Local governmental agencies are increasingly undertaking potentially costly “status-and-trends” monitoring to evaluate the effectiveness of stormwater control measures and land-use planning strategies, or to satisfy regulatory requirements. Little guidance is presently available for such efforts, and so we have explored the application, interpretation, and temporal limitations of well-established hydrologic metrics of runoff changes from urbanization, making use of an unusually long-duration, high-quality data set from the Pacific Northwest (USA) with direct applicability to urban and urbanizing watersheds. Three metrics previously identified for their utility in identifying hydrologic conditions with biological importance that respond to watershed urbanization—TQmean (the fraction of time that flows exceed the mean annual discharge), the Richards-Baker Index (characterizing flashiness relative to the mean discharge), and the annual tally of wet-season day-to-day flow reversals (the total number of days that reverse the prior days’ increasing or decreasing trend)—are all successful in stratifying watersheds across a range of urbanization, as measured by total contributing area of urban development. All metrics respond with statistical significance to multi-decadal trends in urbanization, but none detect trends in watershed-scale urbanization over the course of a single decade. This suggests a minimum period over which dependable trends in hydrologic alteration (or improvement) can be detected with confidence. The metrics also prove less well suited to urbanizing watersheds in a semi-arid climate, with only flow reversals showing a response consistent with prior findings from more humid regions. We also explore the use of stage as a surrogate for discharge in calculating these metrics, recognizing potentially significant agency cost savings in data collection with minimal loss of information. This approach is feasible but cannot be implemented under current data-reporting practices, requiring measurement of water-depth values and preservation of the full precision of the original recorded data. With these caveats, however, hydrologic metrics based on stage should prove as or more useful, at least in the context of status-and-trends monitoring, as those based on subsequent calculations of discharge.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11369","usgsCitation":"Booth, D.B., and Konrad, C.P., 2017, Hydrologic metrics for status-and-trends monitoring in urban and urbanizing watersheds: Hydrological Processes, v. 31, no. 25, p. 4507-4519, https://doi.org/10.1002/hyp.11369.","productDescription":"13 p.","startPage":"4507","endPage":"4519","ipdsId":"IP-090190","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":348629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"25","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-23","publicationStatus":"PW","scienceBaseUri":"5a096bb0e4b09af898c9413d","contributors":{"authors":[{"text":"Booth, Derek B.","contributorId":100873,"corporation":false,"usgs":false,"family":"Booth","given":"Derek","email":"","middleInitial":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":717492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717491,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193010,"text":"70193010 - 2017 - Central Arctic Ocean paleoceanography from ∼50 ka to present, on the basis of ostracode faunal assemblages from the SWERUS 2014 expedition ","interactions":[],"lastModifiedDate":"2017-11-12T12:32:21","indexId":"70193010","displayToPublicDate":"2017-11-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Central Arctic Ocean paleoceanography from ∼50 ka to present, on the basis of ostracode faunal assemblages from the SWERUS 2014 expedition ","docAbstract":"Late Quaternary paleoceanographic changes at the Lomonosov Ridge, central Arctic Ocean, were reconstructed from a multicore and gravity core recovered during the 2014 SWERUS-C3 Expedition. Ostracode assemblages dated by accelerator mass spectrometry (AMS) indicate changing sea-ice conditions and warm Atlantic Water (AW)inflow to the Arctic Ocean from ∼50 ka to present. Key taxa used as environmental indicators include Acetabulastoma arcticum (perennial sea ice), Polycopes pp. (variable sea-ice margins, high surface productivity), Krithe hunti (Arctic Ocean deep water), and Rabilimis mirabilis (water mass change/AW inflow). Results indicate periodic seasonally sea-ice-free conditions during Marine Isotope Stage (MIS) 3 (∼57-29 ka), rapid deglacial changes in water mass conditions (15-11 ka), seasonally sea-ice-free conditions during the early Holocene (∼10-7 ka) and perennial sea ice during the late Holocene. Comparisons with faunal records from other cores from the Mendeleev and Lomonosov ridges suggest generally similar patterns, although sea-ice cover during the Last Glacial Maximum may have been less extensive at the new Lomonosov Ridge core site (∼85.15° N, 152° E) than farther north and towards Greenland. The new data provide evidence for abrupt, large-scale shifts in ostracode species depth and geographical distributions during rapid climatic transitions.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/cp-2017-22","usgsCitation":"Gemery, L., Cronin, T.M., Poirier, R.K., Pearce, C., Barrientos, N., O’Regan, M., Johansson, C., Koshurnikov, A., and Jakobsson, M., 2017, Central Arctic Ocean paleoceanography from ∼50 ka to present, on the basis of ostracode faunal assemblages from the SWERUS 2014 expedition : Climate of the Past, v. 13, p. 1473-1489, https://doi.org/10.5194/cp-2017-22.","productDescription":"17 p.","startPage":"1473","endPage":"1489","ipdsId":"IP-084428","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":469326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-2017-22","text":"Publisher Index Page"},{"id":348623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","volume":"13","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a096bafe4b09af898c94137","contributors":{"authors":[{"text":"Gemery, Laura 0000-0003-1966-8732 lgemery@usgs.gov","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":5402,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","email":"lgemery@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":717636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":717637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poirier, Robert K. rpoirier@usgs.gov","contributorId":5790,"corporation":false,"usgs":true,"family":"Poirier","given":"Robert","email":"rpoirier@usgs.gov","middleInitial":"K.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":717638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":717639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barrientos, Natalia","contributorId":197127,"corporation":false,"usgs":false,"family":"Barrientos","given":"Natalia","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false},{"id":35520,"text":"1Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, 10691, Sweden","active":true,"usgs":false}],"preferred":false,"id":717640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Regan, Matt","contributorId":197135,"corporation":false,"usgs":false,"family":"O’Regan","given":"Matt","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":717641,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johansson, Carina","contributorId":166871,"corporation":false,"usgs":false,"family":"Johansson","given":"Carina","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":717642,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Koshurnikov, Andrey","contributorId":166860,"corporation":false,"usgs":false,"family":"Koshurnikov","given":"Andrey","email":"","affiliations":[{"id":24563,"text":"Tomsk Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":717644,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jakobsson, Martin","contributorId":166854,"corporation":false,"usgs":false,"family":"Jakobsson","given":"Martin","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":717643,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192996,"text":"70192996 - 2017 - Sampling uncharted waters: Examining rearing habitat of larval Longfin Smelt (Spirinchus thaleichthys) in the upper San Francisco Estuary","interactions":[],"lastModifiedDate":"2017-11-12T12:44:02","indexId":"70192996","displayToPublicDate":"2017-11-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Sampling uncharted waters: Examining rearing habitat of larval Longfin Smelt (Spirinchus thaleichthys) in the upper San Francisco Estuary","title":"Sampling uncharted waters: Examining rearing habitat of larval Longfin Smelt (Spirinchus thaleichthys) in the upper San Francisco Estuary","docAbstract":"The southern-most reproducing Longfin Smelt population occurs in the San Francisco Estuary, California, USA. Long-term monitoring of estuarine habitat for this species has generally only considered deep channels, with little known of the role shallow waters play in supporting their early life stage. To address the need for focused research on shallow-water habitat, a targeted study of Longfin Smelt larvae in littoral habitat was conducted to identify potential rearing habitats during 2013 and 2014. Our study objectives were to (1) determine if larval densities vary between littoral habitats (tidal slough vs. open-water shoal), (2) determine how larval densities in littoral habitats vary with physicochemical and biological attributes, (3) determine if larval densities vary between littoral habitats and long-term monitoring channel collections, and (4) determine what factors predict larval rearing distributions from the long-term monitoring channel collections. Larval densities did not vary between littoral habitats but they did vary between years. Water temperature, salinity, and chlorophyll a were found important in predicting larval densities in littoral habitats. Larval densities do not vary between littoral and channel surveys; however, the analysis based on channel data suggests that Longfin Smelt are hatching and rearing in a much broader region and under higher salinities (∼2–12 psu) than previously recognized. Results of this study indicate that conservation efforts should consider how freshwater flow, habitat, climate, and food webs interact as mechanisms that influence Longfin Smelt recruitment in estuarine environments.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-017-0255-9","usgsCitation":"Grimaldo, L., Feyrer, F.V., Burns, J., and Maniscalco, D., 2017, Sampling uncharted waters: Examining rearing habitat of larval Longfin Smelt (Spirinchus thaleichthys) in the upper San Francisco Estuary: Estuaries and Coasts, v. 40, no. 6, p. 1771-1784, https://doi.org/10.1007/s12237-017-0255-9.","productDescription":"14 p.","startPage":"1771","endPage":"1784","ipdsId":"IP-085099","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":348624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","volume":"40","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-17","publicationStatus":"PW","scienceBaseUri":"5a096bb0e4b09af898c9413b","contributors":{"authors":[{"text":"Grimaldo, Lenny","contributorId":10728,"corporation":false,"usgs":false,"family":"Grimaldo","given":"Lenny","email":"","affiliations":[{"id":35724,"text":"ICF, San Francisco, USA","active":true,"usgs":false}],"preferred":false,"id":717561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Jillian","contributorId":198892,"corporation":false,"usgs":false,"family":"Burns","given":"Jillian","email":"","affiliations":[{"id":35724,"text":"ICF, San Francisco, USA","active":true,"usgs":false}],"preferred":false,"id":717562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maniscalco, Donna","contributorId":198893,"corporation":false,"usgs":false,"family":"Maniscalco","given":"Donna","email":"","affiliations":[{"id":35725,"text":"ICF, San Jose, USA","active":true,"usgs":false}],"preferred":false,"id":717563,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193488,"text":"70193488 - 2017 - Modeling of high‐frequency seismic‐wave scattering and propagation using radiative transfer theory ","interactions":[],"lastModifiedDate":"2017-12-19T16:36:35","indexId":"70193488","displayToPublicDate":"2017-11-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Modeling of high‐frequency seismic‐wave scattering and propagation using radiative transfer theory ","docAbstract":"This is a study of the nonisotropic scattering process based on radiative transfer theory and its application to the observation of the M 4.3 aftershock recording of the 2008 Wells earthquake sequence in Nevada. Given a wide range of recording distances from 29 to 320 km, the data provide a unique opportunity to discriminate scattering models based on their distance‐dependent behaviors. First, we develop a stable numerical procedure to simulate nonisotropic scattering waves based on the 3D nonisotropic scattering theory proposed by Sato (1995). By applying the simulation method to the inversion of M 4.3 Wells aftershock recordings, we find that a nonisotropic scattering model, dominated by forward scattering, provides the best fit to the observed high‐frequency direct S waves and S‐wave coda velocity envelopes. The scattering process is governed by a Gaussian autocorrelation function, suggesting a Gaussian random heterogeneous structure for the Nevada crust. The model successfully explains the common decay of seismic coda independent of source–station locations as a result of energy leaking from multiple strong forward scattering, instead of backscattering governed by the diffusion solution at large lapse times. The model also explains the pulse‐broadening effect in the high‐frequency direct and early arriving S waves, as other studies have found, and could be very important to applications of high‐frequency wave simulation in which scattering has a strong effect. We also find that regardless of its physical implications, the isotropic scattering model provides the same effective scattering coefficient and intrinsic attenuation estimates as the forward scattering model, suggesting that the isotropic scattering model is still a viable tool for the study of seismic scattering and intrinsic attenuation coefficients in the Earth.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120160241","usgsCitation":"Zeng, Y., 2017, Modeling of high‐frequency seismic‐wave scattering and propagation using radiative transfer theory : Bulletin of the Seismological Society of America, v. 107, no. 6, p. 2948-2962, https://doi.org/10.1785/0120160241.","productDescription":"15 p.","startPage":"2948","endPage":"2962","ipdsId":"IP-075005","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":348607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-31","publicationStatus":"PW","scienceBaseUri":"5a07e83de4b09af898c8cb14","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":719254,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70193479,"text":"70193479 - 2017 - Future scenarios of land change based on empirical data and demographic trends","interactions":[],"lastModifiedDate":"2017-12-19T16:37:25","indexId":"70193479","displayToPublicDate":"2017-11-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Future scenarios of land change based on empirical data and demographic trends","docAbstract":"Changes in land use and land cover (LULC) have important and fundamental interactions with the global climate system. Top-down global scale projections of land use change have been an important component of climate change research; however, their utility at local to regional scales is often limited. The goal of this study was to develop an approach for projecting changes in LULC based on land use histories and demographic trends. We developed a set of stochastic, empirical-based projections of LULC change for the state of California, for the period 2001–2100. Land use histories and demographic trends were used to project a “business-as-usual” (BAU) scenario and three population growth scenarios. For the BAU scenario, we projected developed lands would more than double by 2100. When combined with cultivated areas, we projected a 28% increase in anthropogenic land use by 2100. As a result, natural lands were projected to decline at a rate of 139 km2 yr−1; grasslands experienced the largest net decline, followed by shrublands and forests. The amount of cultivated land was projected to decline by approximately 10%; however, the relatively modest change masked large shifts between annual and perennial crop types. Under the three population scenarios, developed lands were projected to increase 40–90% by 2100. Our results suggest that when compared to the BAU projection, scenarios based on demographic trends may underestimate future changes in LULC. Furthermore, regardless of scenario, the spatial pattern of LULC change was likely to have the greatest negative impacts on rangeland ecosystems.
","language":"English","publisher":"AGU","doi":"10.1002/2017EF000560","usgsCitation":"Sleeter, B.M., Wilson, T., Sharygin, E., and Sherba, J.T., 2017, Future scenarios of land change based on empirical data and demographic trends: Earth's Future, v. 5, no. 11, p. 1068-1083, https://doi.org/10.1002/2017EF000560.","productDescription":"16 p.","startPage":"1068","endPage":"1083","ipdsId":"IP-085589","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017ef000560","text":"Publisher Index Page"},{"id":348587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"5","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8c2e4b09af898c860bd","contributors":{"authors":[{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":719215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":719216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharygin, Ethan","contributorId":199467,"corporation":false,"usgs":false,"family":"Sharygin","given":"Ethan","email":"","affiliations":[],"preferred":false,"id":719217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherba, Jason T. 0000-0001-9151-686X jsherba@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-686X","contributorId":196154,"corporation":false,"usgs":true,"family":"Sherba","given":"Jason","email":"jsherba@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":719218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192596,"text":"70192596 - 2017 - A Bayesian method for assessing multiscalespecies-habitat relationships","interactions":[],"lastModifiedDate":"2017-12-11T13:12:54","indexId":"70192596","displayToPublicDate":"2017-11-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A Bayesian method for assessing multiscalespecies-habitat relationships","docAbstract":"Context
Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.
Objectives
Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.
Methods
We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.
Results
Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.
Conclusions
Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.
Automated cameras have become increasingly common for monitoring wildlife populations and estimating abundance. Most analytical methods, however, fail to account for incomplete and variable detection probabilities, which biases abundance estimates. Methods which do account for detection have not been thoroughly tested, and those that have been tested were compared to other methods of abundance estimation. The goal of this study was to evaluate the accuracy and effectiveness of the N-mixture method, which explicitly incorporates detection probability, to monitor white-tailed deer (Odocoileus virginianus) by using camera surveys and a known, marked population to collect data and estimate abundance. Motion-triggered camera surveys were conducted at Auburn University’s deer research facility in 2010. Abundance estimates were generated using N-mixture models and compared to the known number of marked deer in the population. We compared abundance estimates generated from a decreasing number of survey days used in analysis and by time periods (DAY, NIGHT, SUNRISE, SUNSET, CREPUSCULAR, ALL TIMES). Accurate abundance estimates were generated using 24 h of data and nighttime only data. Accuracy of abundance estimates increased with increasing number of survey days until day 5, and there was no improvement with additional data. This suggests that, for our system, 5-day camera surveys conducted at night were adequate for abundance estimation and population monitoring. Further, our study demonstrates that camera surveys and N-mixture models may be a highly effective method for estimation and monitoring of ungulate populations.
","language":"English","publisher":"Springer","doi":"10.1007/s13364-017-0319-z","usgsCitation":"Keever, A., McGowan, C.P., Ditchkoff, S.S., Acker, S., Grand, J.B., and Newbolt, C.H., 2017, Efficacy of time-lapse photography and repeated counts abundance estimation for white-tailed deer populations: Mammal Research, v. 62, no. 4, p. 413-422, https://doi.org/10.1007/s13364-017-0319-z.","productDescription":"10 p.","startPage":"413","endPage":"422","ipdsId":"IP-052763","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-20","publicationStatus":"PW","scienceBaseUri":"5a05771ce4b09af898c7085f","contributors":{"authors":[{"text":"Keever, Allison","contributorId":187743,"corporation":false,"usgs":false,"family":"Keever","given":"Allison","email":"","affiliations":[],"preferred":false,"id":721428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":720611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ditchkoff, Stephen S.","contributorId":193053,"corporation":false,"usgs":false,"family":"Ditchkoff","given":"Stephen","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":721429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Acker, S.A.","contributorId":104709,"corporation":false,"usgs":true,"family":"Acker","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":721430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newbolt, Chad H.","contributorId":200209,"corporation":false,"usgs":false,"family":"Newbolt","given":"Chad","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":721431,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193824,"text":"70193824 - 2017 - Conservation status of an imperiled crayfish, Faxonius marchandi Hobbs, 1948 (Decapoda: Cambaridae)","interactions":[],"lastModifiedDate":"2017-11-09T11:00:07","indexId":"70193824","displayToPublicDate":"2017-11-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5546,"text":"Journal of Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Conservation status of an imperiled crayfish, Faxonius marchandi Hobbs, 1948 (Decapoda: Cambaridae)","docAbstract":"We summarize the distribution, ecology, threats, and conservation status of Faxonius marchandi (Hobbs, 1948), the Mammoth Spring crayfish, a limited-range endemic species to the Spring River drainage of Missouri and Arkansas, USA. The species is known from 51 locations on lower-order perennial and intermittent streams in only the eastern portion of the drainage. Faxonius marchandi is found in larger rocky substrates in shallower, slower-velocity habitats of well-buffered, mineral-rich streams. The invading alien crayfish Faxonius neglectus chaenodactylus (Williams, 1952) is the most likely threat to F. marchandi. These compiled data should serve as a baseline for future comparison, and facilitate discussion about future management, conservation, and research efforts.
","language":"English","publisher":"The Crustacean Society","doi":"10.1093/jcbiol/rux075","usgsCitation":"DiStefano, R.J., Magoulick, D.D., Flinders, C., and Imhoff, E., 2017, Conservation status of an imperiled crayfish, Faxonius marchandi Hobbs, 1948 (Decapoda: Cambaridae): Journal of Conservation Biology, v. 37, no. 5, p. 529-534, https://doi.org/10.1093/jcbiol/rux075.","productDescription":"6 p.","startPage":"529","endPage":"534","ipdsId":"IP-087504","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jcbiol/rux075","text":"Publisher Index Page"},{"id":348532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Spring River","volume":"37","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-23","publicationStatus":"PW","scienceBaseUri":"5a05771be4b09af898c7085c","contributors":{"authors":[{"text":"DiStefano, Robert J.","contributorId":178202,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flinders, C.A.","contributorId":6257,"corporation":false,"usgs":true,"family":"Flinders","given":"C.A.","email":"","affiliations":[],"preferred":false,"id":721426,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imhoff, Emily M.","contributorId":145444,"corporation":false,"usgs":false,"family":"Imhoff","given":"Emily M.","affiliations":[],"preferred":false,"id":721427,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190965,"text":"ofr20171118 - 2017 - Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde ","interactions":[],"lastModifiedDate":"2017-11-10T09:59:07","indexId":"ofr20171118","displayToPublicDate":"2017-11-08T10:45:00","publicationYear":"2017","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":"2017-1118","title":"Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde ","docAbstract":"Two Eureka Manta2 3.5 water-quality multiprobe sondes by Eureka Water Probes were tested at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) against known standards over the sonde operating temperatures to verify the manufacturer’s stated accuracy specifications for pH, specific conductance (SC) at 25 degrees Celsius (°C), dissolved oxygen (DO), and turbidity. The Manta2 sondes were evaluated for compliance with the USGS National Field Manual for the Collection of Water-Quality Data (NFM) criteria for continuous water-quality monitors, and for compliance with the manufacturer’s technical specifications. The Manta2 was also evaluated for its compliance to Serial Digital Interface at 1200 baud (SDI-12) version 1.3.
The Manta2 met the NFM recommendations and manufacturer’s accuracy specifications for DO and turbidity at all values tested. The Manta2 pH sensors met the NFM recommendations and manufacturer’s accuracy specification for nominal pH values of 10 and lower. One of the two sensors was out of compliance by 1.2 units for pH 11.16 at 15 °C and by 0.25 unit for pH 10.78 at 40 °C. The Manta2 sensors were within the NFM recommendations for SC, except at 100 microsiemens (μS/cm) at 40 °C, where the SC sensor exceeded the test standard value by as much as 25 percent. One of two sensors was within manufacturer’s accuracy specifications at 25 °C for all the tested SC values, while the other SC sensor was outside the manufacturer’s accuracy specifications at 100 μS/cm, exceeding the test standard value by 9 percent. One of two sensors was outside the manufacturer’s accuracy specifications at 10,000 μS/cm at 15°C, exceeding the test standard value by 3 percent. One Manta2 passed SDI-12 compliance testing with a NR Systems SDI-12 Verifier. One Manta2 was field tested for 6 weeks at USGS station 02492620, National Space Technology Laboratories (NSTL) Station, Mississippi, on the Pearl River and showed overall good agreement with a well-maintained Hydrolab Datasonde 5X site sonde for water temperature, pH, and DO. Differences in SC values between the Manta2 and the site sonde were most likely due to differences in the deployment depth of the sondes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171118","usgsCitation":"Tillman, E.F., 2017, Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde: U.S. Geological Survey Open-File Report 2017–1118, 37 p., https://doi.org/10.3133/ofr20171118. ","productDescription":"vi, 37 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-076099","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":347738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1118/coverthb.jpg"},{"id":347739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1118/ofr20171118.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1118"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
The Columbia River, in Washington and Oregon, and Coos Bay, in Oregon, are economically important shipping channels that are inhabited by several fishes protected under the Endangered Species Act (ESA). Maintenance of shipping channels involves dredge operations to maintain sufficient in-channel depths to allow large ships to navigate the waterways safely. Fishes entrained by dredge equipment often die or experience delayed mortality. Other potential negative effects of dredging include increased turbidity, reductions in prey resources, and the release of harmful contaminants from the dredged sediments. One species of concern is the ESA-listed green sturgeon (Acipenser medirostris; Southern Distinct Population Segment). In this study, we used acoustic telemetry to identify habitat use, arrival and departure timing, and the extent of upstream migration of green sturgeon in the Columbia River and Coos Bay to help inform dredge operations to minimize potential take of green sturgeon. Autonomous acoustic receivers were deployed in Coos Bay from the mouth to river kilometer (rkm) 21.6 from October 2009 through October 2010. In the Columbia River Estuary, receivers were deployed between the mouth and rkm 37.8 from April to November in 2010 and 2011. A total of 29 subadult and adult green sturgeon were tagged with temperature and pressure sensor tags and released during the study, primarily in Willapa Bay and Grays Harbor, Washington, and the Klamath River, Oregon. Green sturgeon detected during the study but released by other researchers also were included in the study.
The number of tagged green sturgeon detected in the two estuaries differed markedly. In Coos Bay, only one green sturgeon was detected for about 2 hours near the estuary mouth. In the Columbia River Estuary, 9 green sturgeon were detected in 2010 and 10 fish were detected in 2011. Green sturgeon entered the Columbia River from May through October during both years, with the greatest numbers of fish being present in August and September. One green sturgeon was detected at the uppermost receiver station (rkm 37.8), but overall, the number of fish detected upriver decreased rapidly with distance from the estuary mouth. Residence times of fish that were only detected in the lower 4.8 rkm generally were less than 24 hours, but fish detected farther upriver had a median residence time greater than 10 days. Green sturgeon were widely dispersed among channel and non-channel habitats in the lower estuary in 2010. In 2011, the fish were more concentrated near the estuary mouth. The intensity of use, measured as the total number of fish detections at each station, generally was greatest from Point Ellice (rkm 20.1) to Rice Island (rkm 33.0) in channel and shallow shoal areas, and lowest at the stations west of Point Ellice with the exception of the area near the entrance to the Ilwaco Channel.
Sensor tag data indicated that the deeper South and North Channel habitats (bottom depth ≥10 m) were used, as were the more shallow sandy shoal, shoreline, and bay habitats (bottom depth <10 m). Median fish depths among fish and receiver locations ranged from 2.5 to 28.2 m below water surface (bws) and water temperatures ranged from 9.1 to 22.0 °C during late May through mid-October. In the deeper channel habitat, near the Ilwaco Channel, fish inhabited water with median temperatures ranging from 11.4 to 16.7 °C, whereas east of Point Ellice, predominantly in shallow non-channel habitats, fish inhabited water with median temperatures ranging from about 17.0 to 21.0 °C.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171144","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansel, H.C., Romine, J.G., and Perry, R.W., 2017, Acoustic tag detections of green sturgeon in the Columbia River and Coos Bay estuaries, Washington and Oregon, 2010–11: U.S. Geological Survey Open-File Report 2017-1144, 30 p., https://doi.org/10.3133/ofr20171144.","productDescription":"vi, 30 p.","onlineOnly":"Y","ipdsId":"IP-088817","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":348413,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1144/ofr20171144.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1144"},{"id":348412,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1144/coverthb.jpg"}],"country":"United States","state":"Oregon","city":"Astoria","otherGeospatial":"Coos Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.36283111572264,\n 43.33067209551502\n ],\n [\n -124.12696838378908,\n 43.33067209551502\n ],\n [\n -124.12696838378908,\n 43.476591264232674\n ],\n [\n -124.36283111572264,\n 43.476591264232674\n ],\n [\n -124.36283111572264,\n 43.33067209551502\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.09263610839842,\n 46.14416148780093\n ],\n [\n -123.61129760742186,\n 46.14416148780093\n ],\n [\n -123.61129760742186,\n 46.32559414426375\n ],\n [\n -124.09263610839842,\n 46.32559414426375\n ],\n [\n -124.09263610839842,\n 46.14416148780093\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Aim
Adult survival is central to theories explaining latitudinal gradients in life history strategies. Life history theory predicts higher adult survival in tropical than north temperate regions given lower fecundity and parental effort. Early studies were consistent with this prediction, but standard-effort netting studies in recent decades suggested that apparent survival rates in temperate and tropical regions strongly overlap. Such results do not fit with life history theory. Targeted marking and resighting of breeding adults yielded higher survival estimates in the tropics, but this approach is thought to overestimate survival because it does not sample social and age classes with lower survival. We compared the effect of field methods on tropical survival estimates and their relationships with life history traits.
Location
Sabah, Malaysian Borneo.
Time period
2008–2016.
Major taxon
Passeriformes.
Methods
We used standard-effort netting and resighted individuals of all social and age classes of 18 tropical songbird species over 8 years. We compared apparent survival estimates between these two field methods with differing analytical approaches.
Results
Estimated detection and apparent survival probabilities from standard-effort netting were similar to those from other tropical studies that used standard-effort netting. Resighting data verified that a high proportion of individuals that were never recaptured in standard-effort netting remained in the study area, and many were observed breeding. Across all analytical approaches, addition of resighting yielded substantially higher survival estimates than did standard-effort netting alone. These apparent survival estimates were higher than for temperate zone species, consistent with latitudinal differences in life histories. Moreover, apparent survival estimates from addition of resighting, but not from standard-effort netting alone, were correlated with parental effort as measured by egg temperature across species.
Main conclusions
Inclusion of resighting showed that standard-effort netting alone can negatively bias apparent survival estimates and obscure life history relationships across latitudes and among tropical species.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.12661","usgsCitation":"Martin, T.E., Riordan, M.M., Repin, R., Mouton, J.C., and Blake, W.M., 2017, Apparent annual survival estimates of tropical songbirds better reflect life history variation when based on intensive field methods: Global Ecology and Biogeography, v. 26, no. 12, p. 1386-1397, https://doi.org/10.1111/geb.12661.","productDescription":"12 p.","startPage":"1386","endPage":"1397","ipdsId":"IP-082313","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.12661","text":"Publisher Index Page"},{"id":348475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"MalaysianBorneo ","otherGeospatial":"Kinabalu Park","volume":"26","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-20","publicationStatus":"PW","scienceBaseUri":"5a0425b2e4b0dc0b45b4530d","contributors":{"authors":[{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riordan, Margaret M.","contributorId":198673,"corporation":false,"usgs":false,"family":"Riordan","given":"Margaret","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":716739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Repin, Rimi","contributorId":198674,"corporation":false,"usgs":false,"family":"Repin","given":"Rimi","email":"","affiliations":[],"preferred":false,"id":716740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mouton, James C.","contributorId":198675,"corporation":false,"usgs":false,"family":"Mouton","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blake, William M.","contributorId":198676,"corporation":false,"usgs":false,"family":"Blake","given":"William","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":716742,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192629,"text":"70192629 - 2017 - Estimating occupancy and abundance using aerial images with imperfect detection","interactions":[],"lastModifiedDate":"2017-12-11T13:14:42","indexId":"70192629","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy and abundance using aerial images with imperfect detection","docAbstract":"We present a Dynamic Energy Budget (DEB) model for the eastern oyster, Crassostrea virginica, which enables the inclusion of salinity as a third environmental variable, on top of the standard foodr and temperature variables. Salinity changes have various effects on the physiology of oysters, potentially altering filtration and respiration rates, and ultimately impacting growth, reproduction and mortality. We tested different hypotheses as to how to include these effects in a DEB model for C. virginica. Specifically, we tested two potential mechanisms to explain changes in oyster shell growth (cm), tissue dry weight (g) and gonad dry weight (g) when salinity moves away from the ideal range: 1) a negative effect on filtration rate and 2) an additional somatic maintenance cost. Comparative simulations of shell growth, dry tissue biomass and dry gonad weight in two monitored sites in coastal Louisiana experiencing salinity from 0 to 28 were statistically analyzed to determine the best hypothesis. Model parameters were estimated through the covariation method, using literature data and a set of specifically designed ecophysiological experiments. The model was validated through independent field studies in estuaries along the northern Gulf of Mexico. Our results suggest that salinity impacts C. virginica’s energy budget predominantly through effects on filtration rate. With an overwhelming number of environmental factors impacting organisms, and increasing exposure to novel and extreme conditions, the mechanistic nature of the DEB model with its ability to incorporate more than the standard food and temperature variables provides a powerful tool to verify hypotheses and predict individual organism performance across a range of conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.09.003","usgsCitation":"Lavaud, R., LaPeyre, M.K., Casas, S.M., Bacher, C., and La Peyre, J.F., 2017, Integrating the effects of salinity on the physiology of the eastern oyster, Crassostrea virginica, in the northern Gulf of Mexico through a Dynamic Energy Budget model: Ecological Modelling, v. 363, p. 221-233, https://doi.org/10.1016/j.ecolmodel.2017.09.003.","productDescription":"13 p.","startPage":"221","endPage":"233","ipdsId":"IP-086164","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348420,"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 -99.140625,\n 24.367113562651262\n ],\n [\n -79.189453125,\n 24.367113562651262\n ],\n [\n -79.189453125,\n 33.063924198120645\n ],\n [\n -99.140625,\n 33.063924198120645\n ],\n [\n -99.140625,\n 24.367113562651262\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"363","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425aee4b0dc0b45b452f1","contributors":{"authors":[{"text":"Lavaud, Romain","contributorId":200114,"corporation":false,"usgs":false,"family":"Lavaud","given":"Romain","email":"","affiliations":[],"preferred":false,"id":721040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":720625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casas, Sandra M.","contributorId":145452,"corporation":false,"usgs":false,"family":"Casas","given":"Sandra","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bacher, C.","contributorId":69742,"corporation":false,"usgs":true,"family":"Bacher","given":"C.","email":"","affiliations":[],"preferred":false,"id":721042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Jerome F.","contributorId":34697,"corporation":false,"usgs":true,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":721043,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209685,"text":"70209685 - 2017 - Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA","interactions":[],"lastModifiedDate":"2020-04-21T16:08:17.708545","indexId":"70209685","displayToPublicDate":"2017-11-06T11:01:32","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA","docAbstract":"We present an electrical resistivity model of the crust and upper mantle from two‐dimensional (2‐D) anisotropic inversion of magnetotelluric data collected along a 450 km transect of the Rio Grande rift, southern Rocky Mountains, and High Plains in Colorado, USA. Our model provides a window into the modern‐day lithosphere beneath the Rocky Mountain Front to depths in excess of 150 km. Two key features of the 2‐D resistivity model are (1) a broad zone (~200 km wide) of enhanced electrical conductivity (<20 Ωm) in the midcrust to lower crust that is centered beneath the highest elevations of the southern Rocky Mountains and (2) hydrated lithospheric mantle beneath the Great Plains with water content in excess of 100 ppm. We interpret the high conductivity region of the lower crust as a zone of partially molten basalt and associated deep‐crustal fluids that is the result of recent (less than 10 Ma) tectonic activity in the region. The recent supply of volatiles and/or heat to the base of the crust in the late Cenozoic implies that modern‐day tectonic activity in the western United States extends to at least the western margin of the Great Plains. The transition from conductive to resistive upper mantle is caused by a gradient in lithospheric modification, likely including hydration of nominally anhydrous minerals, with maximum hydration occurring beneath the Rocky Mountain Front. This lithospheric “hydration front” has implications for the tectonic evolution of the continental interior and the mechanisms by which water infiltrates the lithosphere.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014474","collaboration":"","usgsCitation":"Feucht, D., Sheehan, A.F., and Bedrosian, P.A., 2017, Magnetotelluric imaging of lower crustal melt and lithospheric hydration in the Rocky Mountain Front transition zone, Colorado, USA: Journal of Geophysical Research B: Solid Earth, v. 122, no. 12, p. 9489-9510, https://doi.org/10.1002/2017JB014474.","productDescription":"22 p.","startPage":"9489","endPage":"9510","ipdsId":"IP-091898","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":469344,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jb014474","text":"Publisher Index Page"},{"id":374159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.061279296875,\n 37.01132594307015\n ],\n [\n -102.052001953125,\n 37.01132594307015\n ],\n [\n -102.052001953125,\n 40.98819156349393\n ],\n [\n -109.061279296875,\n 40.98819156349393\n ],\n [\n -109.061279296875,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Feucht, D. W. 0000-0002-3672-4719","orcid":"https://orcid.org/0000-0002-3672-4719","contributorId":224277,"corporation":false,"usgs":false,"family":"Feucht","given":"D. W.","affiliations":[],"preferred":false,"id":787515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheehan, Anne F 0000-0002-9629-1687","orcid":"https://orcid.org/0000-0002-9629-1687","contributorId":224234,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","email":"","middleInitial":"F","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":787516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":787517,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193674,"text":"70193674 - 2017 - Comparing measurement response and inverted results of electrical resistivity tomography instruments","interactions":[],"lastModifiedDate":"2017-11-06T11:19:36","indexId":"70193674","displayToPublicDate":"2017-11-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3928,"text":"Journal of Environmental & Engineering Geophysics","printIssn":"1083-1363","active":true,"publicationSubtype":{"id":10}},"title":"Comparing measurement response and inverted results of electrical resistivity tomography instruments","docAbstract":"In this investigation, we compare the results of electrical resistivity measurements made by six commercially available instruments on the same line of electrodes to determine if there are differences in the measured data or inverted results. These comparisons are important to determine whether measurements made between different instruments are consistent. We also degraded contact resistance on one quarter of the electrodes to study how each instrument responds to different electrical connection with the ground. We find that each instrument produced statistically similar apparent resistivity results, and that any conservative assessment of the final inverted resistivity models would result in a similar interpretation for each. We also note that inversions, as expected, are affected by measurement error weights. Increased measurement errors were most closely associated with degraded contact resistance in this set of experiments. In a separate test we recorded the full measured waveform for a single four-electrode array to show how poor electrode contact and instrument-specific recording settings can lead to systematic measurement errors. We find that it would be acceptable to use more than one instrument during an investigation with the expectation that the results would be comparable assuming contact resistance remained consistent.","language":"English","publisher":"Environmental and Engineering Geophysical Society","doi":"10.2113/JEEG22.3.249","usgsCitation":"Parsekian, A.D., Claes, N., Singha, K., Minsley, B.J., Carr, B., Voytek, E., Harmon, R., Kass, A., Carey, A., Thayer, D., and Flinchum, B., 2017, Comparing measurement response and inverted results of electrical resistivity tomography instruments: Journal of Environmental & Engineering Geophysics, v. 22, no. 3, p. 249-266, https://doi.org/10.2113/JEEG22.3.249.","productDescription":"18 p.","startPage":"249","endPage":"266","ipdsId":"IP-080728","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":348258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-18","publicationStatus":"PW","scienceBaseUri":"5a07e846e4b09af898c8cb2c","contributors":{"authors":[{"text":"Parsekian, Andrew D.","contributorId":23829,"corporation":false,"usgs":false,"family":"Parsekian","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":719851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Claes, Niels","contributorId":199728,"corporation":false,"usgs":false,"family":"Claes","given":"Niels","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":719852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":719853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":719850,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carr, Bradley","contributorId":175482,"corporation":false,"usgs":false,"family":"Carr","given":"Bradley","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":719854,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Voytek, Emily","contributorId":199729,"corporation":false,"usgs":false,"family":"Voytek","given":"Emily","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":719855,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harmon, Ryan","contributorId":191252,"corporation":false,"usgs":false,"family":"Harmon","given":"Ryan","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":720662,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kass, Andy","contributorId":191248,"corporation":false,"usgs":true,"family":"Kass","given":"Andy","email":"","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":720663,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carey, Austin","contributorId":149257,"corporation":false,"usgs":false,"family":"Carey","given":"Austin","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":720664,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thayer, Drew","contributorId":190722,"corporation":false,"usgs":false,"family":"Thayer","given":"Drew","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":720665,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Flinchum, Brady","contributorId":199732,"corporation":false,"usgs":false,"family":"Flinchum","given":"Brady","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":720666,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70193634,"text":"70193634 - 2017 - Examining the occupancy–density relationship for a low-density carnivore","interactions":[],"lastModifiedDate":"2017-11-29T16:09:23","indexId":"70193634","displayToPublicDate":"2017-11-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Examining the occupancy–density relationship for a low-density carnivore","docAbstract":"The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non-invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools.
For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions.
Here, we used a large-scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096-km2 region of western New York, USA. We fit occupancy and Royle–Nichols models to species detection–non-detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15-km2 grid cells was modelled as a function of landscape attributes known to influence fisher distribution.
We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non-detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous-mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non-detection data.
Synthesis and applications. Our work provides empirical evidence that models using detection–non-detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost-effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non-detection data will be affected by aspects of study design and species ecology.
This map is an interpretation of a modern lidar digital elevation model combined with the geology depicted on the Geologic Map of the Des Moines 7.5' Quadrangle, King County, Washington (Booth and Waldron, 2004). Booth and Waldron described, interpreted, and located the geology on the 1:24,000-scale topographic map of the Des Moines 7.5' quadrangle. The base map that they used was originally compiled in 1943 and revised using 1990 aerial photographs; it has 25-ft contours, nominal horizontal resolution of about 40 ft (12 m), and nominal mean vertical accuracy of about 10 ft (3 m). Similar to many geologic maps, much of the geology in the Booth and Waldron (2004) map was interpreted from landforms portrayed on the topographic map. In 2001, the Puget Sound Lidar Consortium obtained a lidar-derived digital elevation model (DEM) for much of the Puget Sound area, including the entire Des Moines 7.5' quadrangle. This new DEM has a horizontal resolution of about 6 ft (2 m) and a mean vertical accuracy of about 1 ft (0.3 m). The greater resolution and accuracy of the lidar DEM compared to topography constructed from air-photo stereo models have much improved the interpretation of geology, even in this heavily developed area, especially the distribution and relative age of some surficial deposits. For a brief description of the light detection and ranging (lidar) remote sensing method and this data acquisition program, see Haugerud and others (2003).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3384","usgsCitation":"Tabor, R.W., and Booth, D.B., 2017, Lidar-revised geologic map of the Des Moines 7.5' quadrangle, King County, Washington: U.S. Geological Survey Scientific Investigations Map 3384, 17 p., 1 sheet, scale 1:24,000, https://doi.org/10.3133/sim3384.","productDescription":"Pamphlet: iii, 17 p.; 1 Sheet: 30.83 x 30.04 inches; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056389","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":399114,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_106502.htm"},{"id":348331,"rank":9,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3384/","text":"Shapefiles and CSV","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3384 shapefiles and CSV"},{"id":348329,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_gdb.zip","text":"Geodatabase","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3384 geodatabase"},{"id":348328,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_readme.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3384 readme"},{"id":348330,"rank":8,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_simple.zip","text":"Shapefiles","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3384 shapefiles"},{"id":348325,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3384 pamphlet"},{"id":348327,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_metadata.xml","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3384 metadata xml"},{"id":348326,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3384/sim3384_metadata.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3384 metadata txt"},{"id":348324,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3384/sim3384.pdf","text":"Map","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3384"},{"id":348323,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3384/coverthb.jpg"}],"scale":"24000","country":"United States","state":"Washington","county":"King County","otherGeospatial":"Des Moines 7.5' quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.375,\n 47.375\n ],\n [\n -122.25,\n 47.375\n ],\n [\n -122.25,\n 47.5\n ],\n [\n -122.375,\n 47.5\n ],\n [\n -122.375,\n 47.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650/329-4936
Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. We present a general integrated population model (IPM), specifically designed for brown bears (Ursus arctos), using three common data types for bear (U. spp.) populations: repeated counts, capture–mark–recapture, and litter size. We considered factors affecting ecological and observation processes for these data. We assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. We then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on Kodiak Island, Alaska, USA. The IPM provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. For the Kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. We detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. Survival probabilities increased from cubs to yearlings to dependent young ≥2 years old and decreased with litter size. Linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. IPMs can also reduce data collection efforts by sharing information among agencies and management units. Our approach responds to an increasing need in bear populations’ management and can be readily adapted to other large carnivores.
Groundwater data were collected on the Quinault Indian Reservation to provide the Quinualt Indian Nation (QIN) with basic knowledge of the existing wells and springs on the reservation, and to establish a water-level network to be monitored by QIN to begin building a long-term groundwater dataset. The 327 mi2 Quinault Indian Reservation is located within the heavily forested Queets-Quinault watershed along the west-central coast of Washington and includes the coastal communities of Taholah and Queets, and the inland community of Amanda Park. Groundwater data were collected or compiled for 87 sites—82 wells and 5 springs. In October 2016, a field inventory was done to locate the sites and acquire site data. Groundwater levels were measured in 15 of the field-inventoried wells and 3 of those wells were observed as flowing (artesian). A monthly groundwater‑level monitoring network of 13 wells was established by the U.S. Geological Survey in March 2017, and the network was transferred to QIN in June 2017 for continued measurements.
Several data needs were identified that would provide a more complete understanding of the groundwater system of the Quinault Indian Reservation. The collection of monthly water-level data for multiple years is an important first step in understanding seasonal and long term changes in water levels. Additionally, the collection of baseline groundwater chemistry and quality data across the reservation would help with future efforts to monitor existing and potentially changing groundwater quality conditions. Development of a water budget of the Queets-Quinault Watershed and the reservation within that area would provide water users with a better understanding of this important resource and provide needed information about the competing demands on local water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1071","collaboration":"Prepared in cooperation with the Quinault Indian Nation","usgsCitation":"Kahle, S.C., Fasser, E.T., and Olsen, T.D., 2017, Groundwater data collection for the Quinault Indian Nation, Grays Harbor and Jefferson Counties, Washington: U.S. Geological Survey Data Series 1071, 13 p., https://doi.org/10.3133/ds1071.","productDescription":"iv, 13 p.","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-088886","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":348178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1071/coverthb.jpg"},{"id":348179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1071/ds1071.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1071"}],"country":"United States","state":"Washington","county":" Grays Harbor County, Jefferson County","otherGeospatial":" Quinault Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.36386108398438,\n 47.245678021018755\n ],\n [\n -123.87359619140624,\n 47.245678021018755\n ],\n [\n -123.87359619140624,\n 47.56726060598141\n ],\n [\n -124.36386108398438,\n 47.56726060598141\n ],\n [\n -124.36386108398438,\n 47.245678021018755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
An investigation was completed by the U.S. Geological Survey in cooperation with the Indiana Office of Community and Rural Affairs that found 1,132 transportation and utility assets in Indiana are vulnerable to fluvial erosion hazards due to close proximity to actively migrating streams. Locations of transportation assets (bridges, roadways, and railroad lines) and selected utility assets (high-capacity overhead power-transmission lines, underground pipelines, water treatment facilities, and in-channel dams) were determined using aerial imagery hosted by the Google Earth platform. Identified assets were aggregated by stream reach, county, and class. Accompanying the report is a polyline shapefile of the stream reaches documented by Robinson. The shapefile, derived from line work in the National Hydrography Dataset and attributed with channel migration rates, is released with complete Federal Geographic Data Committee metadata. The data presented in this report are intended to help stakeholders and others identify high-risk areas where transportation and utility assets may be threatened by fluvial erosion hazards thus warranting consideration for mitigation strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1068","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Sperl, B.J., 2017, Vulnerable transportation and utility assets near actively migrating streams in Indiana: U.S. Geological Survey Data Series 1068, 11 p., https://doi.org/10.3133/ds1068.","productDescription":"Report: iv, 11 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086741","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":347784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1068/ds1068.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Hurricane Matthew moved adjacent to the coasts of Florida, Georgia, South Carolina, and North Carolina. The hurricane made landfall once near McClellanville, South Carolina, on October 8, 2016, as a Category 1 hurricane on the Saffir-Simpson Hurricane Wind Scale. The U.S. Geological Survey (USGS) deployed a temporary monitoring network of storm-tide sensors at 284 sites along the Atlantic coast from Florida to North Carolina to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by Hurricane Matthew. Storm tide, as defined by the National Oceanic and Atmospheric Administration, is the water-level rise generated by a combination of storm surge and astronomical tide during a coastal storm.
The deployment for Hurricane Matthew was the largest deployment of storm-tide sensors in USGS history and was completed as part of a coordinated Federal emergency response as outlined by the Stafford Act (Public Law 92–288, 42 U.S.C. 5121–5207) under a directed mission assignment by the Federal Emergency Management Agency. In total, 543 high-water marks (HWMs) also were collected after Hurricane Matthew, and this was the second largest HWM recovery effort in USGS history after Hurricane Sandy in 2012.
During the hurricane, real-time water-level data collected at temporary rapid deployment gages (RDGs) and long-term USGS streamgage stations were relayed immediately for display on the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#MatthewOctober2016). These data provided emergency managers and responders with critical information for tracking flood-effected areas and directing assistance to effected communities. Data collected from this hurricane can be used to calibrate and evaluate the performance of storm-tide models for maximum and incremental water level and flood extent, and the site-specific effects of storm tide on natural and anthropogenic features of the environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171122","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Frantz, E.R., Byrne, M.J., Sr., Caldwell, A.W., and Harden, S.L., 2017, Monitoring storm tide and flooding from Hurricane Matthew along the Atlantic coast of the United States, October 2016: U.S. Geological Survey Open-File Report 2017–1122, 37 p., https://doi.org/10.3133/ofr20171122.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081187","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":347956,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1122/ofr20171122.pdf","text":"Report","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1122"},{"id":347955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1122/coverthb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.57421875,\n 36.56260003738545\n ],\n [\n -78.50830078125,\n 35.585851593232356\n ],\n [\n -79.69482421875,\n 34.903952965590065\n ],\n [\n -81.9140625,\n 33.358061612778876\n ],\n [\n -82.44140625,\n 31.70947636001935\n ],\n [\n -82.7490234375,\n 30.713503990354965\n ],\n [\n -82.0458984375,\n 28.998531814051795\n ],\n [\n -81.2548828125,\n 27.00040800352175\n ],\n [\n -81.650390625,\n 25.224820176765036\n ],\n [\n -80.595703125,\n 24.666986385216273\n ],\n [\n -79.3212890625,\n 25.20494115356912\n ],\n [\n -79.4970703125,\n 26.96124577052697\n ],\n [\n -79.82666015625,\n 27.994401411046148\n ],\n [\n -80.26611328125,\n 29.592565403314087\n ],\n [\n -80.35400390625,\n 31.259769987394286\n ],\n [\n -78.57421875,\n 32.80574473290688\n ],\n [\n -76.3330078125,\n 33.46810795527896\n ],\n [\n -74.619140625,\n 34.397844946449865\n ],\n [\n -73.32275390625,\n 36.56260003738545\n ],\n [\n -78.57421875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
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Digital flood-inundation maps for a 7.5-mile reach of the White River at Noblesville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the White River at Noblesville, Ind., streamgage (USGS station number 03349000). Real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at the same site as the USGS streamgage (NWS site NBLI3).
Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current (2016) stage-discharge rating at the USGS streamgage 03349000, White River at Noblesville, Ind., and documented high-water marks from the floods of September 4, 2003, and May 6, 2017. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 10.0 ft (the NWS “action stage”) to 24.0 ft, which is the highest stage interval of the current (2016) USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each stage.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175123","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Martin, Z.W., 2017, Flood-inundation maps for the White River at Noblesville, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5123, 11 p., https://doi.org/10.3133/sir20175123.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086871","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":347858,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5123/sir20175123.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5123"},{"id":347859,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7N0J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"White River at Noblesville, Indiana, flood-inundation model and GIS data"},{"id":347857,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5123/coverthb.jpg"}],"country":"United States","state":"Indiana","city":"Noblesville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.03616714477539,\n 40.033792168980135\n ],\n [\n -85.95720291137695,\n 40.033792168980135\n ],\n [\n -85.95720291137695,\n 40.10919420673381\n ],\n [\n -86.03616714477539,\n 40.10919420673381\n ],\n [\n -86.03616714477539,\n 40.033792168980135\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278–1996
The 3D Elevation Program (3DEP) of the U.S. Geological Survey (USGS) acquires high-resolution elevation data for the Nation. This program has been operating under an opportunity-oriented approach, acquiring light detection and ranging (lidar) projects of varying sizes scattered across the United States. As a result, the national 3DEP elevation layer is subject to data gaps or unnecessary overlap between adjacent collections. To mitigate this problem, 3DEP is adopting a strategic, systematic approach to national data acquisition that will create efficiencies in efforts to achieve nationwide elevation data coverage and help capture additional Federal and non-Federal investments resulting from advance awareness of proposed acquisitions and partnership opportunities. The 3DEP Working Group, an interagency group managed by the USGS, has agreed that all future 3DEP collections within the lower 48 States should be coordinated by using a 1-kilometer by 1-kilometer tiling scheme for the conterminous United States. Fiscal Year 2018 is being considered a transition year, and in Fiscal Year 2019 the national indexing scheme will be fully implemented, so that all 3DEP-supported projects will be acquired and delivered in the national indexing scheme and projected into the Albers Equal Area projection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173073","issn":"2327-6916","usgsCitation":"Thatcher, C.A., Heidemann, H.K., Stoker, J.M., and Eldridge, D.F., 2017, The 3D Elevation Program national indexing scheme: U.S. Geological Survey Fact Sheet 2017-3073, 2 p., https://doi.org/10.3133/fs20173073.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088200","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":346327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3073/fs20173073.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3073"},{"id":346326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3073/coverthb.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
Or visit the National Geospatial Program website at https://www2.usgs.gov/ngpo/
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