The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2017.08.002","usgsCitation":"Dewitt, J., Chirico, P.G., Bergstresser, S.E., and Warner, T.A., 2017, Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting: Remote Sensing Applications: Society and Environment, v. 8, p. 126-139, https://doi.org/10.1016/j.rsase.2017.08.002.","productDescription":"14 p.","startPage":"126","endPage":"139","ipdsId":"IP-080382","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":469319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2017.08.002","text":"Publisher Index Page"},{"id":349018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Côte d′Ivoire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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pchirico@usgs.gov","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":195555,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"pchirico@usgs.gov","middleInitial":"G.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":718839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergstresser, Sarah E. 0000-0003-0182-5779 sbergstresser@usgs.gov","orcid":"https://orcid.org/0000-0003-0182-5779","contributorId":195556,"corporation":false,"usgs":true,"family":"Bergstresser","given":"Sarah","email":"sbergstresser@usgs.gov","middleInitial":"E.","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":718840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, Timothy A. 0000-0002-0414-9748","orcid":"https://orcid.org/0000-0002-0414-9748","contributorId":195554,"corporation":false,"usgs":false,"family":"Warner","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718841,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194000,"text":"70194000 - 2017 - eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data","interactions":[],"lastModifiedDate":"2018-03-26T14:30:08","indexId":"70194000","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"displayTitle":"eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data","title":"eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data","docAbstract":"In this article we describe eDNAoccupancy, an R package for fitting Bayesian, multi-scale occupancy models. These models are appropriate for occupancy surveys that include three, nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit, and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). eDNAoccupancy allows users to specify and fit multi-scale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analyzing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12735","usgsCitation":"Dorazio, R., and Erickson, R.A., 2017, eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data: Molecular Ecology Resources, v. 18, no. 2, p. 368-380, https://doi.org/10.1111/1755-0998.12735.","productDescription":"13 p.","startPage":"368","endPage":"380","ipdsId":"IP-087512","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469322,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.12735","text":"Publisher Index Page"},{"id":438153,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23Z67","text":"USGS data release","linkHelpText":"eDNAoccupancy"},{"id":348737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350191,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://my.usgs.gov/bitbucket/projects/USGS_WARC/repos/ednaoccupancy/browse","description":"Software release"}],"volume":"18","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-02","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22beb","contributors":{"authors":[{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":721882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":721883,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":70193273,"text":"70193273 - 2017 - The role of deep-water sedimentary processes in shaping a continental margin: The Northwest Atlantic","interactions":[],"lastModifiedDate":"2017-11-29T16:01:31","indexId":"70193273","displayToPublicDate":"2017-11-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"The role of deep-water sedimentary processes in shaping a continental margin: The Northwest Atlantic","docAbstract":"The tectonic history of a margin dictates its general shape; however, its geomorphology is generally transformed by deep-sea sedimentary processes. The objective of this study is to show the influences of turbidity currents, contour currents and sediment mass failures on the geomorphology of the deep-water northwestern Atlantic margin (NWAM) between Blake Ridge and Hudson Trough, spanning about 32° of latitude and the shelf edge to the abyssal plain. This assessment is based on new multibeam echosounder data, global bathymetric models and sub-surface geophysical information.
The deep-water NWAM is divided into four broad geomorphologic classifications based on their bathymetric shape: graded, above-grade, stepped and out-of-grade. These shapes were created as a function of the balance between sediment accumulation and removal that in turn were related to sedimentary processes and slope-accommodation. This descriptive method of classifying continental margins, while being non-interpretative, is more informative than the conventional continental shelf, slope and rise classification, and better facilitates interpretation concerning dominant sedimentary processes.
Areas of the margin dominated by turbidity currents and slope by-pass developed graded slopes. If sediments did not by-pass the slope due to accommodation then an above grade or stepped slope resulted. Geostrophic currents created sedimentary bodies of a variety of forms and positions along the NWAM. Detached drifts form linear, above-grade slopes along their crests from the shelf edge to the deep basin. Plastered drifts formed stepped slope profiles. Sediment mass failure has had a variety of consequences on the margin morphology; large mass-failures created out-of-grade profiles, whereas smaller mass failures tended to remain on the slope and formed above-grade profiles at trough-mouth fans, or nearly graded profiles, such as offshore Cape Fear.
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.
Estimating population trends provides valuable information for resource managers, but monitoring programs face trade-offs between the quality and quantity of information gained and the number of sites surveyed. We compared the effectiveness of monitoring techniques for estimating population trends of Rana draytonii (California Red-legged Frog) at Point Reyes National Seashore, California, USA, over a 13-yr period. Our primary goals were to: 1) estimate trends for a focal pond at Point Reyes National Seashore, and 2) evaluate whether egg mass counts could reliably estimate an index of abundance relative to more-intensive capture–mark–recapture methods. Capture–mark–recapture (CMR) surveys of males indicated a stable population from 2005 to 2009, despite low annual apparent survival (26.3%). Egg mass counts from 2000 to 2012 indicated that despite some large fluctuations, the breeding female population was generally stable or increasing, with annual abundance varying between 26 and 130 individuals. Minor modifications to egg mass counts, such as marking egg masses, can allow estimation of egg mass detection probabilities necessary to convert counts to abundance estimates, even when closure of egg mass abundance cannot be assumed within a breeding season. High egg mass detection probabilities (mean per-survey detection probability = 0.98 [0.89–0.99]) indicate that egg mass surveys can be an efficient and reliable method for monitoring population trends of federally threatened R. draytonii. Combining egg mass surveys to estimate trends at many sites with CMR methods to evaluate factors affecting adult survival at focal populations is likely a profitable path forward to enhance understanding and conservation of R. draytonii.
","language":"English","publisher":"The Society for the Study of Amphibians and Reptiles","doi":"10.1670/17-054","usgsCitation":"Fellers, G.M., Kleeman, P.M., Miller, D.A., and Halstead, B., 2017, Population trends, survival, and sampling methodologies for a population of Rana draytonii: Journal of Herpetology, v. 51, no. 4, p. 567-573, https://doi.org/10.1670/17-054.","productDescription":"7 p.","startPage":"567","endPage":"573","ipdsId":"IP-075545","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":348562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Point Reyes National Seashore","volume":"51","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8c0e4b09af898c860b6","contributors":{"authors":[{"text":"Fellers, Gary M. 0000-0003-4092-0285 gary_fellers@usgs.gov","orcid":"https://orcid.org/0000-0003-4092-0285","contributorId":3150,"corporation":false,"usgs":true,"family":"Fellers","given":"Gary","email":"gary_fellers@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":721462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":721463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A.W. davidmiller@usgs.gov","contributorId":4043,"corporation":false,"usgs":true,"family":"Miller","given":"David","email":"davidmiller@usgs.gov","middleInitial":"A.W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":721464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":721465,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192625,"text":"70192625 - 2017 - Bias correction of bounded location errors in presence-only data","interactions":[],"lastModifiedDate":"2017-11-10T10:57:21","indexId":"70192625","displayToPublicDate":"2017-11-10T00:00:00","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":"Bias correction of bounded location errors in presence-only data","docAbstract":"Since the early 1990s, >5,000 ha of historic wetlands (and adjacent prairie) have been restored on the row-crop agricultural landscape of Winnebago County, Iowa, USA. From 2008–2011, we surveyed 22 of these sites for probabilities of occupancy and colonization by Boreal Chorus Frogs (BCF; Pseudacris maculata), Northern Leopard Frogs (NLF; Lithobates pipiens), and American Toads (AT; Anaxyrus americanus). We used radio telemetry to measure patterns of movement and habitat use by 22 NLF and 54 AT and deployed biophysical models in available habitats to estimate their physiological costs. The BCF occupied 100% of restored wetlands; NLF and AT occupied 59–91% and 71–89%, respectively, varying according to annual weather conditions. The BCF colonized new sites within a year; NLF and AT required 3 and 2 yr, respectively. These differences were related to distances from the nearest established population and costs of intervening cover types, and were statistically related to the size and orientation of restored wetlands. The ranges of maximum straight-line distances moved by NLF and AT were 31–857 m and 42–2,932 m, respectively. Both NLF and AT selected wetlands and surrounding prairies, though NLF were nine times more likely to select wetland habitats than all others combined. About 24% of AT used row-crop fields extensively, but not until crops had grown sufficiently to reduce the physiological costs of these fields similar to that of prairies. Both BCF and AT navigated the dramatically altered row-crop landscape, but NLF depended more heavily on roadside ditches to find and colonize restored wetlands.
","language":"English","publisher":"The Society for the Study of Amphibians and Reptiles","doi":"10.1670/16-113","usgsCitation":"Bartelt, P.E., and Klaver, R.W., 2017, Response of anurans to wetland restoration on a midwestern agriculture landscape: Journal of Herpetology, v. 51, no. 4, p. 504-514, https://doi.org/10.1670/16-113.","productDescription":"11 p.","startPage":"504","endPage":"514","ipdsId":"IP-073419","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1670/16-113","text":"Publisher Index Page"},{"id":348598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","county":"Winnebago County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.9691,43.5044],[-93.6782,43.5047],[-93.6485,43.5045],[-93.4964,43.504],[-93.4971,43.4347],[-93.4971,43.3446],[-93.4977,43.2568],[-93.6184,43.2572],[-93.7354,43.257],[-93.853,43.2568],[-93.9699,43.2573],[-93.9705,43.3447],[-93.9699,43.4334],[-93.9691,43.5044]]]},\"properties\":{\"name\":\"Winnebago\",\"state\":\"IA\"}}]}","volume":"51","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8c3e4b09af898c860c2","contributors":{"authors":[{"text":"Bartelt, Paul E.","contributorId":18895,"corporation":false,"usgs":true,"family":"Bartelt","given":"Paul","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":719214,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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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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":70192588,"text":"70192588 - 2017 - Thermal adaptation and phenotypic plasticity in a warming world: Insights from common garden experiments on Alaskan sockeye salmon","interactions":[],"lastModifiedDate":"2017-11-29T16:03:41","indexId":"70192588","displayToPublicDate":"2017-11-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Thermal adaptation and phenotypic plasticity in a warming world: Insights from common garden experiments on Alaskan sockeye salmon","docAbstract":"An important unresolved question is how populations of coldwater-dependent fishes will respond to rapidly warming water temperatures. For example, the culturally and economically important group, Pacific salmon (Oncorhynchus spp.), experience site-specific thermal regimes during early development that could be disrupted by warming. To test for thermal local adaptation and heritable phenotypic plasticity in Pacific salmon embryos, we measured the developmental rate, survival, and body size at hatching in two populations of sockeye salmon (Oncorhynchus nerka) that overlap in timing of spawning but incubate in contrasting natural thermal regimes. Using a split half-sibling design, we exposed embryos of 10 families from each of two populations to variable and constant thermal regimes. These represented both experienced temperatures by each population, and predicted temperatures under plausible future conditions based on a warming scenario from the downscaled global climate model (MIROC A1B scenario). We did not find evidence of thermal local adaptation during the embryonic stage for developmental rate or survival. Within treatments, populations hatched within 1 day of each other, on average, and amongtreatments, did not differ in survival in response to temperature. We did detect plasticity to temperature; embryos developed 2.5 times longer (189 days) in the coolest regime compared to the warmest regime (74 days). We also detected variation in developmental rates among families within and among temperature regimes, indicating heritable plasticity. Families exhibited a strong positive relationship between thermal variability and phenotypic variability in developmental rate but body length and mass at hatching were largely insensitive to temperature. Overall, our results indicated a lack of thermal local adaptation, but a presence of plasticity in populations experiencing contrasting conditions, as well as family-specific heritable plasticity that could facilitate adaptive change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13782","usgsCitation":"Sparks, M.M., Westley, P.A., Falke, J.A., and Quinn, T.P., 2017, Thermal adaptation and phenotypic plasticity in a warming world: Insights from common garden experiments on Alaskan sockeye salmon: Global Change Biology, v. 23, no. 12, p. 5203-5217, https://doi.org/10.1111/gcb.13782.","productDescription":"15 p.","startPage":"5203","endPage":"5217","ipdsId":"IP-081151","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-06","publicationStatus":"PW","scienceBaseUri":"5a06c8c5e4b09af898c860d5","contributors":{"authors":[{"text":"Sparks, Morgan M.","contributorId":200252,"corporation":false,"usgs":false,"family":"Sparks","given":"Morgan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westley, Peter A. H.","contributorId":190530,"corporation":false,"usgs":false,"family":"Westley","given":"Peter","email":"","middleInitial":"A. H.","affiliations":[],"preferred":false,"id":721608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quinn, Thomas P.","contributorId":167272,"corporation":false,"usgs":false,"family":"Quinn","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24671,"text":"School of Aquatic and Fsiery Sciences, UW, Box 355020, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":721609,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193823,"text":"70193823 - 2017 - Efficacy of time-lapse photography and repeated counts abundance estimation for white-tailed deer populations","interactions":[],"lastModifiedDate":"2017-11-09T11:05:20","indexId":"70193823","displayToPublicDate":"2017-11-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5278,"text":"Mammal Research","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of time-lapse photography and repeated counts abundance estimation for white-tailed deer populations","docAbstract":"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":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","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":70193809,"text":"70193809 - 2017 - Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection","interactions":[],"lastModifiedDate":"2017-11-09T11:57:32","indexId":"70193809","displayToPublicDate":"2017-11-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection","docAbstract":"Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-21-5443-2017","usgsCitation":"Poole, S., Vis, M., Knight, R., and Seibert, J., 2017, Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection: Hydrology and Earth System Sciences, v. 21, p. 5443-5457, https://doi.org/10.5194/hess-21-5443-2017.","productDescription":"15 p.","startPage":"5443","endPage":"5457","ipdsId":"IP-078840","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":469334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-21-5443-2017","text":"Publisher Index Page"},{"id":348538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.549560546875,\n 36.474306755095206\n ],\n [\n -89.40673828124999,\n 36.1290016556965\n ],\n [\n -88.450927734375,\n 34.49750272138159\n ],\n [\n -88.04443359375,\n 34.125447565116126\n ],\n [\n -87.29736328125,\n 34.125447565116126\n ],\n [\n -86.63818359374999,\n 34.11635246997272\n ],\n [\n -85.924072265625,\n 34.30714385628802\n ],\n [\n -85.71533203124999,\n 34.56990638085633\n ],\n [\n -85.60546875,\n 34.93998515156041\n ],\n [\n -84.99023437499999,\n 34.99400375757575\n ],\n [\n -84.57275390624999,\n 34.858890491257796\n ],\n [\n -84.12231445312499,\n 34.867904962568744\n ],\n [\n -83.60595703124999,\n 35.54116627999815\n ],\n [\n -83.03466796874999,\n 35.71975793933433\n ],\n [\n -82.86987304687499,\n 35.90684930677118\n ],\n [\n -82.46337890625,\n 36.01356058518151\n ],\n [\n -81.91406249999999,\n 36.1822249804225\n ],\n [\n -81.94702148437499,\n 36.70365959719453\n ],\n [\n -81.73828125,\n 37.21283151445594\n ],\n [\n -81.771240234375,\n 37.39634613318923\n ],\n [\n -81.990966796875,\n 37.47485808497102\n ],\n [\n -82.24365234375,\n 37.33522435930639\n ],\n [\n -83.34228515624999,\n 36.84446074079561\n ],\n [\n -84.08935546874999,\n 36.76529191711621\n ],\n [\n -84.86938476562499,\n 36.756490329505155\n ],\n [\n -85.62744140624999,\n 36.633162095586556\n ],\n [\n -85.81420898437499,\n 36.27970720524017\n ],\n [\n -86.91284179687499,\n 36.11125252076156\n ],\n [\n -87.50610351562499,\n 36.32397712011264\n ],\n [\n -87.80273437499999,\n 36.712467243386264\n ],\n [\n -88.01147460937499,\n 37.020098201368086\n ],\n [\n -88.2861328125,\n 37.142803443716836\n ],\n [\n -88.51684570312499,\n 37.05517710666079\n ],\n [\n -88.72558593749999,\n 37.10776507118514\n ],\n [\n -89.03320312499999,\n 37.17782559332976\n ],\n [\n -89.18701171874999,\n 37.02886944696474\n ],\n [\n -89.12109375,\n 36.976226784640936\n ],\n [\n -89.28588867187499,\n 36.5449494414832\n ],\n [\n -89.43969726562499,\n 36.55377524336086\n ],\n [\n -89.549560546875,\n 36.474306755095206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-06","publicationStatus":"PW","scienceBaseUri":"5a05771de4b09af898c70868","contributors":{"authors":[{"text":"Poole, Sandra","contributorId":174147,"corporation":false,"usgs":false,"family":"Poole","given":"Sandra","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":720577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vis, Marc","contributorId":174146,"corporation":false,"usgs":false,"family":"Vis","given":"Marc","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":720578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Rodney 0000-0001-9588-0167 rrknight@usgs.gov","orcid":"https://orcid.org/0000-0001-9588-0167","contributorId":152422,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney","email":"rrknight@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":720576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seibert, Jan","contributorId":176322,"corporation":false,"usgs":false,"family":"Seibert","given":"Jan","email":"","affiliations":[],"preferred":false,"id":720579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193894,"text":"70193894 - 2017 - Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators","interactions":[],"lastModifiedDate":"2018-09-18T09:59:19","indexId":"70193894","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2385,"text":"Journal of Medical Entomology","active":true,"publicationSubtype":{"id":10}},"title":"Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators","docAbstract":"Tick and mosquito management is important to public health protection. At the same time, growing concerns about declines of pollinator species raise the question of whether vector control practices might affect pollinator populations. We report the results of a task force of the North American Pollinator Protection Campaign (NAPPC) that examined potential effects of vector management practices on pollinators, and how these programs could be adjusted to minimize negative effects on pollinating species. The main types of vector control practices that might affect pollinators are landscape manipulation, biocontrol, and pesticide applications. Some current practices already minimize effects of vector control on pollinators (e.g., short-lived pesticides and application-targeting technologies). Nontarget effects can be further diminished by taking pollinator protection into account in the planning stages of vector management programs. Effects of vector control on pollinator species often depend on specific local conditions (e.g., proximity of locations with abundant vectors to concentrations of floral resources), so planning is most effective when it includes collaborations of local vector management professionals with local experts on pollinators. Interventions can then be designed to avoid pollinators (e.g., targeting applications to avoid blooming times and pollinator nesting habitats), while still optimizing public health protection. Research on efficient targeting of interventions, and on effects on pollinators of emerging technologies, will help mitigate potential deleterious effects on pollinators in future management programs. In particular, models that can predict effects of integrated pest management on vector-borne pathogen transmission, along with effects on pollinator populations, would be useful for collaborative decision-making.","language":"English","publisher":"Oxford University Press","doi":"10.1093/jme/tjx146","usgsCitation":"Ginsberg, H., Bargar, T.A., Hladik, M., and Lubelczyk, C., 2017, Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators: Journal of Medical Entomology, v. 54, no. 6, p. 1463-1475, https://doi.org/10.1093/jme/tjx146.","productDescription":"13 p.","startPage":"1463","endPage":"1475","ipdsId":"IP-083417","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjx146","text":"Publisher Index Page"},{"id":348442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-08","publicationStatus":"PW","scienceBaseUri":"5a0425ade4b0dc0b45b452ec","contributors":{"authors":[{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":721071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bargar, Timothy A. 0000-0001-8588-3436 tbargar@usgs.gov","orcid":"https://orcid.org/0000-0001-8588-3436","contributorId":2450,"corporation":false,"usgs":true,"family":"Bargar","given":"Timothy","email":"tbargar@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":721074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":721072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubelczyk, Charles","contributorId":200128,"corporation":false,"usgs":false,"family":"Lubelczyk","given":"Charles","email":"","affiliations":[{"id":35696,"text":"Maine Medical Center Research Institute","active":true,"usgs":false}],"preferred":false,"id":721073,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193834,"text":"70193834 - 2017 - Free-ranging domestic cats (Felis catus) on public lands: estimating density, activity, and diet in the Florida Keys","interactions":[],"lastModifiedDate":"2018-02-28T09:51:24","indexId":"70193834","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Free-ranging domestic cats (Felis catus) on public lands: estimating density, activity, and diet in the Florida Keys","docAbstract":"Feral and free-ranging domestic cats (Felis catus) can have strong negative effects on small mammals and birds, particularly in island ecosystems. We deployed camera traps to study free-ranging cats in national wildlife refuges and state parks on Big Pine Key and Key Largo in the Florida Keys, USA, and used spatial capture–recapture models to estimate cat abundance, movement, and activities. We also used stable isotope analyses to examine the diet of cats captured on public lands. Top population models separated cats based on differences in movement and detection with three and two latent groups on Big Pine Key and Key Largo, respectively. We hypothesize that these latent groups represent feral, semi-feral, and indoor/outdoor house cats based on the estimated movement parameters of each group. Estimated cat densities and activity varied between the two islands, with relatively high densities (~4 cats/km2) exhibiting crepuscular diel patterns on Big Pine Key and lower densities (~1 cat/km2) exhibiting nocturnal diel patterns on Key Largo. These differences are most likely related to the higher proportion of house cats on Big Pine relative to Key Largo. Carbon and nitrogen isotope ratios from hair samples of free-ranging cats (n = 43) provided estimates of the proportion of wild and anthropogenic foods in cat diets. At the population level, cats on both islands consumed mostly anthropogenic foods (>80% of the diet), but eight individuals were effective predators of wildlife (>50% of the diet). We provide evidence that cat groups within a population move different distances, exhibit different activity patterns, and that individuals consume wildlife at different rates, which all have implications for managing this invasive predator.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-017-1534-x","usgsCitation":"Cove, M., Gardner, B., Simons, T.R., Kays, R., and O’Connell, A.F., 2017, Free-ranging domestic cats (Felis catus) on public lands: estimating density, activity, and diet in the Florida Keys: Biological Invasions, v. 20, no. 2, https://doi.org/10.1007/s10530-017-1534-x.","productDescription":"12 p.","startPage":"344","ipdsId":"IP-082053","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.991455078125,\n 25.522614647623293\n ],\n [\n -80.035400390625,\n 25.596948323286135\n ],\n [\n -80.15625,\n 25.596948323286135\n ],\n [\n -80.2716064453125,\n 25.54244147012483\n ],\n [\n -80.3814697265625,\n 25.35891851754525\n ],\n [\n -80.70556640625,\n 25.110471486223346\n ],\n [\n -81.34277343749999,\n 24.886436490787712\n ],\n [\n -81.9854736328125,\n 24.701924833689933\n ],\n [\n -82.144775390625,\n 24.716895455859337\n ],\n [\n -82.3590087890625,\n 24.632038149596895\n ],\n [\n -82.3370361328125,\n 24.52213723599524\n ],\n [\n -82.0404052734375,\n 24.427145340082046\n ],\n [\n -81.45263671875,\n 24.48214938647425\n ],\n [\n -81.10107421874999,\n 24.577099744289427\n ],\n [\n -80.76599121093749,\n 24.716895455859337\n ],\n [\n -80.4034423828125,\n 24.946219074360084\n ],\n [\n -80.255126953125,\n 25.140311914680755\n ],\n [\n -79.991455078125,\n 25.522614647623293\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"2","edition":"333","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-05","publicationStatus":"PW","scienceBaseUri":"5a0425aee4b0dc0b45b452ef","contributors":{"authors":[{"text":"Cove, Michael V.","contributorId":176507,"corporation":false,"usgs":false,"family":"Cove","given":"Michael V.","affiliations":[],"preferred":false,"id":721030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, Beth","contributorId":91612,"corporation":false,"usgs":false,"family":"Gardner","given":"Beth","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":721031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kays, Roland","contributorId":83815,"corporation":false,"usgs":true,"family":"Kays","given":"Roland","affiliations":[],"preferred":false,"id":721032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":720628,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193832,"text":"70193832 - 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","interactions":[],"lastModifiedDate":"2017-11-08T10:51:36","indexId":"70193832","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"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","title":"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","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":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","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":70193828,"text":"70193828 - 2017 - Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act","interactions":[],"lastModifiedDate":"2017-11-10T10:01:47","indexId":"70193828","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act","docAbstract":"Assessment of a species' status is a key part of management decision making for endangered and threatened species under the U.S. Endangered Species Act. Predicting the future state of the species is an essential part of species status assessment, and projection models can play an important role in developing predictions. We built a stochastic simulation model that incorporated parametric and environmental uncertainty to predict the probable future status of the Sonoran desert tortoise in the southwestern United States and North Central Mexico. Sonoran desert tortoise was a Candidate species for listing under the Endangered Species Act, and decision makers wanted to use model predictions in their decision making process. The model accounted for future habitat loss and possible effects of climate change induced droughts to predict future population growth rates, abundances, and quasi-extinction probabilities. Our model predicts that the population will likely decline over the next few decades, but there is very low probability of quasi-extinction less than 75 years into the future. Increases in drought frequency and intensity may increase extinction risk for the species. Our model helped decision makers predict and characterize uncertainty about the future status of the species in their listing decision. We incorporated complex ecological processes (e.g., climate change effects on tortoises) in transparent and explicit ways tailored to support decision making processes related to endangered species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2017.09.004","usgsCitation":"McGowan, C.P., Allan, N., Servoss, J., Hedwall, S.J., and Wooldridge, B., 2017, Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act: Global Ecology and Conservation, v. 12, p. 119-130, https://doi.org/10.1016/j.gecco.2017.09.004.","productDescription":"12 p.","startPage":"119","endPage":"130","ipdsId":"IP-084680","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469339,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2017.09.004","text":"Publisher Index Page"},{"id":348431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.63037109375,\n 34.397844946449865\n ],\n [\n -115.42236328124999,\n 30.240086360983426\n ],\n [\n -111.73095703125,\n 31.55981453201843\n ],\n [\n -114.78515624999999,\n 34.63320791137959\n ],\n [\n -118.63037109375,\n 34.397844946449865\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425afe4b0dc0b45b452f7","contributors":{"authors":[{"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":720620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan","contributorId":187742,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","affiliations":[],"preferred":false,"id":721088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Servoss, Jeff","contributorId":200133,"corporation":false,"usgs":false,"family":"Servoss","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":721089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hedwall, Shaula J.","contributorId":82196,"corporation":false,"usgs":true,"family":"Hedwall","given":"Shaula","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wooldridge, Brian","contributorId":200134,"corporation":false,"usgs":false,"family":"Wooldridge","given":"Brian","email":"","affiliations":[],"preferred":false,"id":721091,"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 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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":787517,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220386,"text":"70220386 - 2017 - Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration","interactions":[],"lastModifiedDate":"2021-05-10T14:36:35.266187","indexId":"70220386","displayToPublicDate":"2017-11-06T09:29:26","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration","docAbstract":"Large Mississippi River (MR) diversions (peak water flow >1416 m3/s and sediment loads >165 kg/s) have been proposed as part of a suite of coastal restoration projects and are expected to rehabilitate and rebuild wetlands to alleviate the significant historic wetland loss in coastal Louisiana. These coastal wetlands are undergoing increasing eustatic sea‐level rise, land subsidence, climate change, and anthropogenic disturbances. However, the effect of MR diversions on wetland soil organic carbon (SOC) sequestration in receiving basins remains unknown. The rate of SOC sequestration or carbon burial in wetlands is one of the variables used to assess the role of wetland soils in carbon cycling and also to construct wetland carbon budgets. In this study, we examined the effects of MR water and sediment diversions on landscape‐scale SOC sequestration rates that were estimated from vertical accretion for the next 50 yr (2010–2060) under two environmental (moderate and less optimistic) scenarios. Our analyses were based on model simulations taken from the Wetland Morphology model developed for Louisiana's 2012 Coastal Master Plan. The master plan modeled a “future‐without‐action” scenario as well as eight individual MR diversion projects in two of the hydrologic basins (Barataria and Breton Sound). We examined the effects that discharge rates (peak flow) and locations of these individual diversion projects had on SOC sequestration rates. Modeling results indicate that large river diversions are capable of improving basin‐wide SOC sequestration capacity (162–222 g C·m−2·yr−1) by up to 14% (30 g C·m−2·yr−1) in Louisiana deltaic wetlands compared to the future‐without‐action scenario, especially under the less optimistic scenario. When large river diversions are placed in the upper receiving basin, SOC sequestration rates are 3.7–10.5% higher (6–24 g C·m−2·yr−1) than when these structures are placed in the lower receiving basin. Modeling results also indicate that both diversion discharge and location have large effects on SOC sequestration in low‐salinity (freshwater and intermediate marshes) as compared to high‐salinity marshes (brackish and saline marshes).
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1984","usgsCitation":"Wang, H., Steyer, G.D., Couvillion, B., Beck, H.J., Rybczyk, J.M., Rivera-Monroy, V.H., Krauss, K.W., and Visser, J.M., 2017, Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration: Ecosphere, v. 8, no. 11, e01984, 15 p., https://doi.org/10.1002/ecs2.1984.","productDescription":"e01984, 15 p.","ipdsId":"IP-070521","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469345,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1984","text":"Publisher Index Page"},{"id":438156,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72R3PWW","text":"USGS data release","linkHelpText":"Predicting landscape effects of Mississippi River diversions on soil organic carbon sequestration"},{"id":385545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin, Breton Sound Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.3128662109375,\n 29.480252193344267\n ],\n [\n -89.00,\n 29.480252193344267\n ],\n [\n -89.00,\n 30.285159872426014\n ],\n [\n -91.3128662109375,\n 30.285159872426014\n ],\n [\n -91.3128662109375,\n 29.480252193344267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"11","noUsgsAuthors":false,"publicationDate":"2017-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":140432,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":815331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steyer, Gregory D. 0000-0001-7231-0110 steyerg@usgs.gov","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":2856,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","email":"steyerg@usgs.gov","middleInitial":"D.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Couvillion, Brady 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":146832,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Holly J. 0000-0002-0567-9329 hbeck@usgs.gov","orcid":"https://orcid.org/0000-0002-0567-9329","contributorId":257931,"corporation":false,"usgs":true,"family":"Beck","given":"Holly","email":"hbeck@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rybczyk, John M","contributorId":257932,"corporation":false,"usgs":false,"family":"Rybczyk","given":"John","email":"","middleInitial":"M","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":815335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":815336,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":815337,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Visser, Jenneke M.","contributorId":178417,"corporation":false,"usgs":false,"family":"Visser","given":"Jenneke","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":815338,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"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
To put marine disease impacts in context requires a broad perspective on the roles infectious agents have in the ocean. Parasites infect most marine vertebrate and invertebrate species, and parasites and predators can have comparable biomass density, suggesting they play comparable parts as consumers in marine food webs. Although some parasites might increase with disturbance, most probably decline as food webs unravel. There are several ways to adapt epidemiological theory to the marine environment. In particular, because the ocean represents a three-dimensional moving habitat for hosts and parasites, models should open up the spatial scales at which infective stages and host larvae travel. In addition to open recruitment and dimensionality, marine parasites are subject to fishing, filter feeders, dosedependent infection, environmental forcing, and death-based transmission. Adding such considerations to marine disease models will make it easier to predict which infectious diseases will increase or decrease in a changing ocean.
","language":"English","publisher":"Annual Reviews","doi":"10.1146/annurev-ecolsys-121415-032147","usgsCitation":"Lafferty, K.D., 2017, Marine infectious disease ecology: Annual Review of Ecology, Evolution, and Systematics, v. 48, p. 473-496, https://doi.org/10.1146/annurev-ecolsys-121415-032147.","productDescription":"14 p.","startPage":"473","endPage":"496","ipdsId":"IP-083677","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488717,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1146/annurev-ecolsys-121415-032147","text":"Publisher Index Page"},{"id":348283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8c6e4b09af898c860df","contributors":{"authors":[{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715161,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}