{"pageNumber":"309","pageRowStart":"7700","pageSize":"25","recordCount":46706,"records":[{"id":70198301,"text":"70198301 - 2019 - Vertical zonation and niche breadth of tidal marsh plants along the Northeast Pacific coast","interactions":[],"lastModifiedDate":"2019-01-28T09:29:44","indexId":"70198301","displayToPublicDate":"2018-07-27T20:13:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Vertical zonation and niche breadth of tidal marsh plants along the Northeast Pacific coast","docAbstract":"<p>The distribution patterns of sessile organisms in coastal intertidal habitats typically exhibit vertical zonation, but little is known about variability in zonation among sites or species at larger spatial scales. Data on such heterogeneity could inform mechanistic understanding of factors affecting species distributions as well as efforts to assess and manage coastal species and habitat vulnerability to sea-level rise. Using data on the vertical distribution of common plant species at 12 tidal marshes across the US Pacific coast, we examined heterogeneity in patterns of zonation to test whether distributions varied by site, species, or latitude. Interspecific zonation was evident at most sites, but the vertical niches of co-occurring common species often overlapped considerably. The median elevation of most species varied across marshes, with site-specific differences in marsh elevation profiles more important than differences in latitude that reflect regional climate gradients. Some common species consistently inhabited lower or higher elevations relative to other species, but others varied among sites. Vertical niche breadth varied more than twofold among species. These results indicate that zonation varies by both site and species at the regional scale, and highlight the potential importance of local marsh elevation profiles to plant vertical distributions. Furthermore, they suggest that coastal foundation species such as marsh plants may differ in their vulnerability to sea-level rise by being restricted to specific elevation zones or by occurring in narrow vertical niches.</p>","language":"English","publisher":"Springer","doi":"10.1007/s12237-018-0420-9","usgsCitation":"Janousek, C.N., Thorne, K.M., and Takekawa, J.Y., 2019, Vertical zonation and niche breadth of tidal marsh plants along the Northeast Pacific coast: Estuaries and Coasts, v. 42, no. 1, p. 85-98, https://doi.org/10.1007/s12237-018-0420-9.","productDescription":"14 p.","startPage":"85","endPage":"98","ipdsId":"IP-097682","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":355994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific Coast","volume":"42","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-23","publicationStatus":"PW","scienceBaseUri":"5b6fc3f2e4b0f5d57878e959","contributors":{"authors":[{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":740953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":740952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":196611,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":740954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208892,"text":"70208892 - 2019 - Multi-measurement approach for establishing the base of gas hydrate occurrence in the Krishna-Godavari Basin for sites cored during Expedition NGHP-02 in the offshore of India","interactions":[],"lastModifiedDate":"2020-03-04T15:05:41","indexId":"70208892","displayToPublicDate":"2018-07-26T14:57:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Multi-measurement approach for establishing the base of gas hydrate occurrence in the Krishna-Godavari Basin for sites cored during Expedition NGHP-02 in the offshore of India","docAbstract":"<p><span>The 2015 National&nbsp;Gas Hydrate&nbsp;Program of India's second expedition, NGHP-02, acquired logging and coring datasets for constraining the base of the gas hydrate occurrence zone (deepest GH) and the theoretical base of gas hydrate stability zone (BGHS). These data are used here for two primary goals: to constrain the deepest occurrence of gas hydrate relative to predicted stability limits and the observed BSR, and to characterize the nature of the contact between gas hydrate-bearing sediment and the underlying gas hydrate-free sediment. A consensus depth for the deepest GH is derived for each NGHP-02 coring site from downhole indicators of gas hydrate occurrence obtained from well-log electrical resistivity and&nbsp;acoustic data, pressure core compressional&nbsp;</span>wave velocity<span>&nbsp;measurements, and conventional core measurements of anomalously low temperatures. To establish the theoretical BGHS, models of gas hydrate phase stability with depth are compared with downhole temperature profiles derived from: 1) assuming a constant&nbsp;geothermal gradient&nbsp;consistent with downhole temperature measurements, and 2) assuming constant heat flow using a geotherm through the downhole temperature measurements and incorporating&nbsp;thermal conductivity&nbsp;calculated from&nbsp;borehole logging&nbsp;data. Although the deepest NGHP-02 GH occurrences are controlled at several sites by a lithologic boundary, most sites have deepest GH occurrences within a single coarse-grained&nbsp;lithology. Cutoffs within a single coarse-grained lithology, which occur for the primary NGHP-02 Area B gas hydrate reservoir, will inhibit&nbsp;pore-pressure&nbsp;drawdowns used to extract methane from gas hydrate as an energy resource.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2018.07.026","usgsCitation":"Waite, W., Ruppel, C.D., Collett, T.S., Schultheiss, P., Holland, M., Shukla, K., and Kumar, P., 2019, Multi-measurement approach for establishing the base of gas hydrate occurrence in the Krishna-Godavari Basin for sites cored during Expedition NGHP-02 in the offshore of India: Marine and Petroleum Geology, v. 108, p. 296-320, https://doi.org/10.1016/j.marpetgeo.2018.07.026.","productDescription":"25 p.","startPage":"296","endPage":"320","ipdsId":"IP-097865","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1703491","text":"Publisher Index Page"},{"id":372916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Bay of Bengal","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              89.12109375,\n              20.24158281954221\n            ],\n            [\n              86.63818359375,\n              21.657428197370653\n            ],\n            [\n              81.80419921875,\n              17.035777250427195\n            ],\n            [\n              82.3974609375,\n              15.644196600866072\n            ],\n            [\n              89.12109375,\n              20.24158281954221\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"108","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":783891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":783893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schultheiss, P.","contributorId":79657,"corporation":false,"usgs":true,"family":"Schultheiss","given":"P.","affiliations":[],"preferred":false,"id":783894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holland, M.","contributorId":17380,"corporation":false,"usgs":true,"family":"Holland","given":"M.","email":"","affiliations":[],"preferred":false,"id":783895,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shukla, K.M.","contributorId":168911,"corporation":false,"usgs":false,"family":"Shukla","given":"K.M.","email":"","affiliations":[{"id":25388,"text":"ONGC","active":true,"usgs":false}],"preferred":false,"id":783896,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, P.","contributorId":45476,"corporation":false,"usgs":true,"family":"Kumar","given":"P.","affiliations":[],"preferred":false,"id":783897,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70206134,"text":"70206134 - 2019 - Accounting for location uncertainty in azimuthaltelemetry data improves ecological inference","interactions":[],"lastModifiedDate":"2019-10-23T15:52:00","indexId":"70206134","displayToPublicDate":"2018-07-25T15:46:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for location uncertainty in azimuthaltelemetry data improves ecological inference","docAbstract":"<h3 class=\"c-article__sub-heading u-h3\" data-test=\"abstract-sub-heading\">Background</h3><p>Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models.</p><p>We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference.</p><h3 class=\"c-article__sub-heading u-h3\" data-test=\"abstract-sub-heading\">Results</h3><p>We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation.</p><h3 class=\"c-article__sub-heading u-h3\" data-test=\"abstract-sub-heading\">Conclusion</h3><p>The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.</p>","language":"English","publisher":"Springer","doi":"10.1186/s40462-018-0129-1","collaboration":"Colorado State University","usgsCitation":"Hooten, M., Brian D. Gerber, Christopher P. Peck, Mindy B. Rice, Anthony D. Apa, Gammonley, J.H., and Amy J. Davis, 2019, Accounting for location uncertainty in azimuthaltelemetry data improves ecological inference: Movement Ecology, v. 6, 14, https://doi.org/10.1186/s40462-018-0129-1.","productDescription":"14","ipdsId":"IP-086823","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468116,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-018-0129-1","text":"Publisher Index Page"},{"id":368535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":773684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brian D. Gerber","contributorId":219968,"corporation":false,"usgs":false,"family":"Brian D. Gerber","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":773685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christopher P. Peck","contributorId":219969,"corporation":false,"usgs":false,"family":"Christopher P. Peck","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":773686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mindy B. Rice","contributorId":219970,"corporation":false,"usgs":false,"family":"Mindy B. Rice","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":773687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anthony D. Apa","contributorId":219971,"corporation":false,"usgs":false,"family":"Anthony D. Apa","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":773688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gammonley, James H.","contributorId":219972,"corporation":false,"usgs":false,"family":"Gammonley","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":773689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amy J. Davis","contributorId":219973,"corporation":false,"usgs":false,"family":"Amy J. Davis","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":773690,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70216091,"text":"70216091 - 2019 - Macroinvertebrate sensitivity thresholds for sediment in Virginia streams","interactions":[],"lastModifiedDate":"2020-11-05T15:09:49.728379","indexId":"70216091","displayToPublicDate":"2018-07-19T09:03:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Macroinvertebrate sensitivity thresholds for sediment in Virginia streams","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Sediment is the most commonly identified pollutant associated with macroinvertebrate community impairments in freshwater streams nationwide. Management of this physical stressor is complicated by the multiple measures of sediment available (e.g., suspended, dissolved, bedded) and the variability in natural “healthy” sediment loadings across ecoregions. Here we examine the relative importance of 9 sediment parameters on macroinvertebrate community health as measured by the Virginia Stream Condition Index (VSCI) across 5 ecoregions. In combination, sediment parameters explained 27.4% of variance in the VSCI in a multiregion data set and from 20.2% to 76.4% of variance for individual ecoregions. Bedded sediment parameters had a stronger influence on VSCI than did dissolved or suspended parameters in the multiregion assessment. However, assessments of individual ecoregions revealed conductivity had a key influence on VSCI in the Central Appalachian, Northern Piedmont and Piedmont ecoregions. In no case was a single sediment parameter sufficient to predict VSCI scores or individual biological metrics. Given the identification of embeddedness and conductivity as key parameters for predicting biological condition, we developed family‐level sensitivity thresholds for these parameters, based on extirpation. Resulting thresholds for embeddedness were 68% for combined ecoregions, 65% for the Mountain bioregion (composed of Central Appalachian, Ridge and Valley, and Blue Ridge ecoregions), and 88% for the Piedmont bioregion (composed of Northern Piedmont and Piedmont ecoregions). Thresholds for conductivity were 366 μS/cm for combined ecoregions, 391 μS/cm for the Mountain bioregion, and 136 μS/cm for the Piedmont bioregion. These thresholds may help water quality professionals identify impaired and at‐risk waters designated to support aquatic life and develop regional strategies to manage sediment‐impaired streams. Inclusion of embeddedness as a restoration endpoint may be warranted; this could be facilitated by application of more quantitative, less time‐intensive measurement approaches. We encourage refinement of thresholds as additional data and genus‐based metrics become available.<span>&nbsp;</span><i>Integr Environ Assess Manag</i><span>&nbsp;</span>2019;15:77–92. Published 2018. This article has been contributed to by US Government employees and their work is in the public domain in the USA.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ieam.4086","usgsCitation":"Govenor, H., Krometis, L.A., Willis, L., Angermeier, P.L., and Hession, W.C., 2019, Macroinvertebrate sensitivity thresholds for sediment in Virginia streams: Integrated Environmental Assessment and Management, v. 15, no. 1, p. 77-92, https://doi.org/10.1002/ieam.4086.","productDescription":"16 p.","startPage":"77","endPage":"92","ipdsId":"IP-094435","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":460597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.4086","text":"Publisher Index Page"},{"id":380193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Govenor, Heather","contributorId":244545,"corporation":false,"usgs":false,"family":"Govenor","given":"Heather","email":"","affiliations":[],"preferred":false,"id":804144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krometis, Leigh Anne H.","contributorId":244546,"corporation":false,"usgs":false,"family":"Krometis","given":"Leigh","email":"","middleInitial":"Anne H.","affiliations":[],"preferred":false,"id":804145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willis, Lawrence","contributorId":244547,"corporation":false,"usgs":false,"family":"Willis","given":"Lawrence","email":"","affiliations":[],"preferred":false,"id":804146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":804147,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hession, W. Cully","contributorId":244548,"corporation":false,"usgs":false,"family":"Hession","given":"W.","email":"","middleInitial":"Cully","affiliations":[],"preferred":false,"id":804148,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70203659,"text":"70203659 - 2019 - Optimal treatment allocations in space and time for online control of anemerging infectious disease","interactions":[],"lastModifiedDate":"2019-05-30T15:07:49","indexId":"70203659","displayToPublicDate":"2018-07-18T15:02:16","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2568,"text":"Journal of the Royal Statistical Society. Series C: Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Optimal treatment allocations in space and time for online control of anemerging infectious disease","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up‐to‐date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at outbreak. We derive a Bayesian on‐line estimator of the optimal allocation strategy that combines simulation–optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/rssc.12266","usgsCitation":"Laber, E.B., Meyer, N.J., Reich, B.J., Pacifici, K., Collazo, J., and Drake, J.M., 2019, Optimal treatment allocations in space and time for online control of anemerging infectious disease: Journal of the Royal Statistical Society. Series C: Applied Statistics, v. 67, no. 4, p. 743-789, https://doi.org/10.1111/rssc.12266.","productDescription":"45 p.","startPage":"743","endPage":"789","ipdsId":"IP-071304","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468117,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rssc.12266","text":"Publisher Index Page"},{"id":364258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Laber, Eric B.","contributorId":215934,"corporation":false,"usgs":false,"family":"Laber","given":"Eric","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":763448,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Nick J.","contributorId":215935,"corporation":false,"usgs":false,"family":"Meyer","given":"Nick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":763449,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reich, Brian J.","contributorId":150871,"corporation":false,"usgs":false,"family":"Reich","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":763450,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pacifici, Krishna","contributorId":26564,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":763451,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":763444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drake, John M.","contributorId":175372,"corporation":false,"usgs":false,"family":"Drake","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":27562,"text":"Odum School of Ecology, University of Georgia, Athens, GA","active":true,"usgs":false}],"preferred":false,"id":763452,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206810,"text":"70206810 - 2019 - Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition","interactions":[],"lastModifiedDate":"2019-11-22T10:25:23","indexId":"70206810","displayToPublicDate":"2018-07-16T10:23:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition","docAbstract":"1. Although interspecific competition plays a principle role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence.\n2. We conducted a multi-landscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. \n3. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over three years in ten study sites covering 5,070 km2. We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. \n4. Results showed that both lion and leopard occupancy was independent of – rather than conditional on – their competitor’s presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, ‘hideable’ habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores and there was no significant relationship between species densities. \n4. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore suggested to achieve cost- and space-effective conservation.","language":"English","publisher":"Wiley","doi":"10.1111/1365-2656.12883","usgsCitation":"Fuller, A.K., Miller, J., Pittman, R., Mann, G., and Balme, G., 2019, Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition: Journal of Animal Ecology, v. 87, no. 6, p. 1709-1726, https://doi.org/10.1111/1365-2656.12883.","productDescription":"18 p.","startPage":"1709","endPage":"1726","ipdsId":"IP-089044","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12883","text":"Publisher Index Page"},{"id":369459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":775837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Jennifer","contributorId":197539,"corporation":false,"usgs":false,"family":"Miller","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":775838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pittman, Ross","contributorId":220824,"corporation":false,"usgs":false,"family":"Pittman","given":"Ross","email":"","affiliations":[{"id":40281,"text":"Institute for Communities and Wildlife in Africa","active":true,"usgs":false}],"preferred":false,"id":775839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mann, Gareth","contributorId":220825,"corporation":false,"usgs":false,"family":"Mann","given":"Gareth","email":"","affiliations":[{"id":40281,"text":"Institute for Communities and Wildlife in Africa","active":true,"usgs":false}],"preferred":false,"id":775840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Balme, Guy","contributorId":220826,"corporation":false,"usgs":false,"family":"Balme","given":"Guy","email":"","affiliations":[{"id":40281,"text":"Institute for Communities and Wildlife in Africa","active":true,"usgs":false}],"preferred":false,"id":775841,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70204422,"text":"70204422 - 2019 - Importance of riparian forest corridors for the ocelot in agricultural landscapes","interactions":[],"lastModifiedDate":"2019-07-23T08:51:11","indexId":"70204422","displayToPublicDate":"2018-07-09T08:50:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Importance of riparian forest corridors for the ocelot in agricultural landscapes","docAbstract":"Worldwide, private lands have attracted increased attention from conservationists, not only because most of the globe is privately owned, but also because private lands can be an asset to the protected area conservation strategy. In Brazil, the riverine Areas of Permanent Protection (APPs) is a key instrument of the Forest Code to protect native vegetation on private lands. Although APPs were conceived to function as potential wildlife corridors, this putative role has been rarely assessed. Further, recent debatable changes in the Forest Code have decreased the extent of APPs. Given this lack of information and the declining area being protected along with the growing demand for agricultural and biofuel production, which stimulate additional deforestation, it is timely to assess the role of APPs in wildlife conservation. We did this analyzing how ocelot (Leopardus pardalis) occurrence is influenced by covariates in 3 cerrado landscapes dominated by sugarcane and managed forests of eucalyptus and pine trees. We collected detection/non-detection data by camera trapping during 2 dry seasons (2013 and 2014) at 208 stations (6606 camera-days). We estimated ocelot detection and mean relative abundance using a single-species/single-season occupancy model that accounts for heterogeneous detection probability induced by variation in abundance. Modeling results showed that percentage of native forest was the most important covariate to explain ocelot mean relative abundance. This parameter was also affected positively by APPs, with ocelots being more abundant inside than outside APPs and, unexpectedly, by sugarcane. Given study design, however, the positive effect of this crop likely reflects the contact zone between sugarcane and native forest. Our findings show that landscape composition affects ocelot abundance and highlight the importance of APPs and Legal Reserves in agricultural landscapes. We conclude that, in such landscapes, compliance to the Forest Code by private land owners is key to supporting ocelot occurrence.","language":"English","publisher":"Oxford Academic ","doi":"10.1093/jmammal/gyy075","usgsCitation":"Paolino, R., Royle, A., Versiani, N., Rodrigues, T.F., Pasqualotto, N., Krepschi, V., and Adriano Chiarello, 2019, Importance of riparian forest corridors for the ocelot in agricultural landscapes: Journal of Mammalogy, v. 99, no. 4, p. 874-884, https://doi.org/10.1093/jmammal/gyy075.","productDescription":"11 p.","startPage":"874","endPage":"884","ipdsId":"IP-092001","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyy075","text":"Publisher Index Page"},{"id":365833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365832,"type":{"id":15,"text":"Index Page"},"url":"https://academic.oup.com/jmammal/article-abstract/99/4/874/5050937"}],"volume":"99","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Paolino, Roberta","contributorId":217469,"corporation":false,"usgs":false,"family":"Paolino","given":"Roberta","email":"","affiliations":[{"id":38961,"text":"Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":766836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":766835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Versiani, Natalia","contributorId":217470,"corporation":false,"usgs":false,"family":"Versiani","given":"Natalia","email":"","affiliations":[{"id":38961,"text":"Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":766837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodrigues, Thiago F.","contributorId":217471,"corporation":false,"usgs":false,"family":"Rodrigues","given":"Thiago","email":"","middleInitial":"F.","affiliations":[{"id":39648,"text":"Universidade Estadual Paulista (UNESP)","active":true,"usgs":false}],"preferred":false,"id":766838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pasqualotto, Nielson","contributorId":217472,"corporation":false,"usgs":false,"family":"Pasqualotto","given":"Nielson","email":"","affiliations":[{"id":38961,"text":"Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":766839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krepschi, Victor","contributorId":217473,"corporation":false,"usgs":false,"family":"Krepschi","given":"Victor","email":"","affiliations":[{"id":38961,"text":"Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":766840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adriano Chiarello","contributorId":217474,"corporation":false,"usgs":false,"family":"Adriano Chiarello","affiliations":[{"id":38961,"text":"Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":766841,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70197990,"text":"70197990 - 2019 - Modeling framework to estimate spawning and hatching locations of pelagically-spawned eggs","interactions":[],"lastModifiedDate":"2019-03-26T16:24:40","indexId":"70197990","displayToPublicDate":"2018-07-05T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Modeling framework to estimate spawning and hatching locations of pelagically-spawned eggs","docAbstract":"<p><span>Identifying spawning and hatching locations is vital to controlling invasive fish and conserving imperiled fish, which can be difficult for pelagically-spawning species with semi-buoyant eggs. In freshwater systems, this reproductive strategy is common among cyprinid species, such as Chinese carp species currently threatening the Great Lakes. Following the confirmation that one of these species, Grass Carp (Ctenopharyngodon idella), was spawning in a Great Lakes tributary, we developed a modeling framework to combine field data with hydraulic models to calculate the most probable spawning and hatching locations for collected eggs. Our results indicate that the estimated spawning location encompassed habitat consistent with spawning sites in Grass Carp’s native range. Additionally, all eggs were identified to have hatched in the river, increasing the likelihood of successful recruitment. This modeling framework can be used to estimate spawning and hatching locations for Chinese carp species, as well as all pelagic, riverine spawners. Spawning and hatching locations provide key information to researchers about the reproductive requirements of species and to agencies about how best to manage populations for control or restoration.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0047","usgsCitation":"Embke, H.S., Kocovsky, P., Garcia, T., Mayer, C.M., and Qian, S.S., 2019, Modeling framework to estimate spawning and hatching locations of pelagically-spawned eggs: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 4, p. 597-607, https://doi.org/10.1139/cjfas-2018-0047.","productDescription":"11 p.","startPage":"597","endPage":"607","ipdsId":"IP-087557","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":501079,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/92211","text":"External Repository"},{"id":355500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d077","contributors":{"authors":[{"text":"Embke, Holly S. 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":173026,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"S.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":false,"id":739500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":739499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Tatiana 0000-0002-1979-7246 tgarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-1979-7246","contributorId":140327,"corporation":false,"usgs":true,"family":"Garcia","given":"Tatiana","email":"tgarcia@usgs.gov","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, Christine M.","contributorId":50814,"corporation":false,"usgs":true,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":739502,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Qian, Song S.","contributorId":198934,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":739503,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70197975,"text":"70197975 - 2019 - Diets of endangered silver chub (Macrhybopsis storeriana, Kirtland, 1844) in Lake Erie and implications for recovery","interactions":[],"lastModifiedDate":"2019-01-28T09:31:23","indexId":"70197975","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Diets of endangered silver chub (<i>Macrhybopsis storeriana</i>, Kirtland, 1844) in Lake Erie and implications for recovery","title":"Diets of endangered silver chub (Macrhybopsis storeriana, Kirtland, 1844) in Lake Erie and implications for recovery","docAbstract":"<p><span>Silver chub (</span><i>Macrhybopsis storeriana</i><span>, Kirtland, 1844) is a native Cyprinid in Lake Erie, one of the Laurentian Great Lakes of North America. It is listed as endangered by the US state of New York and Canada, which has a recovery plan, and as special concern by the state of Michigan. Silver chub faces a potential threat to recovery from control efforts for invasive Grass carp (</span><i>Ctenopharyngodon idella</i><span>, Valenciennes 1844). Among the knowledge gaps for protection and restoration is current diet data. I describe the diet of silver chub from western Lake Erie in 2014, and I compare it to past studies to assess changes in diet through time. Silver chub captured in bottom trawls May–September 2014 were frozen in the field, and stomach contents were preserved in ethanol. Diet taxa were identified to the lowest practical taxonomic unit, then dried and weighed. Frequency of occurrence in silver chub diets was highest for<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. mayflies (79%).<span>&nbsp;</span></span><i>Dreissena</i><span><span>&nbsp;</span>spp. and<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. were both 41% of the diet by dry weight. Analysis of δ</span><sup>13</sup><span>C isotopes identified<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. as the primary source of carbon in silver chub. Compared to past studies,<span>&nbsp;</span></span><i>Dreissena</i><span><span>&nbsp;</span>spp. have mostly replaced Sphaeriidae and Gastropoda in silver chub diets. There also have been seasonal shifts in relative amounts of shelled organisms and<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. This study and past research suggest a functional link between silver chub and<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. abundance. Maintenance and recovery of silver chub may be dependent on maintaining<span>&nbsp;</span></span><i>Hexagenia</i><span><span>&nbsp;</span>spp. populations.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/eff.12424","usgsCitation":"Kocovsky, P., 2019, Diets of endangered silver chub (Macrhybopsis storeriana, Kirtland, 1844) in Lake Erie and implications for recovery: Ecology of Freshwater Fish, v. 28, no. 1, p. 33-40, https://doi.org/10.1111/eff.12424.","productDescription":"8 p.","startPage":"33","endPage":"40","ipdsId":"IP-096199","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":460599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12424","text":"Publisher Index Page"},{"id":355441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Erie","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -83.66638183593749,\n              41.35825713137813\n            ],\n            [\n              -81.97174072265625,\n              41.35825713137813\n            ],\n            [\n              -81.97174072265625,\n              42.20817645934742\n            ],\n            [\n              -83.66638183593749,\n              42.20817645934742\n            ],\n            [\n              -83.66638183593749,\n              41.35825713137813\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e546e4b060350a15d08b","contributors":{"authors":[{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":739415,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70223490,"text":"70223490 - 2019 - Let’s agree to disagree: Comparing auto-acoustic identification programs for northeastern bats","interactions":[],"lastModifiedDate":"2021-08-30T13:11:53.375779","indexId":"70223490","displayToPublicDate":"2018-07-01T08:09:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Let’s agree to disagree: Comparing auto-acoustic identification programs for northeastern bats","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>With the declines in abundance and changing distribution of white-nose syndrome–affected bat species, increased reliance on acoustic monitoring is now the new “normal.” As such, the ability to accurately identify individual bat species with acoustic identification programs has become increasingly important. We assessed rates of disagreement between the three U.S. Fish and Wildlife Service–approved acoustic identification software programs (Kaleidoscope Pro 4.2.0, Echoclass 3.1, and Bat Call Identification 2.7d) and manual visual identification using acoustic data collected during summers from 2003 to 2017 at Fort Drum, New York. We assessed the percentage of agreement between programs through pairwise comparisons on a total nightly count level, individual file level (e.g., individual echolocation pass call file), and grouped maximum likelihood estimate level (e.g., probability values that a species is misclassified as present when in fact it is absent) using preplanned contrasts, Akaike Information Criterion, and annual confusion matrices. Interprogram agreement on an individual file level was low, as measured by Cohen's Kappa (0.2–0.6). However, site-night level pairwise comparative analysis indicated that program agreement was higher (40–90%) using single season occupancy metrics. In comparing analytical outcomes of our different datasets (i.e., how comparable programs and visual identification are regarding the relationship between environmental conditions and bat activity), we determined high levels of congruency in the relative rankings of the model as well as the relative level of support for each individual model. This indicated that among individual software packages, when analyzing bat calls, there was consistent ecological inference beyond the file-by-file level at the scales used by managers. Depending on objectives, we believe our results can help users choose automated software and maximum likelihood estimate thresholds more appropriate for their needs and allow for better cross-comparison of studies using different automated acoustic software.</p></div>","language":"English","publisher":"Allen Press","doi":"10.3996/102018-JFWM-090","usgsCitation":"Ford, W., Nocera, T., Silvis, A., and Dobony, C., 2019, Let’s agree to disagree: Comparing auto-acoustic identification programs for northeastern bats: Journal of Fish and Wildlife Management, v. 10, no. 2, p. 346-361, https://doi.org/10.3996/102018-JFWM-090.","productDescription":"16 p.","startPage":"346","endPage":"361","ipdsId":"IP-101166","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/102018-jfwm-090","text":"Publisher Index Page"},{"id":388654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":822153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nocera, Tomás","contributorId":264895,"corporation":false,"usgs":false,"family":"Nocera","given":"Tomás","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":822154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":264896,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":54472,"text":"RES Inc.","active":true,"usgs":false}],"preferred":false,"id":822155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobony, Christopher A.","contributorId":264897,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher A.","affiliations":[{"id":54576,"text":"DoD","active":true,"usgs":false}],"preferred":false,"id":822156,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70197422,"text":"70197422 - 2019 - Climatic sensitivity of dryland soil CO2 fluxes differs dramatically with biological soil crust successional state","interactions":[],"lastModifiedDate":"2019-02-21T14:58:45","indexId":"70197422","displayToPublicDate":"2018-06-04T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Climatic sensitivity of dryland soil CO<sub>2</sub> fluxes differs dramatically with biological soil crust successional state","title":"Climatic sensitivity of dryland soil CO2 fluxes differs dramatically with biological soil crust successional state","docAbstract":"<p><span>Arid and semiarid ecosystems make up approximately 41% of Earth’s terrestrial surface and are suggested to regulate the trend and interannual variability of the global terrestrial carbon (C) sink. Biological soil crusts (biocrusts) are common dryland soil surface communities of bryophytes, lichens, and/or cyanobacteria that bind the soil surface together and that may play an important role in regulating the climatic sensitivity of the dryland C cycle. Major uncertainties exist in our understanding of the interacting effects of changing temperature and moisture on CO</span><sub>2</sub><span><span>&nbsp;</span>uptake (photosynthesis) and loss (respiration) from biocrust and sub-crust soil, particularly as related to biocrust successional state. Here, we used a mesocosm approach to assess how biocrust successional states related to climate treatments. We subjected bare soil (Bare), early successional lightly pigmented cyanobacterial biocrust (Early), and late successional darkly pigmented moss-lichen biocrust (Late) to either ambient or + 5°C above ambient soil temperature for 84&nbsp;days. Under ambient temperatures, Late biocrust mesocosms showed frequent net uptake of CO</span><sub>2</sub><span>, whereas Bare soil, Early biocrust, and warmed Late biocrust mesocosms mostly lost CO</span><sub>2</sub><span><span>&nbsp;</span>to the atmosphere. The inhibiting effect of warming on CO</span><sub>2</sub><span><span>&nbsp;</span>exchange was a result of accelerated drying of biocrust and soil. We used these data to parameterize, via Bayesian methods, a model of ecosystem CO</span><sub>2</sub><span><span>&nbsp;</span>fluxes, and evaluated the model with data from an autochamber CO</span><sub>2</sub><span><span>&nbsp;</span>system at our field site on the Colorado Plateau in SE Utah. In the context of the field experiment, the data underscore the negative effect of warming on fluxes both biocrust CO</span><sub>2</sub><span><span>&nbsp;</span>uptake and loss—which, because biocrusts are a dominant land cover type in this ecosystem, may extend to ecosystem-scale C cycling.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0250-4","usgsCitation":"Tucker, C., Ferrenberg, S., and Reed, S.C., 2019, Climatic sensitivity of dryland soil CO2 fluxes differs dramatically with biological soil crust successional state: Ecosystems, v. 22, no. 1, p. 15-32, https://doi.org/10.1007/s10021-018-0250-4.","productDescription":"18 p.","startPage":"15","endPage":"32","ipdsId":"IP-083628","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":354689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-30","publicationStatus":"PW","scienceBaseUri":"5b46e575e4b060350a15d191","contributors":{"authors":[{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":737103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrenberg, Scott 0000-0002-3542-0334 sferrenberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3542-0334","contributorId":147684,"corporation":false,"usgs":true,"family":"Ferrenberg","given":"Scott","email":"sferrenberg@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":737104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":737105,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70197333,"text":"70197333 - 2019 - Gas and ash emissions associated with the 2010–present activity of Sinabung Volcano, Indonesia","interactions":[],"lastModifiedDate":"2019-12-21T09:06:03","indexId":"70197333","displayToPublicDate":"2018-05-30T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Gas and ash emissions associated with the 2010–present activity of Sinabung Volcano, Indonesia","docAbstract":"Sinabung Volcano (Sumatra, Indonesia) awoke from over 1200 years of dormancy with multiple phreatic explosions in 2010. After a period of quiescence, Sinabung activity resumed in 2013, producing frequent explosions, lava dome extrusion, and pyroclastic flows from dome collapses, becoming one of the world's most active volcanoes and displacing over 20,000 citizens. This study presents a compilation of the geochemical datasets collected by the Indonesian Center for Volcanology and Geological Hazard Mitigation (CVGHM) from 2010 - current (2016), which provides insights into the evolution of the eruption. Based on observations of SO2 emissions, ash componentry, leachate chemistry, and bulk ash geochemistry, the eruption can be split into five distinct phases. The initial stage of phreatic summit explosions occurred from August - October 2010, during which background SO2 emissions averaged ~550 ± 180 t/d (1 s.d.). An eruptive pause (phase two) starting in October 2010 abruptly ended in September 2013 with a resumption of conduit-clearing eruptions. This third phase had a relatively modest background SO2 emission rate (avg. ~410 ± 275 t/d) and produced ash consisting entirely of accidental ejecta with high S/Cl leachate ratios (up to 30), suggestive of deep-sourced magma and the incorporation of hydrothermal sulfur-bearing phases. The most intense phase of the eruption (phase four) occurred from December 2013 to February 2014, when juvenile magma first reached the surface. This period included dozens of large eruptions per day, high SO2 emission rates (average: 1,120 ± 1,030 t/d, peak: ~3,800 t/d), the onset of lava dome extrusion, and a dramatic drop in S/Cl ash leachates to ratios < 5, all reflecting increased degassing from shallow magma and the clearing out of sulfurous phases from the old hydrothermal system. From late February 2014 through the time of writing (September 2016), Sinabung settled into a relatively steady state of lower activity (phase five). Ash emissions now consist of dominantly juvenile material, and background SO2 emission rates have been progressively decreasing to an average of ~250 - 300 t/d. Starting August 2016, SO2 emissions started being measured in a continuous manner using a network of permanent scanning DOAS instruments. We find that long-term SO2 emission rates have been gradually declining at Sinabung since early 2014, consistent with an apparent decrease in magma supply. Our degassing model suggests that large explosions and pyroclastic flows could continue in the near-term owing to conduit plugging and dome collapses, remaining a major threat until the magma supply rate decreases further and the eruption ends.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.11.018","usgsCitation":"Primulyana, S., Kern, C., Lerner, A., Saing, U., Kunrat, S., Alfianti, H., and Marlia, M., 2019, Gas and ash emissions associated with the 2010–present activity of Sinabung Volcano, Indonesia: Journal of Volcanology and Geothermal Research, v. 382, p. 184-196, https://doi.org/10.1016/j.jvolgeores.2017.11.018.","productDescription":"13 p.","startPage":"184","endPage":"196","ipdsId":"IP-080511","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.11.018","text":"Publisher Index Page"},{"id":354587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","state":"Sumatra","otherGeospatial":"Mount Sinabung","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              94.04296874999999,\n              6.140554782450308\n            ],\n            [\n              97.03125,\n              -1.0546279422758742\n            ],\n            [\n              101.7333984375,\n              -1.0546279422758742\n            ],\n            [\n              101.0302734375,\n              4.565473550710278\n            ],\n            [\n              97.03125,\n              7.885147283424331\n            ],\n            [\n              94.04296874999999,\n              6.140554782450308\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"382","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155df3e4b092d9651e1b92","contributors":{"authors":[{"text":"Primulyana, Sofyan","contributorId":194978,"corporation":false,"usgs":false,"family":"Primulyana","given":"Sofyan","email":"","affiliations":[],"preferred":false,"id":736704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":736703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lerner, Allan","contributorId":205264,"corporation":false,"usgs":false,"family":"Lerner","given":"Allan","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":736705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saing, Ugan","contributorId":205265,"corporation":false,"usgs":false,"family":"Saing","given":"Ugan","email":"","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":736706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kunrat, Syegi","contributorId":205266,"corporation":false,"usgs":false,"family":"Kunrat","given":"Syegi","email":"","affiliations":[{"id":37069,"text":"CVGHM, Portland State University","active":true,"usgs":false}],"preferred":false,"id":736707,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alfianti, Hilma","contributorId":205267,"corporation":false,"usgs":false,"family":"Alfianti","given":"Hilma","email":"","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":736708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marlia, Mitha","contributorId":205268,"corporation":false,"usgs":false,"family":"Marlia","given":"Mitha","email":"","affiliations":[{"id":37068,"text":"CVGHM","active":true,"usgs":false}],"preferred":false,"id":736709,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70217275,"text":"70217275 - 2019 - Slope failure and mass transport processes along the Queen Charlotte Fault Zone, western British Columbia","interactions":[],"lastModifiedDate":"2021-01-14T18:47:10.564976","indexId":"70217275","displayToPublicDate":"2018-05-24T12:37:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1791,"text":"Geological Society, London, Special Publications","active":true,"publicationSubtype":{"id":10}},"title":"Slope failure and mass transport processes along the Queen Charlotte Fault Zone, western British Columbia","docAbstract":"<p><span>Multibeam echosounder (MBES) images, 3.5 kHz seismic-reflection profiles and piston cores obtained along the southern Queen Charlotte Fault Zone are used to map and date mass-wasting events at this transform margin – a seismically active boundary that separates the Pacific Plate from the North American Plate. Whereas the upper continental slope adjacent to and east (upslope) of the fault zone offshore of the Haida Gwaii is heavily gullied, few large-sized submarine landslides in this area are observed in the MBES images. However, smaller submarine seafloor slides exist locally in areas where fluid flow appears to be occurring and large seafloor slides have recently been detected at the base of the steep continental slope just above its contact with the abyssal plain on the Queen Charlotte Terrace. In addition, along the subtle slope re-entrant area offshore of the Dixon Entrance shelf bathymetric data suggest that extensive mass wasting has occurred in the vicinity of an active mud volcano venting gas. We surmise that the relative lack of submarine slides along the upper slope in close proximity to the Queen Charlotte Fault Zone may be the result of seismic strengthening (compaction and cohesion) of a sediment-starved shelf and slope through multiple seismic events.</span></p>","language":"English","publisher":"Geological Society of London","doi":"10.1144/SP477.31","usgsCitation":"Greene, H.G., Barrie, J., Brothers, D.S., Conrad, J.E., Conway, K., East, A.E., Enkin, R.J., Maier, K.L., Walton, M.A., and Rohr, K..., 2019, Slope failure and mass transport processes along the Queen Charlotte Fault Zone, western British Columbia: Geological Society, London, Special Publications, v. 477, p. 85-106, https://doi.org/10.1144/SP477.31.","productDescription":"22 p.","startPage":"85","endPage":"106","ipdsId":"IP-093171","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":382178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"British Columbia","otherGeospatial":"Queen Charlotte Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -128.3642578125,\n              50.28933925329178\n            ],\n            [\n              -133.0224609375,\n              56.41390137600676\n            ],\n            [\n              -136.58203125,\n              59.085738569819505\n            ],\n            [\n              -142.734375,\n              56.992882804633986\n            ],\n            [\n              -135.1318359375,\n              46.37725420510028\n            ],\n            [\n              -128.3642578125,\n              50.28933925329178\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"477","noUsgsAuthors":false,"publicationDate":"2018-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Greene, H. G.","contributorId":116109,"corporation":false,"usgs":true,"family":"Greene","given":"H.","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":808231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barrie, J. Vaughn","contributorId":242728,"corporation":false,"usgs":false,"family":"Barrie","given":"J. Vaughn","affiliations":[{"id":48497,"text":"2Geological Survey of Canada (Pacific,)","active":true,"usgs":false}],"preferred":false,"id":808232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":808233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":808234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conway, Kim","contributorId":242731,"corporation":false,"usgs":false,"family":"Conway","given":"Kim","email":"","affiliations":[{"id":48501,"text":"Geological Survey of Canada (Pacific)","active":true,"usgs":false}],"preferred":false,"id":808235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":808236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Enkin, Randolph J.","contributorId":75373,"corporation":false,"usgs":true,"family":"Enkin","given":"Randolph","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":808237,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maier, Katherine L. 0000-0003-2908-3340 kcoble@usgs.gov","orcid":"https://orcid.org/0000-0003-2908-3340","contributorId":4926,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine","email":"kcoble@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":808238,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walton, Maureen A. L. 0000-0001-8496-463X","orcid":"https://orcid.org/0000-0001-8496-463X","contributorId":211025,"corporation":false,"usgs":true,"family":"Walton","given":"Maureen","email":"","middleInitial":"A. L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":808239,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rohr, K .M. M.","contributorId":238949,"corporation":false,"usgs":false,"family":"Rohr","given":"K","email":"","middleInitial":".M. M.","affiliations":[{"id":47832,"text":"Geological Survey of Canada – Pacific, 9860 West Saanich Road, Sidney BC, V8L 4B2, Canada","active":true,"usgs":false}],"preferred":false,"id":808240,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70204878,"text":"70204878 - 2019 - Synchrony — An emergent property of recreational fisheries","interactions":[],"lastModifiedDate":"2019-08-21T15:17:07","indexId":"70204878","displayToPublicDate":"2018-05-21T15:16:19","publicationYear":"2019","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":"Synchrony — An emergent property of recreational fisheries","docAbstract":"Recreational fisheries are traditionally managed at local scales, but more effective management could be achieved using a cross‐scale approach. To do this, we must first understand how local processes scale up to influence landscape patterns between anglers and resources. We highlight how population‐based synchrony methods, used in conjunction with a complex‐adaptive‐systems framework, can reveal emergent spatial properties within social‐ecological systems such as recreational fisheries. Herein, we quantified the level of spatial synchrony in angler behaviour, defined the relationship between angler synchrony and distance among waterbodies, and highlighted social‐ecological attributes contributing to these patterns. We leveraged a 111 waterbody‐year (34 waterbodies, 5‐year collection period) recreational fisheries dataset from Nebraska, USA to address these objectives. Intra‐annual patterns in angler behaviour were moderately synchronous across large spatial scales and predominately unrelated to distance among waterbodies. Large‐scale synchronous patterns in angler behaviour emerged from local‐scale interactions between angler heterogeneity and waterbody diversity. Spatial synchrony in angler behaviour is an emergent property that resulted from local‐level processes that scaled up to form large‐scale patterns. We posit that angler utility in combination with waterbodies sharing these desired utility components caused spatial synchrony among anglers with similar preferences or specializations. The level of spatial synchrony in angler behaviour will therefore depend on the degree of angler heterogeneity and waterbody diversity on the landscape, with high or low levels of both leading to low and high levels of spatial synchrony respectively. Synthesis and applications. Synchrony‐based methods proved useful for unveiling an emergent property in recreational fisheries that is beneficial for effective cross‐scale management. It may not be appropriate to extrapolate information and apply uniform management actions among local waterbodies because angler behaviour was not synchronous at small scales. Rather, anglers respond uniquely to waterbody diversity and therefore substitute waterbodies may be dispersed throughout the landscape. Creating boat access, for example could yield unintended consequences for a particular angler group and cause local and regional shifts in angler behaviour. Evaluating appropriate management options will require a cross‐scale monitoring approach that captures angler heterogeneity and waterbody diversity at multiple scales. Recreational fisheries are traditionally managed at local scales, but more effective management could be achieved using a cross‐scale approach. To do this, we must first understand how local processes scale up to influence landscape patterns between anglers and resources. We highlight how population‐based synchrony methods, used in conjunction with a complex‐adaptive‐systems framework, can reveal emergent spatial properties within social‐ecological systems such as recreational fisheries. Herein, we quantified the level of spatial synchrony in angler behaviour, defined the relationship between angler synchrony and distance among waterbodies, and highlighted social‐ecological attributes contributing to these patterns. We leveraged a 111 waterbody‐year (34 waterbodies, 5‐year collection period) recreational fisheries dataset from Nebraska, USA to address these objectives. Intra‐annual patterns in angler behaviour were moderately synchronous across large spatial scales and predominately unrelated to distance among waterbodies. Large‐scale synchronous patterns in angler behaviour emerged from local‐scale interactions between angler heterogeneity and waterbody diversity. Spatial synchrony in angler behaviour is an emergent property that resulted from local‐level processes that scaled up to form large‐scale patterns. We posit that angler","language":"English","publisher":"Wiley","doi":"10.1111/1365-2664.13164","usgsCitation":"Pope, K.L., 2019, Synchrony — An emergent property of recreational fisheries: Journal of Applied Ecology, v. 55, no. 6, p. 2986-2996, https://doi.org/10.1111/1365-2664.13164.","productDescription":"11 p.","startPage":"2986","endPage":"2996","ipdsId":"IP-086746","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":366808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":768861,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70207403,"text":"70207403 - 2019 - Ecological and management implications of climate change induced shifts in phenology of coastal fish and wildlife species in the Northeast CASC region","interactions":[],"lastModifiedDate":"2020-07-27T19:03:00.238284","indexId":"70207403","displayToPublicDate":"2018-05-01T20:09:55","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Ecological and management implications of climate change induced shifts in phenology of coastal fish and wildlife species in the Northeast CASC region","docAbstract":"<div class=\"field field-name-body\"><p>Climate change is causing species to shift their phenology, or the timing of recurring life events such as migration and reproduction, in variable and complex ways. This can potentially result in mismatches or asynchronies in food and habitat resources that negatively impact individual fitness, population dynamics, and ecosystem function. Numerous studies have evaluated phenological shifts in terrestrial species, particularly birds and plants, yet far fewer evaluations have been conducted for marine animals. This project seeks to improve our understanding of shifts in the timing of seasonal migration, spawning or breeding, and biological development (i.e. life stages present, dominant) of coastal fishes, marine mammals,and migratory shore and seabirds along the U.S Atlantic coast. Ideally the suite of species selected will allow us to compare whether fish, marine mammals, shore and seabird predators are shifting their phenology at different rates than their primary prey and optimal habitat conditions, thus influencing trophic interactions and population dynamics. A comprehensive literature review will be conducted simultaneous to data collection and synthesis to determine what is known, and what knowledge/information/data gaps exist regarding regional phenological responses of coastal species to climate change. Project results will help managers assess the vulnerability of coastal species to climate change by providing information on how they are responding to impacts in the region.</p></div>","language":"English","publisher":"Northwest Climate Adaption Science Center","usgsCitation":"Staudinger, M., and Jordaan, A., 2019, Ecological and management implications of climate change induced shifts in phenology of coastal fish and wildlife species in the Northeast CASC region, 12 p.","productDescription":"12 p.","ipdsId":"IP-114333","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":370441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":370440,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/projects/ecological-and-management-implications-climate-change-induced-shifts-phenology-coastal-fish"}],"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              -83.056640625,\n              41.64007838467894\n            ],\n            [\n              -75.5859375,\n              37.020098201368114\n            ],\n            [\n              -70.48828125,\n              41.04621681452063\n            ],\n            [\n              -69.08203125,\n              42.61779143282346\n            ],\n            [\n              -67.1484375,\n              43.83452678223682\n            ],\n            [\n              -67.8515625,\n              47.57652571374621\n            ],\n            [\n              -69.43359375,\n              47.635783590864854\n            ],\n            [\n              -71.630859375,\n              45.089035564831036\n            ],\n            [\n              -76.552734375,\n              44.59046718130883\n            ],\n            [\n              -79.189453125,\n              43.77109381775651\n            ],\n            [\n              -79.716796875,\n              42.4234565179383\n            ],\n            [\n              -83.056640625,\n              41.64007838467894\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Staudinger, Michelle D. 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":207908,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle D.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":777910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordaan, Adrian","contributorId":220124,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","email":"","affiliations":[{"id":37062,"text":"UMASS","active":true,"usgs":false}],"preferred":false,"id":777911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70204360,"text":"70204360 - 2019 - On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models","interactions":[],"lastModifiedDate":"2020-02-19T13:43:22","indexId":"70204360","displayToPublicDate":"2018-05-01T11:34:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5548,"text":"Spatial Statistics","active":true,"publicationSubtype":{"id":10}},"title":"On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models","docAbstract":"<p><span>We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive models. We review the literature on this topic and find that it is mostly incomplete. Our main result is that a SAR model can be written as a unique CAR model, and while a CAR model can be written as a SAR model, it is not unique. In fact, we show how any&nbsp;multivariate&nbsp;Gaussian distribution&nbsp;on a finite set of points with a positive-definite&nbsp;covariance&nbsp;matrix can be written as either a CAR or a SAR model. We illustrate how to obtain any number of SAR covariance matrices from a single CAR covariance matrix by using&nbsp;</span>Givens rotation<span>&nbsp;matrices on a simulated example. We also discuss sparseness in the original CAR construction, and for the resulting SAR&nbsp;weights matrix. For a real example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show that a geostatistical model optimizes the likelihood much better than typical first-order CAR models. We then use the implied weights from the geostatistical model to estimate CAR model parameters that provides the best overall optimization.</span></p>","language":"English","publisher":"Wiley","doi":"10.1016/j.spasta.2018.04.006","usgsCitation":"Hoef, J.M., Hanksb, E.M., and Hooten, M., 2019, On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models: Spatial Statistics, v. 25, p. 68-85, https://doi.org/10.1016/j.spasta.2018.04.006.","productDescription":"18 p.","startPage":"68","endPage":"85","ipdsId":"IP-080788","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468128,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://arxiv.org/abs/1710.07000","text":"Publisher Index Page"},{"id":365775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoef, Jay M. Ver","contributorId":217288,"corporation":false,"usgs":false,"family":"Hoef","given":"Jay","email":"","middleInitial":"M. Ver","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":766520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanksb, Ephraim M.","contributorId":217289,"corporation":false,"usgs":false,"family":"Hanksb","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":766521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":766519,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196649,"text":"70196649 - 2019 - Spatial, road geometric, and biotic factors associated with Barn Owl mortality along an interstate highway","interactions":[],"lastModifiedDate":"2019-01-28T09:54:21","indexId":"70196649","displayToPublicDate":"2018-04-23T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Spatial, road geometric, and biotic factors associated with Barn Owl mortality along an interstate highway","docAbstract":"<p><span>Highway programs typically focus on reducing vehicle collisions with large mammals because of economic or safety reasons while overlooking the millions of birds that die annually from traffic. We studied wildlife‐vehicle collisions along an interstate highway in southern Idaho, USA, with among the highest reported rates of American Barn Owl&nbsp;</span><i>Tyto furcata</i><span><span>&nbsp;</span>road mortality. Carcass data from systematic and<span>&nbsp;</span></span><i>ad hoc</i><span><span>&nbsp;</span>surveys conducted in 2004–2006 and 2013–2015 were used to explore the extent to which spatial, road geometric, and biotic factors explained Barn Owl‐vehicle collisions. Barn Owls outnumbered all other identified vertebrate species of roadkill and represented &gt; 25% of individuals and 73.6% of road‐killed birds. At a 1‐km highway segment scale, the number of dead Barn Owls decreased with increasing numbers of human structures, cumulative length of secondary roads near the highway, and width of the highway median. Number of dead Barn Owls increased with higher commercial average annual daily traffic (CAADT), small mammal abundance index, and with grass rather than shrubs in the roadside verge. The small mammal abundance index was also greater in roadsides with grass versus mixed shrubs, suggesting that Barn Owls may be attracted to grassy portions of the highway with more abundant small mammals for hunting prey. When assessed at a 3‐km highway segment scale, the number of dead Barn Owls again increased with higher CAADT as well as with greater numbers of dairy farms. At a 5‐km scale, number of dead Barn Owls increased with greater percentage of cropland near the highway. While human conversion of the environment from natural shrub‐steppe to irrigated agriculture in this region of Idaho has likely enhanced habitat for Barns Owls, it simultaneously has increased risk for owl‐vehicle collisions where an interstate highway traverses the altered landscape. We review some approaches for highway mitigation and suggest that reducing wildlife‐vehicle collisions involving Barn Owls may contribute to the persistence of this species.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12593","usgsCitation":"Arnold, E.M., Hanser, S.E., Regan, T., Thompson, J., Lowe, M., Kociolek, A., and Belthoff, J.R., 2019, Spatial, road geometric, and biotic factors associated with Barn Owl mortality along an interstate highway: Ibis, v. 161, no. 1, p. 147-161, https://doi.org/10.1111/ibi.12593.","productDescription":"15 p.","startPage":"147","endPage":"161","ipdsId":"IP-084873","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":353670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.290283203125,\n              42.51665075361143\n            ],\n            [\n              -112.4285888671875,\n              42.51665075361143\n            ],\n            [\n              -112.4285888671875,\n              43.60823944964323\n            ],\n            [\n              -116.290283203125,\n              43.60823944964323\n            ],\n            [\n              -116.290283203125,\n              42.51665075361143\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"161","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-03","publicationStatus":"PW","scienceBaseUri":"5afee6d2e4b0da30c1bfbe6a","contributors":{"authors":[{"text":"Arnold, Erin M.","contributorId":204412,"corporation":false,"usgs":false,"family":"Arnold","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":733909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanser, Steve E. 0000-0002-4430-2073 shanser@usgs.gov","orcid":"https://orcid.org/0000-0002-4430-2073","contributorId":152523,"corporation":false,"usgs":true,"family":"Hanser","given":"Steve","email":"shanser@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":733908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regan, Tempe","contributorId":204413,"corporation":false,"usgs":false,"family":"Regan","given":"Tempe","email":"","affiliations":[],"preferred":false,"id":733910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Jeremy","contributorId":204414,"corporation":false,"usgs":false,"family":"Thompson","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":733911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowe, Melinda","contributorId":204415,"corporation":false,"usgs":false,"family":"Lowe","given":"Melinda","email":"","affiliations":[],"preferred":false,"id":733912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kociolek, Angela","contributorId":104796,"corporation":false,"usgs":true,"family":"Kociolek","given":"Angela","email":"","affiliations":[],"preferred":false,"id":733913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":733914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70205109,"text":"70205109 - 2019 - A new indicator framework for quantifying the intensity of the terrestrialwater cycle","interactions":[],"lastModifiedDate":"2019-09-03T15:14:53","indexId":"70205109","displayToPublicDate":"2018-04-02T15:10:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A new indicator framework for quantifying the intensity of the terrestrialwater cycle","docAbstract":"A quantitative framework for characterizing the intensity of the water cycle over land is presented, and illustrated using a spatially distributed water-balance model of the conterminous United States (CONUS). We approach water cycle intensity (WCI) from a landscape perspective; WCI is defined as the sum of precipitation (P) and actual evapotranspiration (AET) over a spatially explicit landscape unit of interest, averaged over a specified time period (step) of interest. The time step may be of any length for which data or simulation results are available (e.g., sub-daily to multi-decadal). We define the storage-adjusted runoff (Q0) as the sum of actual runoff (Q) and the rate of change in soil moisture storage (DS/Dt, positive or negative) during the time step of interest. The Q0 indicator is demonstrated to be mathematically complementary to WCI, in a manner that allows graphical interpretation of their relationship. For the purposes of this study, the indicators were demonstrated using long-term, spatially distributed model simulations with an annual time step. WCI was found to increase over most of the CONUS between the 1945 to 1974 and 1985 to 2014 periods, driven primarily by increases in P. In portions of the western and southeastern CONUS, Q0 decreased because of decreases in Q and soil moisture storage. Analysis of WCI and Q0 at temporal scales ranging from sub-daily to multi-decadal could improve understanding of the wide spectrum of hydrologic responses that have been attributed to water cycle intensification, as well as trends in those responses.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.048","usgsCitation":"Huntington, T.G., Weiskel, P., Wolock, D.M., and McCabe, G.J., 2019, A new indicator framework for quantifying the intensity of the terrestrialwater cycle: Journal of Hydrology, v. 559, p. 361-372, https://doi.org/10.1016/j.jhydrol.2018.02.048.","productDescription":"12 p.","startPage":"361","endPage":"372","ipdsId":"IP-070433","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":367152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              ],\n              [\n                -91.64,\n                48.14\n              ],\n              [\n                -90.83,\n                48.27\n       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     [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"559","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiskel, Peter 0000-0002-9139-8215 pweiskel@usgs.gov","orcid":"https://orcid.org/0000-0002-9139-8215","contributorId":218731,"corporation":false,"usgs":true,"family":"Weiskel","given":"Peter","email":"pweiskel@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":770058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":770059,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204358,"text":"70204358 - 2019 - Evaluation of ageing accuracy with complementary non‐lethal methods for slow‐growing, northern populations of shoal bass","interactions":[],"lastModifiedDate":"2019-12-22T14:37:20","indexId":"70204358","displayToPublicDate":"2018-03-07T11:24:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of ageing accuracy with complementary non‐lethal methods for slow‐growing, northern populations of shoal bass","docAbstract":"<p><span>In the upper Chattahoochee River basin, where some populations of shoal bass,&nbsp;</span><i>Micropterus cataractae</i><span>&nbsp;Williams &amp; Burgess, are imperilled, age and growth data are lacking. Age and growth of shoal bass in this basin were assessed with non‐lethal means using scales and mark–recapture. Mark–recapture data allowed for estimation of accuracy and determination of effects of any scale‐based inaccuracies on growth models. Scale‐based age estimates were accurate for 57% of the samples, and errors of 1 to 3&nbsp;years included equal numbers of over‐ and underestimates of age. von Bertalanffy growth models based on scale ages were similar to those based on mark–recapture ages for ages 3–8 but noticeably divergent for younger and older fish. Scales provided estimates of longevity up to 12&nbsp;years of age, and growth models produced from mark–recapture suggest scale ages underestimated age, especially for older fish. These populations of shoal bass live longer and grow slower than other populations, suggesting regional management strategies may be needed.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/fme.12274","usgsCitation":"Long, J.M., Holley, C.T., and Taylor, A.T., 2019, Evaluation of ageing accuracy with complementary non‐lethal methods for slow‐growing, northern populations of shoal bass: Fisheries Management and Ecology, v. 25, no. 2, p. 150-157, https://doi.org/10.1111/fme.12274.","productDescription":"7 p.","startPage":"150","endPage":"157","ipdsId":"IP-080326","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":365774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Gerogia","otherGeospatial":"Chattahoochee River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -83.91357421875,\n              34.939985151560435\n            ],\n            [\n              -85.95703125,\n              33.63291573870479\n            ],\n            [\n              -86.02294921875,\n              32.175612478499325\n            ],\n            [\n              -85.62744140625,\n              30.619004797647808\n            ],\n            [\n              -84.74853515625,\n              29.611670115197377\n            ],\n            [\n              -83.75976562499999,\n              29.859701442126756\n            ],\n            [\n              -83.95751953125,\n              30.600093873550072\n            ],\n            [\n              -84.52880859375,\n              32.30570601389429\n            ],\n            [\n              -84.83642578125,\n              33.137551192346145\n            ],\n            [\n              -82.96875,\n              34.59704151614417\n            ],\n            [\n              -83.91357421875,\n              34.939985151560435\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":766516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holley, C. T.","contributorId":217373,"corporation":false,"usgs":false,"family":"Holley","given":"C.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":766517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, A. T.","contributorId":217377,"corporation":false,"usgs":false,"family":"Taylor","given":"A.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":766670,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204369,"text":"70204369 - 2019 - Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements","interactions":[],"lastModifiedDate":"2019-07-22T13:00:12","indexId":"70204369","displayToPublicDate":"2018-02-05T12:54:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating abundance of an open population with an <i>N</i>-mixture model using auxiliary data on animal movements","title":"Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements","docAbstract":"<p><span>Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical&nbsp;</span><i>N</i><span>‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (</span><i>Cervus elaphus nelsoni</i><span>) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1692","usgsCitation":"Ketz, A.C., Johnson, T.L., Monello, R.J., Mack, J.A., George, J.L., Hooten, M., Kraft, B.R., Wild, M.A., and Hobbs, N.T., 2019, Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements: Ecological Applications, v. 28, no. 3, p. 816-825, https://doi.org/10.1002/eap.1692.","productDescription":"10 p.","startPage":"816","endPage":"825","ipdsId":"IP-082132","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":365802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ketz, Alison C.","contributorId":217310,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Therese L.","contributorId":217311,"corporation":false,"usgs":false,"family":"Johnson","given":"Therese","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monello, Ryan J.","contributorId":217312,"corporation":false,"usgs":false,"family":"Monello","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mack, John A.","contributorId":217313,"corporation":false,"usgs":false,"family":"Mack","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, Janet L.","contributorId":217314,"corporation":false,"usgs":false,"family":"George","given":"Janet","email":"","middleInitial":"L.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":766558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kraft, Benjamin R.","contributorId":217315,"corporation":false,"usgs":false,"family":"Kraft","given":"Benjamin","email":"","middleInitial":"R.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wild, Margaret A.","contributorId":217316,"corporation":false,"usgs":false,"family":"Wild","given":"Margaret","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766565,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hobbs, N. Thompson","contributorId":217317,"corporation":false,"usgs":false,"family":"Hobbs","given":"N.","email":"","middleInitial":"Thompson","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766566,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70194829,"text":"sir20185003 - 2019 - Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","interactions":[],"lastModifiedDate":"2019-02-04T09:40:36","indexId":"sir20185003","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5003","title":"Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","docAbstract":"<p>Beginning in the 1970s, Alameda County Water District began infiltrating imported water through ponds in repurposed gravel quarries at the Quarry Lakes Regional Park, in the Niles Cone groundwater subbasin, to recharge groundwater and to minimize intrusion of saline, San Francisco Bay water into freshwater aquifers. Hydraulic connection between distinct aquifers underlying Quarry Lakes allows water to recharge the upper aquifer system to depths of 400 feet below land surface, and the Deep aquifer to depths of more than 650 feet. Previous studies of the Niles Cone and southern East Bay Plain groundwater subbasins suggested that these two subbasins may be hydraulically connected. Characterization of storage capacities and hydraulic properties of the complex aquifers and the structural and stratigraphic controls on groundwater movement aids in optimal storage and recovery of recharged water and provides information on the ability of aquifers shared by different water management agencies to fulfill competing storage and extraction demands. The movement of recharge water through the Niles Cone groundwater subbasin from Quarry Lakes and the possible hydraulic connection between the Niles Cone and the southern East Bay Plain groundwater subbasins were investigated using interferometric synthetic aperture radar (InSAR), water-chemistry, and isotopic data, including tritium/helium-3, helium-4, and carbon-14 age-dating techniques.</p><p>InSAR data collected during refilling of the Quarry Lakes recharge ponds show corresponding ground-surface displacement. Maximum uplift was about 0.8 inches, reasonable for elastic expansion of sedimentary materials experiencing an increase in hydraulic head that resulted from pond refilling. Sodium concentrations increase while calcium and magnesium concentrations in groundwater decrease along groundwater flowpaths from the Niles Cone groundwater subbasin through the Deep aquifer to the northwest toward the southern East Bay Plain groundwater subbasin. Residual effects of pre-1970s intrusion of saline water from San Francisco Bay, including high chloride concentrations in&nbsp;groundwater, are evident in parts of the Niles Cone subbasin. Noble gas recharge temperatures indicate two primary recharge sources (Quarry Lakes and Alameda Creek) in the Niles Cone groundwater subbasin. Although recharge at Quarry Lakes affects hydraulic heads as far as the transition zone between the Niles Cone and East Bay Plain groundwater subbasins (about 5 miles), the effect of recharged water on water quality is only apparent in wells near (less than 2 miles) recharge sources. Groundwater chemistry from upper aquifer system wells near Quarry Lakes showed an evaporated signal (less negative oxygen and hydrogen isotopic values) relative to surrounding groundwater and a tritium concentration (2 tritium units) consistent with recently recharged water from a surface-water impoundment.</p><p>Uncorrected carbon-14 activities measured in water sampled from wells in the Niles Cone groundwater subbasin range from 16 to 100 percent modern carbon (pmC). The geochemical reaction modeling software NETPATH was used to interpret carbon-14 ages along a flowpath from Quarry Lakes toward the East Bay Plain groundwater subbasin. Model results indicate that changes in groundwater chemistry are controlled by cation exchange on clay minerals and weathering of primary silicate minerals. Old groundwater (lower carbon-14 activities) is characterized by high dissolved silica and pH. Interpreted carbon-14 ages ranged from 830 to more than 7,000 years before present and are less than helium-4 ages that range from 2,000 to greater than 11,000 years before present. The average horizontal groundwater velocity along the studied flowpath, as calculated using interpreted carbon-14 ages, through the Deep aquifer of the Niles Cone groundwater subbasin is between 3 and 12 feet per year. The groundwater velocity decreases near the boundary of the transition zone to the southern East Bay Plain groundwater subbasin to about 0.5 feet per year. These changes may result from water recharged from different sources converging in flowpaths north of the transition zone, or a boundary to flow between the Niles Cone and southern East Bay Plain groundwater subbasins, likely owing to changes in lithology caused by depositional patterns.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185003","collaboration":"Prepared in cooperation with the East Bay Municipal Utility District, City of Hayward, and Alameda County Water District","usgsCitation":"Teague, Nick, Izbicki, John, Borchers, Jim, Kulongoski, Justin, and Jurgens, Bryant, 2018, Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2018–5003, 62 p., https://doi.org/10.3133/sir20185003.","productDescription":"x, 62 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-043410","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":360934,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5003/versionHist.txt"},{"id":351228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5003/coverthb.jpg"},{"id":351229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5003/sir20185003_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5003"}],"country":"United States","state":"California","county":"Alameda County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.3333,\n              37.5\n            ],\n            [\n              -121.9167,\n              37.5\n            ],\n            [\n              -121.9167,\n              37.8333\n            ],\n            [\n              -122.3333,\n              37.8333\n            ],\n            [\n              -122.3333,\n              37.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Ver. 1.0: February 2018; Ver. 1.1: February 2019","contact":"<p><a href=\"https://mail.google.com/mail/?view=cm&amp;fs=1&amp;tf=1&amp;to=dc_ca@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,&nbsp;<br><a href=\"http://ca.water.usgs.gov/\" data-mce-href=\"http://ca.water.usgs.gov/\">California Water Science Center</a><br>6000 J Street, Placer Hall<br>Sacramento, CA 95819</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Methods<br></li><li>Hydrogeology<br></li><li>Geochemistry<br></li><li>Summary<br></li><li>References Cited<br></li><li>Appendixes<br></li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2018-02-01","revisedDate":"2019-02-01","noUsgsAuthors":false,"publicationDate":"2018-02-01","publicationStatus":"PW","scienceBaseUri":"5a743584e4b0a9a2e9e25c9b","contributors":{"authors":[{"text":"Teague, Nicholas F. 0000-0001-5289-1210 nteague@usgs.gov","orcid":"https://orcid.org/0000-0001-5289-1210","contributorId":2145,"corporation":false,"usgs":true,"family":"Teague","given":"Nicholas","email":"nteague@usgs.gov","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":725483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":1375,"corporation":false,"usgs":true,"family":"Izbicki","given":"John A.","email":"jaizbick@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":726097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borchers, Jim","contributorId":201596,"corporation":false,"usgs":false,"family":"Borchers","given":"Jim","email":"","affiliations":[],"preferred":false,"id":726098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":919,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin T.","email":"kulongos@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":726099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127839,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant C.","email":"bjurgens@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":726100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70204760,"text":"70204760 - 2019 - Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","interactions":[],"lastModifiedDate":"2019-09-03T08:16:10","indexId":"70204760","displayToPublicDate":"2017-12-31T12:46:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","docAbstract":"<p>We genotyped 7,588 brook trout representing 406 collections from across the State&nbsp;of North Carolina (Figure 1) at 12 microsatellite loci (King et al. 2012). The vast majority of<br>collections appeared to represent single populations, based on general conformance to HardyWeinberg equilibrium and limited evidence for linkage-disequilibrium. Allelic diversity was low to&nbsp;moderate relative to Brook Trout Salvelinus fontinalis populations endemic to higher latitudes.&nbsp;Effective population sizes varied widely among populations, but were often very small and&nbsp;indicate that many populations are at risk of losing diversity through genetic drift. Remarkable&nbsp;levels of genetic differentiation exist among populations, which suggests that little, if any, gene&nbsp;flow occurs among most populations. Analysis of molecular variance (AMOVA) revealed that&nbsp;a substantial portion of the observed genetic variation was attributed to differences among&nbsp;patches (44.8%), and there was some variation (11.2%) even among collections within a single&nbsp;patch. These results, taken in conjunction with high levels of genetic differentiation among&nbsp;populations, suggest that the fundamental unit of management for Brook Trout should be the<br>population. Interestingly, despite extensive stocking across the state, the vast majority of wild&nbsp;populations show limited evidence of introgression by northern origin hatchery strains. These&nbsp;results represent a valuable baseline for management and restoration efforts, and can be&nbsp;used to (a) select suitable donor streams for translocation efforts, (b) identify streams with low&nbsp;effective population sizes that may be vulnerable to extirpation, and (c) target stocking efforts&nbsp;into watersheds where extensive introgression has already occurred. All data associated with this&nbsp;manuscript has been publicly released (Kazyak et al. 2017).</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Wild Trout XII Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Wild Trout XII","conferenceDate":"September 26-29, 2017","conferenceLocation":"West Yellowstone, MO","language":"English","publisher":" Wild Trout Symposium","usgsCitation":"Kazyak, D., Lubinski, B.A., Rash, J.M., and King, T.L., 2019, Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King, <i>in</i> Proceedings of the Wild Trout XII Symposium, v. 12, West Yellowstone, MO, September 26-29, 2017, p. 111-117.","productDescription":"7 p.","startPage":"111","endPage":"117","ipdsId":"IP-090520","costCenters":[{"id":365,"text":"Leetown Science 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Carolina\",\"nation\":\"USA  \"}}]}","volume":"12","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":768362,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":768363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rash, Jacob M","contributorId":218128,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","email":"","middleInitial":"M","affiliations":[{"id":39760,"text":"Division of Inland Fisheries, North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":768364,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":768365,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70190431,"text":"sir20175093 - 2019 - Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam","interactions":[],"lastModifiedDate":"2019-12-30T14:46:50","indexId":"sir20175093","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5093","title":"Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam","docAbstract":"<p>In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model.</p><p>Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong&nbsp;Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River.</p><p>The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume.</p><p>In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas.</p><p>The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175093","collaboration":"Prepared in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP)","usgsCitation":"Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.","productDescription":"Report: viii, 64 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-081743","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":349631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5093/coverthb2.jpg"},{"id":349632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5093/sir20175093.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5093 v1.1"},{"id":361066,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5093/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017-5093 Version History"}],"otherGeospatial":"Guam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              144.6240234375,\n              13.230587802102518\n            ],\n            [\n              144.96047973632812,\n              13.230587802102518\n            ],\n            [\n              144.96047973632812,\n              13.652659349024093\n            ],\n            [\n              144.6240234375,\n              13.652659349024093\n            ],\n            [\n              144.6240234375,\n              13.230587802102518\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: December 2017; Version 1.1: February 2019","contact":"<p><a href=\"https://mail.google.com/mail/?view=cm&amp;fs=1&amp;tf=1&amp;to=dc_hi@usgs.gov\" target=\"_blank\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,<br><a href=\"http://hi.water.usgs.gov/\" data-mce-href=\"http://hi.water.usgs.gov/\">Pacific Islands Water Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Acknowledgments<br></li><li>Abstract<br></li><li>Introduction<br></li><li>Description of Study Area<br></li><li>Precipitation-Runoff Modeling System IV<br></li><li>Fena Valley Reservoir Water-Balance Model<br></li><li>Two-Step Modeling Procedure for Fena Valley Reservoir<br></li><li>Summary and Conclusions<br></li><li>Acknowledgments<br></li><li>References Cited<br></li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2017-12-01","revisedDate":"2019-02-06","noUsgsAuthors":false,"publicationDate":"2017-12-01","publicationStatus":"PW","scienceBaseUri":"5a60faf9e4b06e28e9c22a6a","contributors":{"authors":[{"text":"Rosa, Sarah N. 0000-0002-3653-0826 snrosa@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-0826","contributorId":2968,"corporation":false,"usgs":true,"family":"Rosa","given":"Sarah","email":"snrosa@usgs.gov","middleInitial":"N.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":709123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":709124,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70203665,"text":"70203665 - 2019 - Spatial autoregressive models for statistical inference from ecological data","interactions":[],"lastModifiedDate":"2019-05-30T15:18:06","indexId":"70203665","displayToPublicDate":"2017-11-13T15:15:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Spatial autoregressive models for statistical inference from ecological data","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (<i>Phoca vitulina</i>) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ecm.1283","usgsCitation":"Ver Hoef, J.M., Peterson, E.E., Hooten, M., Hanks, E., and Fortin, M., 2019, Spatial autoregressive models for statistical inference from ecological data: Ecological Monographs, v. 88, no. 1, p. 36-59, https://doi.org/10.1002/ecm.1283.","productDescription":"24 p.","startPage":"36","endPage":"59","ipdsId":"IP-074171","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468136,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecm.1283","text":"External Repository"},{"id":364260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ver Hoef, Jay M.","contributorId":42504,"corporation":false,"usgs":true,"family":"Ver Hoef","given":"Jay","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":763472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Erin E.","contributorId":177839,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":763473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":763474,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":763475,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fortin, Marie-Josée","contributorId":40462,"corporation":false,"usgs":true,"family":"Fortin","given":"Marie-Josée","affiliations":[],"preferred":false,"id":763476,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70205038,"text":"70205038 - 2019 - Exploring the historical earthquakes preceding the giant 1960 Chile earthquake in a time‐dependent seismogenic zone","interactions":[],"lastModifiedDate":"2019-08-29T09:25:04","indexId":"70205038","displayToPublicDate":"2017-11-07T09:23:24","publicationYear":"2019","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":"Exploring the historical earthquakes preceding the giant 1960 Chile earthquake in a time‐dependent seismogenic zone","docAbstract":"New documentary findings and available paleoseismological evidence provide both new insights into the historical seismic sequence that ended with the giant 1960 south-central Chile earthquake and relevant information about the region’s seismogenic zone. According to the few available written records, this region was previously struck by earthquakes of varying size in 1575, 1737, and 1837. We expanded the existing compilations of the effects of the two latter using unpublished first-hand accounts found in archives in Chile, Peru, Spain, and New England. We further investigated their sources by comparing the newly unearthed historical data and available paleoseismological evidence with the effects predicted by hypothetical dislocations. The results reveal significant differences in the along-strike and depth distribution of the ruptures in 1737, 1837, and 1960. While the 1737 rupture likely occurred in the northern half of the 1960 region, on a narrow and deep portion of the megathrust, the 1837 rupture occurred mainly in the southern half and slipped over a wide range of depth. Such a wide rupture in 1837 disagrees with the narrow and shallow seismogenic zone currently inferred along this region. If in fact there is now a narrow zone where 200 years ago there was a wider one, it means that the seismogenic zone changes with time, perhaps between seismic cycles. Such change probably explains the evident variability in both size and location of the great earthquakes that have struck this region over the last centuries, as evidenced by written history, and through millennia, as inferred from paleoseismology.","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0120170103","usgsCitation":"Cisternas, M., Carvajal, M., Wesson, R.L., Ely, L., and Gorigoitia, N., 2019, Exploring the historical earthquakes preceding the giant 1960 Chile earthquake in a time‐dependent seismogenic zone: Bulletin of the Seismological Society of America, v. 107, no. 6, p. 2664-2675, https://doi.org/10.1785/0120170103.","productDescription":"12 p.","startPage":"2664","endPage":"2675","ipdsId":"IP-091754","costCenters":[{"id":300,"text":"Geologic Hazards Science 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