pine beetles have killed 75 percent of the mature cone-bearing trees, and 40.9 percent of monitored trees have been infected with white pine blister rust. We identified models of white pine blister rust infection that indicate an August and September interaction between relative humidity and temperature were better predictors of white pine blister rust infection in whitebark pine than location and site characteristics in the Greater Yellowstone Ecosystem. The climate conditions conducive to white pine blister rust occur throughout the ecosystem, but larger trees in relatively warm and humid conditions were more likely to be infected between 2000 and 2018. We mapped the infection probability over the past two decades to identify coarse-scale patterns of climate conditions conducive to white pine blister rust infection in whitebark pine.","language":"English","publisher":"MDPI","doi":"10.3390/f10080666","usgsCitation":"Thoma, D., Shanahan, E.K., and Irvine, K., 2019, Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem: Forests, v. 10, no. 8, 16 p., https://doi.org/10.3390/f10080666.","productDescription":"16 p.","ipdsId":"IP-109650","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f10080666","text":"Publisher Index Page"},{"id":366366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.32421875,\n 42.76314586689492\n ],\n [\n -108.2373046875,\n 42.76314586689492\n ],\n [\n -108.2373046875,\n 45.72152152227954\n ],\n [\n -112.32421875,\n 45.72152152227954\n ],\n [\n -112.32421875,\n 42.76314586689492\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Thoma, David","contributorId":190258,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[],"preferred":false,"id":767850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shanahan, Erin K.","contributorId":217938,"corporation":false,"usgs":false,"family":"Shanahan","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":767851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":217937,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":767849,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203464,"text":"sir20195044 - 2019 - Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California","interactions":[],"lastModifiedDate":"2019-08-07T08:45:48","indexId":"sir20195044","displayToPublicDate":"2019-08-06T12:45:52","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5044","displayTitle":"Using the Precipitation-Runoff Modeling System to Predict Seasonal Water Availability in the Upper Klamath River Basin, Oregon and California","title":"Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California","docAbstract":"Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration.
The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195044","collaboration":"Prepared in cooperation with the U.S. Bureau of Reclamation","usgsCitation":"Risley, J.C., 2019, Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California: U.S. Geological Survey Scientific Investigations Report 2019–5044, 37 p., https://doi.org/10.3133/sir20195044.","productDescription":"vi, 37 p.","onlineOnly":"Y","ipdsId":"IP-098864","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":366315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5044/coverthb.jpg"},{"id":366316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5044/sir20195044.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5044"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Upper Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.42041015624999,\n 40.76806170936614\n ],\n [\n -119.94323730468749,\n 40.76806170936614\n ],\n [\n -119.94323730468749,\n 43.205175817237304\n ],\n [\n -123.42041015624999,\n 43.205175817237304\n ],\n [\n -123.42041015624999,\n 40.76806170936614\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201