Coral growth anomalies (GAs) are tumor-like protrusions that are detrimental to coral health, affecting both the coral skeleton and soft tissues. These lesions are increasingly found throughout the tropics and are commonly associated with high human population density, yet little is known about the molecular pathology of the disease. Here, we investigate the metabolic impacts of GAs through 1H nuclear magnetic resonance (NMR) metabolomics in Porites compressa tissues from a site of high disease prevalence (Coconut Island, Hawaii). We putatively identified 18 metabolites (8.1% of total annotated features) through complementary 1H and 1H–13C heteronuclear single quantum correlation NMR data that increase confidence in pathway analyses and may bolster future coral metabolite annotation efforts. Extract yield was elevated in both GA and unaffected (normal tissue from a diseased colony) compared to reference (normal tissue from GA-free colony) samples, potentially indicating elevated metabolic activity in GA-impacted colonies. Relatively high variation in metabolomic profiles among coral samples of the same treatment (i.e., inter-colony variation) confounded data interpretation, however, analyses of paired GA and unaffected samples identified 73 features that differed between these respective metabolome types. These features were largely annotated as unknowns, but 1-methylnicotinamide and trigonelline were found to be elevated in GA samples, while betaine, glycine, and histamine were lower in GA samples. Pathway analyses indicate decreased choline oxidation in GA samples, making this a pathway of interest for future targeted studies. Collectively, our results provide unique insights into GA pathophysiology by showing these lesions alter both the absolute and relative metabolism of affected colonies and by identifying features (metabolites and unknowns) and metabolic pathways of interest in GA pathophysiology going forward.
","language":"English","publisher":"Springer","doi":"10.1007/s00338-021-02125-7","usgsCitation":"Andersson, E.R., Day, R.D., Work, T.M., Anderson, P.E., Woodley, C.M., and Schock, T.B., 2021, Identifying metabolic alterations associated with coral growth anomalies using 1H NMR metabolomics: Coral Reefs, v. 40, p. 1195-1209, https://doi.org/10.1007/s00338-021-02125-7.","productDescription":"15 p.","startPage":"1195","endPage":"1209","ipdsId":"IP-126580","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467239,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s00338-021-02125-7","text":"External Repository"},{"id":386978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","noUsgsAuthors":false,"publicationDate":"2021-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Andersson, Erik R.","contributorId":260786,"corporation":false,"usgs":false,"family":"Andersson","given":"Erik","email":"","middleInitial":"R.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Rusty D.","contributorId":260787,"corporation":false,"usgs":false,"family":"Day","given":"Rusty","email":"","middleInitial":"D.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":818741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Paul E.","contributorId":260788,"corporation":false,"usgs":false,"family":"Anderson","given":"Paul","email":"","middleInitial":"E.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodley, Cheryl M.","contributorId":260789,"corporation":false,"usgs":false,"family":"Woodley","given":"Cheryl","email":"","middleInitial":"M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818743,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schock, Tracey B.","contributorId":260790,"corporation":false,"usgs":false,"family":"Schock","given":"Tracey","email":"","middleInitial":"B.","affiliations":[{"id":47720,"text":"NIST","active":true,"usgs":false}],"preferred":false,"id":818744,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222099,"text":"70222099 - 2021 - Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","interactions":[],"lastModifiedDate":"2021-07-20T12:18:00.475837","indexId":"70222099","displayToPublicDate":"2021-06-12T07:15:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2476,"text":"Journal of Thermal Biology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","docAbstract":"Under a warmer future climate, thermal refuges could facilitate the persistence of species relying on cold-water habitat. Often these refuges are small and easily missed or smoothed out by averaging in models. Thermal infrared (TIR) imagery can provide empirical water surface temperatures that capture these features at a high spatial resolution (<1 m) and over tens of kilometers. Our study examined how TIR data could be used along with spatial stream network (SSN) models to characterize thermal regimes spatially in the Middle Fork John Day (MFJD) River mainstem (Oregon, USA). We characterized thermal variation in seven TIR longitudinal temperature profiles along the MFJD mainstem and compared them with SSN model predictions of stream temperature (for the same time periods as the TIR profiles). TIR profiles identified reaches of the MFJD mainstem with consistently cooler temperatures across years that were not consistently captured by the SSN prediction models. SSN predictions along the mainstem identified ~80% of the 1-km reach scale temperature warming or cooling trends observed in the TIR profiles. We assessed whether landscape features (e.g., tributary junctions, valley confinement, geomorphic reach classifications) could explain the fine-scale thermal heterogeneity in the TIR profiles (after accounting for the reach-scale temperature variability predicted by the SSN model) by fitting SSN models using the TIR profile observation points. Only the distance to the nearest upstream tributary was identified as a statistically significant landscape feature for explaining some of the thermal variability in the TIR profile data. When combined, TIR data and SSN models provide a data-rich evaluation of stream temperature captured in TIR imagery and a spatially extensive prediction of the network thermal diversity from the outlet to the headwaters.
At present, the most reliable information for inferring storm-time ground electric fields along electrical transmission lines comes from coarsely sampled, national-scale magnetotelluric (MT) data sets, such as that provided by the EarthScope USArray program. An underlying assumption in the use of such data is that they adequately sample the spatial heterogeneity of the surface relationship between geomagnetic and geoelectric fields. Here, we assess the degree to which the density of MT data sampling affects geoelectric hazard assessments. For electrical transmission networks in each of four focus regions across the contiguous United States, we perform two parallel band-limited (101–103 s) hazard analyses: one using only USArray-style (∼70-km station spacing) MT data, and one incorporating denser (≪70-km station spacing) MT data. We find that the use of USArray-style MT sampling alone provides a useful first-order estimate of integrated geoelectric fields along electrical transmission lines. However, we also find that the use of higher density MT data can in some areas lead to order-of-magnitude differences in line-averaged electric field estimates at the level of individual transmission lines and can also yield significant differences in subregional hazard patterns. As we demonstrate using variogram plots, these differences reflect short-spatial-scale variability in Earth conductivity, which in turn reflects regional lithotectonic structure and history. We also provide a cautionary example in the use of electrical conductivity models to predict dense MT data; although valuable for hazard applications, models may only be able to reproduce surface geoelectric fields as captured by the MT data from which they were derived.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002693","usgsCitation":"Murphy, B.S., Lucas, G., Love, J.J., Kelbert, A., Bedrosian, P.A., and Rigler, E.J., 2021, Magnetotelluric sampling and geoelectric hazard estimation: Are national-scale surveys sufficient?: AGU Space Weather, v. 19, no. 7, e2020SW002693, 24 p., https://doi.org/10.1029/2020SW002693.","productDescription":"e2020SW002693, 24 p.","ipdsId":"IP-128631","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002693","text":"Publisher Index Page"},{"id":387180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203124999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 39.70718665682654\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.140625,\n 37.23032838760387\n ],\n [\n -94.921875,\n 37.23032838760387\n ],\n [\n -94.921875,\n 41.64007838467894\n ],\n [\n -99.140625,\n 41.64007838467894\n ],\n [\n -99.140625,\n 37.23032838760387\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.833984375,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 33.211116472416855\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.240234375,\n 44.276671273775186\n ],\n [\n -83.671875,\n 44.276671273775186\n ],\n [\n -83.671875,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 44.276671273775186\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":223556,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg M.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":819287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819291,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263493,"text":"70263493 - 2021 - The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data","interactions":[],"lastModifiedDate":"2025-02-13T14:57:02.527753","indexId":"70263493","displayToPublicDate":"2021-06-11T08:25:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data","docAbstract":"The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source-based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity (IMMI) using velocity and acceleration data, leveraging co-located sensors to avoid problematic signals. An event detection is issued when the observed IMMI exceeds a given threshold(s). We find a two-station detection method using IMMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes (N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify IMMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB021053","usgsCitation":"Kilb, D., Bunn, J.J., Saunders, J.K., Cochran, E.S., Minson, S.E., Baltay Sundstrom, A.S., O’Rourke, C.T., Hoshiba, M., and Kodera, Y., 2021, The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data: JGR Solid Earth, v. 126, no. 7, e2020JB021053, 25 p., https://doi.org/10.1029/2020JB021053.","productDescription":"e2020JB021053, 25 p.","ipdsId":"IP-127285","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487640,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb021053","text":"Publisher Index Page"},{"id":481971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n 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0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Rourke, Colin T 0000-0001-5403-4685","orcid":"https://orcid.org/0000-0001-5403-4685","contributorId":290635,"corporation":false,"usgs":true,"family":"O’Rourke","given":"Colin","email":"","middleInitial":"T","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927151,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoshiba, Mitsuyuki","contributorId":216382,"corporation":false,"usgs":false,"family":"Hoshiba","given":"Mitsuyuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":927152,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kodera, Yuki","contributorId":290636,"corporation":false,"usgs":false,"family":"Kodera","given":"Yuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":927153,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229492,"text":"70229492 - 2021 - Caution is warranted when using animal space-use and movement to infer behavioral states","interactions":[],"lastModifiedDate":"2022-03-09T12:58:06.746424","indexId":"70229492","displayToPublicDate":"2021-06-11T06:53:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Caution is warranted when using animal space-use and movement to infer behavioral states","docAbstract":"Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.
Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.
For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.
Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00264-8","usgsCitation":"Buderman, F.E., Gingery, T.M., Diefenbach, D.R., Gigliotti, L., Begley-Miller, D., McDill, M.E., Wallingford, B., Rosenberry, C., and Drohan, P.J., 2021, Caution is warranted when using animal space-use and movement to infer behavioral states: Movement Ecology, v. 9, 30, 12 p., https://doi.org/10.1186/s40462-021-00264-8.","productDescription":"30, 12 p.","ipdsId":"IP-126195","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00264-8","text":"Publisher Index Page"},{"id":396898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Buderman, Frances E.","contributorId":171634,"corporation":false,"usgs":false,"family":"Buderman","given":"Frances","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gingery, Tess M.","contributorId":204865,"corporation":false,"usgs":false,"family":"Gingery","given":"Tess","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":837601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":837599,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gigliotti, Laura C.","contributorId":204828,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":837602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Begley-Miller, Danielle","contributorId":288270,"corporation":false,"usgs":false,"family":"Begley-Miller","given":"Danielle","affiliations":[{"id":54482,"text":"Teatown Lake Reservation","active":true,"usgs":false}],"preferred":false,"id":837603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDill, Marc E.","contributorId":264499,"corporation":false,"usgs":false,"family":"McDill","given":"Marc","email":"","middleInitial":"E.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":837654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wallingford, Bret D.","contributorId":276207,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret D.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":837604,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosenberry, Christopher S.","contributorId":276209,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":837605,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Drohan, Patrick J.","contributorId":190141,"corporation":false,"usgs":false,"family":"Drohan","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":837655,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221411,"text":"70221411 - 2021 - Diet composition and body condition of polar bears (Ursus maritimus) in relation to sea ice habitat in the Canadian High Arctic","interactions":[],"lastModifiedDate":"2021-06-30T19:05:36.806175","indexId":"70221411","displayToPublicDate":"2021-06-11T06:52:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3093,"text":"Polar Biology","active":true,"publicationSubtype":{"id":10}},"title":"Diet composition and body condition of polar bears (Ursus maritimus) in relation to sea ice habitat in the Canadian High Arctic","docAbstract":"Polar bears (Ursus maritimus) rely on sea ice for hunting marine mammal prey. Declining sea ice conditions associated with climate warming have negatively affected polar bears, especially in the southern portion of their range. At higher latitudes, the transition from multi-year ice to thinner annual ice has been hypothesized to increase biological productivity and potentially improve polar bear foraging conditions. To investigate this possibility, we used quantitative fatty acid signature analysis to characterize the diet composition of 148 polar bears in two high-latitude subpopulations from 2012 to 2014: (1) Viscount Melville Sound, where little is known about marine mammal ecology, and (2) Northern Beaufort Sea, a subpopulation considered stable with comparatively more ecological data. We used adipose tissue lipid content as an index of body condition. To characterize long-term habitat conditions, we examined trends in sea ice metrics from 1979 to 2014 in both regions. Although the diets of bears in both subpopulations were dominated by ringed seal (Pusa hispida, mean biomass consumption = 45%), bears in Viscount Melville Sound showed higher proportional consumption of beluga whale (Delphinapterus leucas; mean biomass consumption = 37%) than any other polar bear subpopulation studied to date. Although the three-year duration of our study precludes long-term insights, relatively lighter sea ice conditions in Viscount Melville Sound were associated with reduced consumption of preferred prey (i.e., ringed seal), especially among female polar bears. Further, polar bears in Viscount Melville sound were in poorer body condition than those in the Northern Beaufort Sea. Our results do not indicate that declining sea ice has had any positive effect on polar bear foraging at high-latitudes.
The Upper Yampa River Basin drains approximately 2,100 square miles west of the Continental Divide in north-western Colorado. There is a growing need to understand potential changes in the quantity and quality of water resources as the basin is undergoing increasing land and water development to support growing municipal, industrial, and recreational needs. The U.S. Geological Survey, in cooperation with stakeholders in the Upper Yampa River Basin water community, began a study to characterize and identify changes in streamflow and selected water-quality constituents, including suspended sediment, Kjeldahl nitrogen, total nitrogen, total phosphorus, and orthophosphate, in the basin. This study used streamflow and water-quality data from selected U.S. Geological Survey sites to provide a better understanding of how major factors, including land use, climate change, and geological features, may influence streamflow and water quality.
Analysis of long-term (1910–2018) and short-term (1992–2018) records of streamflow at main-stem Yampa River and tributary sites indicate downward trends in one or more streamflow statistics, including 1-day maximum, mean, and 7-day minimum. Long-term downward trends in daily mean streamflow in April (22 percent overall) at Yampa River at Steamboat Springs, Colorado, correspond to observed changes in streamflow documented across western North America and the Colorado River Basin that are predominately associated with changes in snowmelt runoff and temperatures. During the short-term period of analysis, decreases in streamflow at main-stem Yampa River and some tributary sites are likely related to changes in consumptive use and reservoir management or, at sites with no upstream flow impoundments, changes in irrigation diversions and climate.
Concentrations of water-quality constituents were typically highest in spring (March, April, and May) during the early snowmelt runoff period as material that is washed off the land surface drains into streams. Highest concentrations occurred slightly later, in May, June, and July, at Yampa River above Stagecoach Reservoir, Colo., and slightly earlier, in February and March at Yampa River at Milner, Colo., indicating that these sites may have different or additional sources of phosphorus from upstream inputs. Yampa River at Milner, Colo., and Yampa River above Elkhead Creek, Colo., had the highest net yields of suspended sediment, Kjeldahl nitrogen, and total phosphorus, and are likely influenced by land use and erosion as the basins of both of these sites are underlain by highly erodible Cretaceous shales.
Upward trends in estimated Kjeldahl nitrogen and total phosphorus concentrations and loads were found at Yampa River at Steamboat Springs, Colo. From 1999 to 2018, the Kjeldahl nitrogen concentration increased by 10 percent or 0.035 milligram per liter, and load increased by 22 percent or 26 tons. Total phosphorus concentration increased by 20 percent or 0.0081 milligram per liter, and loads increased by 41 percent or 6.2 tons. Decreases in streamflow and changes in land use may contribute to these trends.
During multiple summer sampling events at Stagecoach Reservoir, the physical and chemical factors indicated conditions conducive to cyanobacterial blooms, including surface-water temperatures greater than 20 degrees Celsius and total phosphorus and total nitrogen concentrations in exceedance of Colorado Department of Public Health and Environment interim concentrations for water-quality standards. Local geological features (predominately sandstones and shales) and additional inputs from upstream land use likely contribute to the elevated nutrient conditions in Stagecoach Reservoir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215016","isbn":"978-1-4113-4402-0","collaboration":"Prepared in cooperation with Upper Yampa River Watershed Group, Upper Yampa Water Conservancy District, Colorado Water Conservation Board, Yampa-White-Green Basin Roundtable, Mount Werner Water and Sanitation District, Routt County, Colorado, and the city of Steamboat Springs, Colorado","usgsCitation":"Day, N.K., 2021, Assessment of streamflow and water quality in the Upper Yampa River Basin, Colorado, 1992–2018: U.S. Geological Survey Scientific Investigations Report 2021–5016, 45 p., https://doi.org/10.3133/sir20215016.","productDescription":"Report: vii, 45 p.; Data Release","onlineOnly":"N","ipdsId":"IP-118673","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":386393,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5016/coverthb.jpg"},{"id":386394,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5016/sir20215016.pdf","text":"Report","size":"4.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5016"},{"id":386395,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L7S3NQ","text":"USGS data release","linkHelpText":"Input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper Yampa River Basin, Colorado, from 1992-2018"}],"country":"United States","state":"Colorado","otherGeospatial":"Upper Yampa River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.314453125,\n 40.052847601823984\n ],\n [\n -106.424560546875,\n 40.052847601823984\n ],\n [\n -106.424560546875,\n 40.9964840143779\n ],\n [\n -107.314453125,\n 40.9964840143779\n ],\n [\n -107.314453125,\n 40.052847601823984\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Stationarity of environmental preferences is a primary assumption required for any paleoenvironmental reconstruction using fossil materials based upon calibration to modern organisms. Confidence in this assumption decreases the further back in time one goes, and the validity of the assumption that species temperature tolerances have not changed over time has been challenged in Pliocene studies. We use paired UK′37 (unsaturated ketones with 37 carbon atoms) sea surface temperature (SST) and faunal assemblage data to directly calibrate North Atlantic Piacenzian planktonic foraminifer assemblages to Piacenzian alkenone paleotemperature estimates to provide an alternative paleoceanographic reconstruction approach that does not rely on stationarity. In doing so, we extend Pliocene SST estimates to sites where only quantitative faunal assemblage data were previously available and improve the spatial resolution of the North Atlantic SST reconstruction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215051","usgsCitation":"Dowsett, H.J., Robinson, M.M., and Foley, K.M., 2021, Estimating Piacenzian sea surface temperature using an alkenone-calibrated transfer function: U.S. Geological Survey Scientific Investigations Report 2021–5051, 17 p., https://doi.org/10.3133/sir20215051.","productDescription":"Report: vi, 17 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114329","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386375,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.1038/sdata.2015.76","text":"Scientific Data","linkHelpText":"— A global planktic foraminifer census data set for the Pliocene ocean"},{"id":386376,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7959G1S","text":"USGS data release","linkHelpText":"PRISM late Pliocene (Piacenzian) alkenone-derived SST data"},{"id":386374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5051/sir20215051.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5051"},{"id":386373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5051/coverthb.jpg"},{"id":386377,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://www.ncdc.noaa.gov/paleo/study/27310","text":"National Oceanic and Atmospheric Administration, National Centers for Environmental Information","linkHelpText":"— A global planktic foraminifer census data set for the Pliocene ocean, addendum"}],"contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The persistence of coastal marsh is dependent on its ability to maintain elevation relative to sea level, particularly for marshes experiencing high rates of shoreline erosion due to wave-attack, storms, and sea level rise. Sediments eroded at the marsh edge are either delivered onto the marsh platform or into the estuary, the latter resulting in a net loss of marsh sediments and soil carbon. Knowledge on the timing, pattern, and quantity of sediment deposition versus shoreline erosion along the marsh-estuary interface is critical for evaluating the overall health and vulnerability of coastal marshes to future scenarios of sea level rise and for estimating sediment budgets. Here we examined marsh shoreline erosion and sediment deposition for marsh sites experiencing a range of shoreline erosion rates and different levels of wind-wave exposure within the Grand Bay National Estuarine Research Reserve and Wildlife Refuge in Mississippi. We developed a method for calculating an erosion-deposition sediment budget using marsh elevation profiles, shoreline erosion rate, and sediment deposition measurements. Sediment budgets were calculated at four sites with varying shoreline erosion rates. Much of the sediment eroded at the marsh edge can be accounted for as marsh platform deposition, except at the most erosive site, suggesting a possible erosion threshold where eroded sediment mass is greater than platform deposition. Consistent with other studies of marsh creeks, sediment delivery to the marsh platform appears to be largely driven by wave climate. These data suggest that for erosive bay-estuarine shorelines, sediment delivered into the marsh is largely concentrated near the marsh shoreline, although shoreline erosion does not always result in a net loss of sediments from the marsh system in either decadal or annual assessments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2021.107829","usgsCitation":"Smith, K., Terrano, J.F., Khan, N.S., Smith, C., and Pitchford, J.L., 2021, Lateral shoreline erosion and shore-proximal sediment deposition on a coastal marsh from seasonal, storm and decadal measurements: Geomorphology, v. 389, 107829, 16 p., https://doi.org/10.1016/j.geomorph.2021.107829.","productDescription":"107829, 16 p.","ipdsId":"IP-122214","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451930,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2021.107829","text":"Publisher Index Page"},{"id":386866,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Grand Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.76953125,\n 30.246018268082167\n ],\n [\n -88.39736938476562,\n 30.246018268082167\n ],\n [\n -88.39736938476562,\n 30.527962116594164\n ],\n [\n -88.76953125,\n 30.527962116594164\n ],\n [\n -88.76953125,\n 30.246018268082167\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"389","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Kathryn E.L. 0000-0002-7521-7875 kelsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-7521-7875","contributorId":173264,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn","email":"kelsmith@usgs.gov","middleInitial":"E.L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terrano, Joseph F. 0000-0003-3060-7682 jterrano@usgs.gov","orcid":"https://orcid.org/0000-0003-3060-7682","contributorId":173263,"corporation":false,"usgs":true,"family":"Terrano","given":"Joseph","email":"jterrano@usgs.gov","middleInitial":"F.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khan, Nicole S.","contributorId":213942,"corporation":false,"usgs":false,"family":"Khan","given":"Nicole","email":"","middleInitial":"S.","affiliations":[{"id":38935,"text":"Asian School of the Environment, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798","active":true,"usgs":false}],"preferred":false,"id":818476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pitchford, Jonathan L 0000-0003-1168-5087","orcid":"https://orcid.org/0000-0003-1168-5087","contributorId":260687,"corporation":false,"usgs":false,"family":"Pitchford","given":"Jonathan","email":"","middleInitial":"L","affiliations":[{"id":52643,"text":"Grand Bay National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":818478,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222443,"text":"70222443 - 2021 - Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake","interactions":[],"lastModifiedDate":"2021-07-30T14:11:27.923693","indexId":"70222443","displayToPublicDate":"2021-06-10T09:08:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9121,"text":"Frontiers Earth Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake","docAbstract":"Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.
Annual exceedance probability flows at gaged locations and regional regression equations used to estimate annual exceedance probability flows at ungaged locations were developed by the U.S. Geological Survey, in cooperation with the Mississippi Department of Transportation, to improve flood-frequency estimates at rural streams in the alluvial plain of the lower Mississippi River. These estimates were developed using current geospatial data, analytical methods, and annual peak-flow data through September 2017 at 58 streamgages in the alluvial plain of the lower Mississippi River, including 9 in Mississippi, 35 in Arkansas, 4 in Missouri, and 10 in Louisiana. Annual exceedance probability flows presented in this report incorporate streamflow data through the 2017 water year, 32 additional years of record since the previous study in 1985 of flood magnitude and frequency in the Mississippi portion of the alluvial plain of the lower Mississippi River. Ranges for standard error of prediction, average variance of prediction, and pseudo-R2 are 45–61 percent, 0.035–0.059 (log cubic feet per second)2, and 90–94 percent, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215046","collaboration":"Prepared in cooperation with the Mississippi Department of Transportation","usgsCitation":"Anderson, B.T., 2021, Magnitude and frequency of floods in the alluvial plain of the lower Mississippi River, 2017: U.S. Geological Survey Scientific Investigations Report 2021–5046, 15 p., https://doi.org/10.3133/sir20215046.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"N","ipdsId":"IP-118369","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":386320,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5046/images"},{"id":386316,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5046/coverthb.jpg"},{"id":386317,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5046/sir20215046.pdf","text":"Report","size":"2.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5046"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Alluvial plain of the lower Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.36279296875,\n 37.16031654673677\n ],\n [\n -90.3076171875,\n 36.914764288955936\n ],\n [\n -90.791015625,\n 36.19109202182454\n ],\n [\n -91.51611328125,\n 34.867904962568716\n ],\n [\n -92.0654296875,\n 33.99802726234877\n ],\n [\n -92.21923828124999,\n 32.713355353177555\n ],\n [\n -92.83447265624999,\n 30.845647420182598\n ],\n [\n -92.7685546875,\n 29.516110386062277\n ],\n [\n -91.97753906249999,\n 29.11377539511439\n ],\n [\n -90.4833984375,\n 28.844673680771795\n ],\n [\n -89.12109375,\n 28.94086176940557\n ],\n [\n -89.23095703125,\n 29.554345125748267\n ],\n [\n -89.49462890625,\n 30.35391637229704\n ],\n [\n -89.9560546875,\n 30.713503990354965\n ],\n [\n -90.263671875,\n 31.59725256170666\n ],\n [\n -89.75830078125,\n 34.615126683462194\n ],\n [\n -89.07714843749999,\n 35.639441068973944\n ],\n [\n -88.61572265625,\n 36.474306755095235\n ],\n [\n -88.70361328125,\n 36.914764288955936\n ],\n [\n -89.36279296875,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Coseismic landslides are a major source of transportation disruption in mountainous areas, but few approaches exist for rapidly estimating impacts to road networks. We develop a model that links the U.S. Geological Survey (USGS) near-real-time earthquake-triggered landslide hazard model with Open Street Map (OSM) road network data to rapidly estimate segment-level obstruction risk following major earthquake activity worldwide. To train and validate the model, we process OSM data for 15 historical earthquakes and calculate the average segment-level landslide hazard from the USGS model for each event. We then fit a multivariate adaptive regression spline model for the probability of road obstruction as a function of road segment length and landslide hazard, using a training and validation dataset derived from the intersections of road networks with earthquake-triggered landslide inventories. The resulting probabilistic model is well calibrated across a range of earthquake events, with estimated obstruction probabilities matching the relative frequency of potential road obstructions. The model runs quickly and is capable of producing road segment-level obstruction estimates within minutes to hours of a major earthquake. However, in near-real-time application, the accuracy of the obstruction estimates will be dependent on the quality of the ShakeMap shaking estimates, which often improves with time as more information becomes available after the earthquake. By providing a rapid first-order translation of landslide hazard into potential infrastructure impacts, this model helps provide emergency responders with tangible information on initial areas of concern.
Instream barriers can constrain dispersal of nonnative fishes, creating opportunities to test their impact on native communities above and below these barriers. Deposition of sediments in a river inflow to Lake Powell, USA resulted in creation of a large waterfall prohibiting upstream movement of fishes from the reservoir allowing us to evaluate the trophic niche of fishes above and below this barrier. We expected niche overlap among native and nonnative species would increase in local assemblages downstream of the barrier where nonnative fish diversity and abundance were higher. Fishes upstream of the barrier had more distinct isotopic niches and species exhibited a wider range in δ15N relative to downstream. In the reservoir, species were more constrained in δ15N and differed more in δ13C, representing a shorter, wider food web. Differences in energetic pathways and resource availability among habitats likely contributed to differences in isotopic niches. Endangered Razorback Sucker (Xyrauchen texanus) aggregate at some reservoir inflows in the Colorado River basin, and this is where we found the highest niche overlap among species. Whether isotopic niche overlap among adult native and nonnative species has negative consequences is unclear, because data on resource availability and use are lacking; however, these observations do indicate the potential for competition. Still, the impacts of diet overlap among trophic generalists, such as Razorback Sucker, are likely low, particularly in habitats with diverse and abundant food bases such as river-reservoir inflows.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41598-021-91730-1","usgsCitation":"Pennock, C., Ahrens, Z.T., McKinstry, M., Budy, P., and Gido, K., 2021, Trophic niches of native and nonnative fishes along a river-reservoir continuum: Scientific Reports, v. 11, p. 1-12, https://doi.org/10.1038/s41598-021-91730-1.","productDescription":"12140, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-123081","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":451948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-91730-1","text":"Publisher Index Page"},{"id":396658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell, San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.72021484375,\n 37.131855694734625\n ],\n [\n -109.77951049804688,\n 37.131855694734625\n ],\n [\n -109.77951049804688,\n 37.34395908944491\n ],\n [\n -110.72021484375,\n 37.34395908944491\n ],\n [\n -110.72021484375,\n 37.131855694734625\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pennock, Casey A.","contributorId":287044,"corporation":false,"usgs":false,"family":"Pennock","given":"Casey A.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":836859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahrens, Zachary T.","contributorId":287536,"corporation":false,"usgs":false,"family":"Ahrens","given":"Zachary","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":836860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinstry, Mark","contributorId":271257,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark","affiliations":[{"id":12646,"text":"BOR","active":true,"usgs":false}],"preferred":false,"id":836861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836858,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gido, Keith B.","contributorId":17465,"corporation":false,"usgs":true,"family":"Gido","given":"Keith B.","affiliations":[],"preferred":false,"id":836862,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223155,"text":"70223155 - 2021 - Biotic vs abiotic controls on temporal sensitivity of primary production to precipitation across North American drylands","interactions":[],"lastModifiedDate":"2021-09-14T16:50:28.22241","indexId":"70223155","displayToPublicDate":"2021-06-09T07:19:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Biotic vs abiotic controls on temporal sensitivity of primary production to precipitation across North American drylands","docAbstract":"Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the most data deficient. Datasets of surface water presence exist across multiple data collection groups in the United States but are not well aligned for easy integration. Given the value of these data, a unified approach for organizing information on surface water presence and absence collected by diverse surveys would facilitate more effective and broad application of these data and address the gap in streamflow data in headwaters. In this paper, we highlight the numerous existing datasets on surface water presence in headwater streams, including recently developed crowdsourcing approaches. We identify the challenges of integrating multiple surface water presence/absence datasets that include differences in the definitions and categories of streamflow status, data collection method, spatial and temporal resolution, and accuracy of geographic location. Finally, we provide a list of critical and useful components that could be used to integrate different streamflow permanence datasets.
","language":"English","publisher":"MDPI","doi":"10.3390/w13121627","usgsCitation":"Jaeger, K.L., Hafen, K., Dunham, J.B., Fritz, K.M., Kampf, S.K., Barnhart, T., Kaiser, K.E., Sando, R., Johnson, S.L., McShane, R., and Dunn, S.B., 2021, Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States: Water, v. 12, no. 13, 20 p., https://doi.org/10.3390/w13121627.","productDescription":"20 p.","ipdsId":"IP-126559","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":451965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13121627","text":"Publisher Index Page"},{"id":386412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n 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0000-0003-4463-0074","orcid":"https://orcid.org/0000-0003-4463-0074","contributorId":260169,"corporation":false,"usgs":true,"family":"Dunn","given":"Sarah","email":"","middleInitial":"Beth","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817407,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70221226,"text":"ofr20211021 - 2021 - Cape Romain partnership for coastal protection","interactions":[],"lastModifiedDate":"2021-06-09T15:41:26.952716","indexId":"ofr20211021","displayToPublicDate":"2021-06-08T16:20:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1021","displayTitle":"Cape Romain Partnership for Coastal Protection","title":"Cape Romain partnership for coastal protection","docAbstract":"This final report summarizes activities, outcomes, and lessons learned from a 3-year project titled “Climate Change Adaptation for Coastal National Wildlife Refuges” with the Cape Romain National Wildlife Refuge (NWR) and local partners in the surrounding South Carolina Lowcountry. The Lowcountry is classified as the 10-county area encompassing the coastal plain of South Carolina (this report specifically focuses on Berkeley, Charleston, and Georgetown Counties). The goals of this work, sponsored by the U.S. Geological Survey’s Southeast Climate Adaptation Science Center (SECASC), were to foster active engagement with stakeholders; to develop a comprehensive definition of adaptation problems faced by agencies, organizations, and individuals near the Cape Romain NWR that accounts for global change, local values, knowledge and perceptions; and to encourage social learning and building of effective networks and trust across South Carolina Lowcountry organizations and individuals. Although project scoping began at the scale of the Atlantic seaboard, by engaging with NWRs from Massachusetts to Florida, participating refuge personnel eventually selected the Cape Romain NWR to serve as a case study for testing our goals. The Cape Romain Partnership for Coastal Conservation was established to address global change impacts at a regional level and includes representation from Federal and State resource agencies, local conservation nongovernmental organizations, and organizations representing underserved community interests. Research topics, originating from discussions with Cape Romain Partnership for Coastal Conservation members, focused on quantifying key drivers of change including localized sea-level rise (SLR) predictions, estimates of coastal hurricane inundation as amplified by SLR, and urban growth trends and forecasts. These key drivers provided a foundation to engage stakeholders in planning exercises to begin a process of collective understanding and collaborative decision making. The goal of this process was to develop collective strategies of adaptation to enhance community and ecosystem resilience in the South Carolina Lowcountry.
South Carolina’s Lowcountry is experiencing rapid environmental and social transformation because of SLR rates approaching twice the global average, chronic tidal flooding and catastrophic storm surges, erosion and loss of habitats that provide essential services to wildlife and humans, and increasing social polarization fueled by aggressive low-density urban growth and other forms of land conversion. To support characterizations of plausible future scenarios, we used available or, in some cases, developed new models to project future conditions of key environmental and social-economic drivers. Because of the imprecision of mean global SLR projections, the SECASC commissioned a climatological study to account for local conditions and multiple representative concentration pathways to project a tailored distribution of future sea levels. These projections were matched to SLR scenarios provided by existing models to anticipate the range of future coastal habitat changes in the South Carolina Lowcountry. SLR scenarios were also incorporated into existing storm-surge models, which do not account for alternate baseline sea levels, to project the local effects of future hurricanes. To evaluate the extent and effects of population growth and urban expansion, we relied on an existing urban-growth model to map the spatial distribution of land-conversion probabilities, the total area of which is predicted to increase twofold to threefold over the next 60 years. In addition to this simplified model, an econometric model is in development to account for nonlinear feedback dynamics in land value, land use, and ecosystem service production. Although not yet completed, the goals of this model are to produce more-detailed projections of growth dynamics and to allow predictions of development patterns resulting from alternate land-use planning policies and incentives.
Collaborative planning for an uncertain future requires more than providing decision makers with information on future physical and ecological conditions; developing effective and consensual strategies must also integrate sociological values, multiple cultural perspectives, and an understanding of human behavior. To support broad stakeholder engagement in integrative approaches to adaptation planning, emphasis was placed on the importance of considering differences in how individuals perceive their environment and create meaning. Because cultural frameworks form the basis for perceptions and, ultimately, the behaviors of individuals and institutions, we describe a model of human behavior and how it can be used to understand the effect of cultural complexity and variation in perception on choices, behavioral change, and long-term maintenance of behaviors. We consider a model commonly used in the field of behavioral health that accommodates variation in human perception when describing stages of behavior and the dynamics of behavioral change. Tailoring communication and engagement activities to targeted stakeholders is likely to benefit from increased understanding of behavioral change processes.
The complex nature of this problem limited the usefulness of a traditional decision-analytic approach, we explored alternative methods for engagement, collaborative learning and decision making. Recognizing that project partners and Lowcountry stakeholders may be at different stages of preparedness and interest level for modifying behavior as a function of global change, we facilitated a scenario-planning exercise to familiarize partners with this well-established approach for communicating the opportunities and threats arising under alternative, plausible futures. We developed narratives for four alternative South Carolina Lowcountry scenarios to be used in later strategic planning that focus on quantitative trends for three primary drivers with high impact and high uncertainty: manifestations of climate change, social-political shifts at a global level, and forces of local value and power structures. This scenario-planning exercise underscored the complex relation between the temporospatial scale of the production of ecological goods and services and the institutional scale at which they are managed. We then guided the partners through an assessment of the relevant strengths and weaknesses of the Cape Romain Partnership for Coastal Protection, using the threats and opportunities characterized by each scenario to understand how the partnership might respond when attempting to meet conservation and societal objectives. The partnership identified key strengths including partnership experience, outreach and technical capacities, a substantial conservation land base, and high social cohesion in the South Carolina Lowcountry. Limited communication expertise, institutional inertia, and insufficient staffing and funding were recognized as important weaknesses across the partnership. By examining and scoring combinations of internal strengths and weaknesses and external threats and opportunities, the partnership developed sets of prioritized strategies to consider in the context of a given scenario. Although we had insufficient time to examine all scenarios in detail, the intent was to identify a portfolio of strategic actions to address threats and opportunities represented in multiple plausible futures. Top-ranking strategies encompassed a range of actions that focused on strengthening the conservation community and communicating the benefits of nature (that is, ecosystem services) to leveraging partnerships to expand land protection.
This report also details the methods and preliminary results of several models developed or applied in support of this project. Two parcel-selection algorithms were used to evaluate anticipated habitat changes and patterns of urban growth to guide decisions on optimal conservation reserve design to protect habitat communities. One approach used a widely available planning software (MARXAN) to maximize conservation benefits near the Cape Romain NWR, whereas the other approach was a novel application of economic theory to account for uncertainty in future conditions and for the risks of unanticipated habitat loss. This latter model applies modern portfolio theory to estimate the risk of investing in any portfolio of land parcels (that is, candidate “reserves”) under climate-change uncertainty by quantifying the variation and spatial correlation of conservation benefits derived from each portfolio. We expanded the range of actions beyond simply whether or not to invest in a set of land parcels, an approach commonly used in spatial conservation planning, to also include consideration of divestment from currently protected lands. Such refinements allow for better accounting of system dynamics and can evaluate the benefits of flexible conservation tools such as rolling easements. Model results were conditional on a decision maker’s risk tolerance but highlighted general strategies of land conservation to increase future habitat representation beyond what is expected under the current protected land base. We built models that may help inform coastal planning by estimating salinity dynamics and the performance of oyster reef restoration efforts to predict the combined effects of global change and management of freshwater flows on coastal habitats and the processes that contribute to their resilience. These models can support restoration decisions by evaluating the expected benefits of site locations for shoreline protection and fisheries production. Lastly, we developed a spatially explicit economic model that predicts feedback dynamics among land value, land-use change, and effects on ecosystem service provision to explore zoning policies and incentives on urban growth and ecosystem services.
We summarize these efforts with insights and considerations for the Cape Romain Partnership for Coastal Protection to continue to engage stakeholders in effective adaptation planning. First, notions of place attachment (referred to as sense of place), and the role of culture in social discourse are increasingly being used to understand the complex interactions between society and the environment and how societies respond and adapt to climate change. Sense of place was a unifying theme whenever the future of the South Carolina Lowcountry was discussed. The contribution of the South Carolina Lowcountry’s environmental wealth, rich cultural heritage, and quality of life to sense of place has important implications for how adaptation planning might best be pursued. More community-based governance of the commons (in other words, natural and cultural resources held in common), in which broad stakeholder participation and power sharing are key elements, is considered important. This devolution of governance is characterized by polycentric institutions and self-organizing social networks that promote a local culture of knowledge sharing, problem solving, and learning. These so-called bridging organizations (or individuals) often provide the leadership necessary to bring together potentially disparate Government agencies and institutions, private organizations, and individuals in a collective process of problem solving. Our observations also suggest that the conservation community in the South Carolina Lowcountry views its activities as integral to the broader governance of social-ecological systems, in which responses to the forces of global change are mediated through culture, economics, and politics. Rather than directly competing with other interests, the South Carolina Lowcountry conservation community seems to embrace an interpretation of conservation in which the fundamental objective is the quality of human life rather than environmental protection.
Fundamental to the types of governance reforms described above is the notion of coproduction, in which experts and users collaborate to develop a shared body of knowledge. In this approach, scientists work with stakeholders to help frame questions, design research, and collect and analyze data. Such sustained collaborations are increasingly believed to be an effective way to produce useable (or actionable) science. The emphasis on social learning, leveraging strong social networks, coordinating and deliberating among diverse stakeholders, and applying principles of adaptive management is an essential contribution to adaptive capacity. The diverse and robust set of scientific approaches, methods to help stakeholders collaborate in effective and goal-driven planning processes, and decision tools resulting from this project hopefully will assist Cape Romain NWR and its partners prepare for climatic, ecological, and social changes over the coming decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211021","usgsCitation":"Eaton, M.J., Johnson, F.A., Mikels-Carrasco, J., Case, D.J., Martin, J., Stith, B., Yurek, S., Udell, B., Villegas, L., Taylor, L., Haider, Z., Charkhgard, H., and Kwon, C., 2021, Cape Romain Partnership for Coastal Protection: U.S. Geological Survey Open-File Report 2021–1021, 158 p., https://doi.org/10.3133/ofr20211021.","productDescription":"xii, 158 p.","numberOfPages":"174","onlineOnly":"Y","ipdsId":"IP-100705","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":386276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1021/coverthb.jpg"},{"id":386277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1021/ofr20211021.pdf","text":"Report","size":"33.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1021"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.8431396484375,\n 32.78842902722552\n ],\n [\n -79.815673828125,\n 32.765336175015776\n ],\n [\n -79.63577270507811,\n 32.85421076375021\n ],\n [\n -79.55886840820312,\n 32.92455477363828\n ],\n [\n -79.47784423828125,\n 33.00981511270531\n ],\n [\n -79.3487548828125,\n 33.0063602132054\n ],\n [\n -79.27047729492188,\n 33.12490094278685\n ],\n [\n -79.34600830078125,\n 33.16169660598766\n ],\n [\n -79.50393676757812,\n 33.060471419708115\n ],\n [\n -79.60968017578125,\n 32.99599470276581\n ],\n [\n -79.6673583984375,\n 32.93838636388491\n ],\n [\n -79.68658447265625,\n 32.91533251206152\n ],\n [\n -79.8431396484375,\n 32.78842902722552\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Southeast Climate Adaptation Science Center
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The Fluvial Egg Drift Simulator (FluEgg) was developed to simulate the transport and dispersion of invasive carp eggs and larvae in a river. FluEgg currently (2020) supports modeling of bighead carp (Hypophthalmichthys nobilis), silver carp (H. molitrix), and grass carp (Ctenopharyngodon idella), with the planned addition of black carp (Mylopharyngodon piceus) once developmental data are available. FluEgg integrates the biological development of invasive carp eggs and larvae with a particle transport model that simulates the advection and dispersion of the eggs and larvae based on user-supplied one-dimensional hydraulic conditions. FluEgg can be used to evaluate the hydrodynamic suitability of a river for invasive carp spawning, to inform sampling and monitoring efforts, and to identify the most likely spawning areas of captured eggs or larvae.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211052","usgsCitation":"Domanski, M.M., LeRoy, J.Z., Berutti, M., and Jackson, P.R., 2021, Fluvial Egg Drift Simulator (FluEgg) user’s manual: U.S. Geological Survey Open-File Report 2021–1052, 30 p., https://doi.org/10.3133/ofr20211052.","productDescription":"Report: vii, 30 p.; Software Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-120778","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1052/coverthb.jpg"},{"id":386270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1052/ofr20211052.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1052"},{"id":386273,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P93UCQR2","text":"USGS software release","linkHelpText":"— FluEgg"}],"contact":"Director, Central Midwest Water Science Center
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The Central Valley of California is one of the most important areas for wintering waterfowl in the world and the focus of extensive conservation efforts to mitigate for historical losses and counter continuing stressors to habitats. To guide conservation, we analyzed trends in the abundance and distribution (spatiotemporal abundance patterns) of waterfowl and their habitats in the Central Valley and its major subregions (Sacramento Valley, Suisun Marsh, Delta, San Joaquin Valley), from 1973 through 2000. We used existing databases, satellite imagery, and aerial photography to measure habitat area, and aerial surveys and radio telemetry to track the abundance and distribution of wintering waterfowl. Wetlands increased throughout the Central Valley, but agricultural fields flooded after harvest increased greatly to the north in the Sacramento Valley and decreased to the south in the San Joaquin Valley, resulting in an overall increase in the relative availability of winter habitat in the former region. Reflecting the continental decline of the most abundant wintering species (Northern Pintail, Anas acuta), the overall abundance of wintering waterfowl in the Central Valley declined during our study. By contrast, numbers of the American Wigeon (A. americana), Mallard (A. platyrhynchos), and Northern Shoveler (A. clypeata) were stable, and numbers of the Green-winged Teal (A. crecca), Gadwall (A. strepera), diving ducks, and geese increased from 1973–1982 to 1998–2000. The areas of greatest abundance of wintering waterfowl within the Central Valley shifted northward as many species responded to changes in the distribution of habitats. Wintering waterfowl migrated earlier in fall and winter from the San Joaquin Valley, Suisun Marsh, and Delta to the Sacramento Valley, and fewer waterfowl emigrated from the Sacramento Valley to other parts of the Central Valley. Because changes in waterfowl distribution were primarily a response to the increase of a beneficial agricultural practice (i.e., the flooding of rice after harvest) in the Sacramento Valley, changing agro-economics, reduction of water supplies, or other factors that reduce this practice could change the abundance and distribution of wintering waterfowl in the Central Valley rapidly. Thus to maintain abundant suitable habitat and restore the historical distribution of wintering waterfowl, our results suggest a continuing need for the conservation of wetlands and other waterfowl habitats with secure water supplies throughout the Central Valley. Despite our findings, achieving goals for winter waterfowl populations in the Central Valley likely will depend on a combination of factors including some acting in breeding ranges farther north or elsewhere outside of the valley.
","language":"English","publisher":"Western Field Ornithologists","doi":"10.21199/SWB3.2","usgsCitation":"Fleskes, J.P., Casazza, M.L., Overton, C.T., Matchett, E., and Yee, J.L., 2021, Changes in the abundance and distribution of waterfowl wintering in the Central Valley of California, 1973–2000: Studies of Western Birds, v. 3, p. 50-74, https://doi.org/10.21199/SWB3.2.","productDescription":"25 p.","startPage":"50","endPage":"74","ipdsId":"IP-073693","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21199/swb3.2","text":"Publisher Index Page"},{"id":386342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley of California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 40.44694705960048\n ],\n [\n -122.51953124999999,\n 38.95940879245423\n ],\n [\n -121.70654296874999,\n 37.54457732085582\n ],\n [\n -120.08056640625,\n 35.92464453144099\n ],\n [\n -119.20166015625,\n 35.15584570226544\n ],\n [\n -118.43261718749999,\n 35.38904996691167\n ],\n [\n -119.0478515625,\n 36.73888412439431\n ],\n [\n -120.89355468749999,\n 38.238180119798635\n ],\n [\n -122.3876953125,\n 40.29628651711716\n ],\n [\n -122.958984375,\n 40.44694705960048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2017-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Fleskes, Joseph P. 0000-0001-5388-6675 joe_fleskes@usgs.gov","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":177154,"corporation":false,"usgs":true,"family":"Fleskes","given":"Joseph","email":"joe_fleskes@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":817258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817260,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817261,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263832,"text":"70263832 - 2021 - Detailed traveltime tomography and seismic catalog around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data","interactions":[],"lastModifiedDate":"2025-02-25T15:45:23.244346","indexId":"70263832","displayToPublicDate":"2021-06-08T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Detailed traveltime tomography and seismic catalog around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data","docAbstract":"We derive a detailed earthquake catalogue and Vp, Vs and Vp/Vs models for the region around the 2019 Mw 6.4 and Mw7.1 Ridgecrest, California, earthquake sequence using data recorded by rapid-response, densely deployed sensors following the Ridgecrest main shock and the regional network. The new catalogue spans a 4-month period, starting on 1 June 2019, and it includes nearly 95 000 events detected and located with iterative updates to our velocity models. The final Vp and Vs models correlate well with surface geology in the top 4 km of the crust and spatial seismicity patterns at depth. Joint interpretation of the derived catalogue, velocity models, and surface geology suggests that (i) a compliant low-velocity zone near the Garlock Fault arrested the Mw 7.1 rupture at the southeast end; (ii) a stiff high-velocity zone beneath the Coso Mountains acted as a strong barrier that arrested the rupture at the northwest end and (iii) isolated seismicity on the Garlock Fault accommodated transtensional-stepover strain triggered by the main events. The derived catalogue and velocity models can be useful for multiple future studies, including further analysis of seismicity patterns, derivations of accurate source properties (e.g. focal mechanisms) and simulations of earthquake processes and radiated seismic wavefields.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab224","usgsCitation":"White, M., Fang, H., Catchings, R.D., Goldman, M., Steidl, J.H., and Ben-Zion, Y., 2021, Detailed traveltime tomography and seismic catalog around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data: Geophysical Journal International, v. 227, no. 1, p. 204-227, https://doi.org/10.1093/gji/ggab224.","productDescription":"24 p.","startPage":"204","endPage":"227","ipdsId":"IP-124945","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.33486333179661,\n 36.08725945725399\n ],\n [\n -118.33486333179661,\n 35.3380510630776\n ],\n [\n -117.28305638115125,\n 35.3380510630776\n ],\n [\n -117.28305638115125,\n 36.08725945725399\n ],\n [\n -118.33486333179661,\n 36.08725945725399\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"227","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Malcolm 0000-0001-7543-3896","orcid":"https://orcid.org/0000-0001-7543-3896","contributorId":351476,"corporation":false,"usgs":false,"family":"White","given":"Malcolm","affiliations":[{"id":47795,"text":"USC","active":true,"usgs":false}],"preferred":false,"id":928573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fang, Hongjian","contributorId":351481,"corporation":false,"usgs":false,"family":"Fang","given":"Hongjian","affiliations":[{"id":47799,"text":"MIT","active":true,"usgs":false}],"preferred":false,"id":928584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ben-Zion, Yehuda 0000-0002-9602-2014","orcid":"https://orcid.org/0000-0002-9602-2014","contributorId":350966,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":928578,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221212,"text":"ofr20211030I - 2021 - System characterization report on the WorldView-3 Imager","interactions":[{"subject":{"id":70221212,"text":"ofr20211030I - 2021 - System characterization report on the WorldView-3 Imager","indexId":"ofr20211030I","publicationYear":"2021","noYear":false,"chapter":"I","displayTitle":"System Characterization Report on the WorldView-3 Imager","title":"System characterization report on the WorldView-3 Imager"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2023-01-27T14:35:39.264069","indexId":"ofr20211030I","displayToPublicDate":"2021-06-07T09:23:24","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":"2021-1030","chapter":"I","displayTitle":"System Characterization Report on the WorldView-3 Imager","title":"System characterization report on the WorldView-3 Imager","docAbstract":"This report addresses system characterization of the Maxar WorldView-3 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2020. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
WorldView-3 is a high-resolution multispectral satellite launched in 2014 by Maxar Technologies on an Atlas V launch vehicle from Vandenberg Air Force Base in California for Earth resources monitoring. WorldView-3 provides substantial technical improvements to previous WorldView satellites, including spectral bands, ground sample distance, and swath. The WorldView-3 satellite was designed and built by Lockheed Martin for Maxar Technologies using the BCP–5000 bus with the WorldView-3 Imager and the Clouds, Aerosols, Vapors, Ice, and Snow sensor. The high-resolution WorldView-3 Imager is the main instrument, and the Clouds, Aerosols, Vapors, Ice, and Snow sensor provides additional data on obscurants and other atmospheric effects used in data production. More information on Maxar WorldView satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.maxar.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that WorldView-3 has a range of interior geometric performance of −0.09 (−0.07 pixel) to 0.24 meter (0.19 pixel) in band-to-band registration; an exterior geometric performance in the range of a −21.10- (−2.11 pixels) to 28.23-meter (2.82 pixels) offset in comparison to Sentinel-2; a radiometric performance in the range of −0.121 to 1.420 (offset and slope); and a spatial performance in the range of 1.2 to 1.7 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.093 to 0.185.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030I","usgsCitation":"Cantrell, S.J., Christopherson, J.B., Anderson, C., Stensaas, G.L., Ramaseri Chandra, S.N., Kim, M., and Park, S., 2021, System characterization report on the WorldView-3 Imager (ver. 1.1, October 2021), chap. I of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 29 p., https://doi.org/10.3133/ofr20211030I.","productDescription":"Report: v, 29 p.; Version History","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126804","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":391140,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/i/versionHist.txt","text":"Version History","size":"878 B","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2021–1030I Version History"},{"id":391139,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/i/ofr20211030i_ver1.1.pdf","text":"Report","size":"20.1 MB","description":"OFR 2021–1030I"},{"id":391138,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/i/images"},{"id":391137,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/i/ofr20211030i.xml"},{"id":386257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/i/coverthb_2.jpg"}],"edition":"Version 1.0: June 2021; Version 1.1: October 2021","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of Gaofen-1 and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2020. These reports present the detail methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at http://www.cnsageo.com/#/detailIndex?secondIndex=2&id=3&code=8.
The Earth Resources Observation and Science Cal/Val Center of Excellence System Characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030B","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on the Gaofen-1, chap. B of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 11 p., https://doi.org/10.3133/ofr20211030B.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-126808","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/b/coverthb.jpg"},{"id":386256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/b/ofr20211030b.pdf","text":"Report","size":"3.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030B"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions.
In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030A","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., and Anderson, C., 2021, System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS), chap. A of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 9 p., https://doi.org/10.3133/ofr20211030A.","productDescription":"iv, 9 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-126586","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/a/coverthb.jpg"},{"id":386253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/a/ofr20211030a.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030A"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Harvest is a common management tool for fish and game species and can also be used for overabundant populations when stakeholders want to reduce populations reduced and still provide recreational opportunities. The authors propose a framework to determine if harvest can be used to control populations when overabundance is an issue, stakeholders support harvest, information is available to set harvest goals and evaluate impacts of harvest, and assessments are conducted to evaluate unintended consequences of harvest. The chapter provides two case examples of mid-continent light geese and blue catfish in the Chesapeake Bay watershed, for which overabundance was a problem and stakeholders had interest in harvest. Substantial data existed to set goals for light geese whereas blue catfish data were limited. For both light geese and blue catfish, desired outcomes have not yet been achieved, but hunting and fishing opportunities generated societal benefits despite existing barriers to increasing harvest. Harvest to control overabundant populations can be a useful tool, but consideration of stakeholder support, the data require to establish and monitor goals, and unintended consequences should be considered for an effective harvest plan.
Harvest is a common management tool used for centuries to limit populations of game species (Caughley 1977, Redmond 1986). Managing populations using harvest regulations allow certain sizes, numbers, sex, and species to be harvested, and often include open or closed seasons. Regulated hunting opportunity and harvest are cornerstones of the North American Model of Wildlife Conservation, which developed gradually following unregulated harvest of wildlife populations that often were at risk of overharvest or extinction (Geist et al. 2001). Since then, many populations have recovered and expanded to the point where harvest regulations are now often used to limit or even reduce populations of some species. In general, harvest regulations have been well established as an effective way to control animal populations in many aquatic and terrestrial systems and are broadly accepted among the hunting and fishing public. For example, harvest regulations have been established or adapted to reduce or control populations of feral hogs (Sus scrofa; Hanson et al. 2009), white-tailed deer (Odocoileus virginianus; Simard et al. 2013) and cougar (Puma concolor; Cooley et al. 2009), overabundant small black bass (Micropertus spp.; Isermann and Paukert 2010), northern pike (Esox lucius; Pierce 2010) or non-native species (Arlinghaus et al. 2016b).
Harvest has also been employed as a tool for controlling populations of invasive species. However, in many cases invasive species are so overabundant that a substantial commitment to harvest is necessary, which may exceed recreational harvest capacity and require commercial harvest or an active lethal control program by management agencies. Often removal of invasive species is challenging because the ultimate goal may be to eliminate the entire population, which may require impractical efforts. For example, controlling Asian carp in the Illinois River may require harvest rates of at least 70% (Tsehaye et al. 2013), whereas in the Great Smoky Mountains, an annual harvest rate of 40% would be necessary to decrease feral hog populations (Salinas et al. 2015). Many invasive species are known to negatively impact native species and ecosystems; thus, eradication is an ideal outcome. However, there may be opportunity to use harvest to control populations of native (or non-native) species that have some value yet are still overabundant. In this chapter, we explore the process of using harvest to control overabundant populations that have some recreational value, provide two examples to control overabundant populations using harvest, and describe the challenges and effectiveness associated with these efforts and some of the unintended effects of using harvest to control populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Harvest of fish and wildlife: New paradigms for sustainable management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","usgsCitation":"Paukert, C.P., Webb, E.B., Fowler, D.N., and Hilling, C.D., 2021, Harvest as a tool to manage populations of undesirable or overabundant fish and wildlife, chap. 18 of Harvest of fish and wildlife: New paradigms for sustainable management, p. 249-261.","productDescription":"13 p.","startPage":"249","endPage":"261","ipdsId":"IP-119950","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fowler, Drew N.","contributorId":205356,"corporation":false,"usgs":false,"family":"Fowler","given":"Drew","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":836091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hilling, Corbin D. 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":257754,"corporation":false,"usgs":false,"family":"Hilling","given":"Corbin","email":"","middleInitial":"D.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":836092,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}