{"pageNumber":"295","pageRowStart":"7350","pageSize":"25","recordCount":40783,"records":[{"id":70208560,"text":"70208560 - 2020 - UAV-derived estimates of forest structure to inform ponderosa pine forest restoration","interactions":[],"lastModifiedDate":"2020-06-19T16:23:52.408024","indexId":"70208560","displayToPublicDate":"2019-12-16T06:54:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"UAV-derived estimates of forest structure to inform ponderosa pine forest restoration","docAbstract":"<p><span>Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (&lt;4&nbsp;ha) and mid‐scales (4–400&nbsp;ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/rse2.137","usgsCitation":"Belmonte, A., Sankey, T.T., Biederman, J.A., Bradford, J.B., Goetz, S.J., Kolb, T., and Woolley, T., 2020, UAV-derived estimates of forest structure to inform ponderosa pine forest restoration: Remote Sensing in Ecology and Conservation, v. 6, no. 2, p. 181-197, https://doi.org/10.1002/rse2.137.","productDescription":"17 p.","startPage":"181","endPage":"197","ipdsId":"IP-113835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.137","text":"Publisher Index Page"},{"id":372377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Western United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.150390625,\n              48.8936153614802\n            ],\n            [\n              -123.48632812499999,\n              49.1242192485914\n            ],\n            [\n              -123.22265625000001,\n              48.31242790407178\n            ],\n            [\n              -125.595703125,\n              48.42920055556841\n            ],\n            [\n              -124.76074218749999,\n              46.800059446787316\n            ],\n            [\n              -125.33203125,\n              41.77131167976407\n          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-104.150390625,\n              48.8936153614802\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":782489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":782490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biederman, Joel A.","contributorId":201939,"corporation":false,"usgs":false,"family":"Biederman","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":782491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":782488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott J.","contributorId":222547,"corporation":false,"usgs":false,"family":"Goetz","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":782492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolb, Thomas","contributorId":174381,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","affiliations":[],"preferred":false,"id":782493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woolley, Travis","contributorId":222548,"corporation":false,"usgs":false,"family":"Woolley","given":"Travis","affiliations":[{"id":40560,"text":"The Nature Conservancy Northern Arizona Program, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":782494,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70208852,"text":"70208852 - 2020 - Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager","interactions":[],"lastModifiedDate":"2020-03-03T11:28:40","indexId":"70208852","displayToPublicDate":"2019-12-13T11:24:27","publicationYear":"2020","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":"Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager","docAbstract":"<p><span>Central-place foragers can be constrained by the distance between habitats. When an organism relies on a central place for thermal refuge, the distance to food resources can potentially constrain foraging behavior. We investigated the effect of distance between thermal refuges and forage patches of the cold-intolerant marine mammal, the Florida manatee (</span><i>Trichechus manatus latirostris</i><span>), on foraging duration. We tested the alternative hypotheses of time minimization and energy maximization as a response to distance between habitats. We also determined if manatees mitigate foraging constraints with increased visits to closer thermal refuges. We used hidden Markov models to assign discrete behaviors from movement parameters as a function of water temperature and assessed the influence of distance on foraging duration in water temperatures above (&gt; 20°C) and below (≤ 20°C) the lower critical limit of the thermoneutral zone of manatees. We found that with increased distance, manatees decreased foraging duration in cold water temperature and increased foraging duration in warmer temperatures. We also found that manatees returned to closer thermal refuges more often. Our results suggest that the spatial relationship of thermal and forage habitats can impact behavioral decisions regarding foraging. Addressing foraging behavior questions while considering thermoregulatory behavior implicates the importance of understanding changing environments on animal behavior, particularly in the face of current global change.</span></p>","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyz197","usgsCitation":"Haase, C.G., Fletcher, R.J., Slone, D.H., Reid, J.P., and Butler, S.M., 2020, Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager: Journal of Mammalogy, v. 101, no. 1, p. 271-280, https://doi.org/10.1093/jmammal/gyz197.","productDescription":"10 p.","startPage":"271","endPage":"280","ipdsId":"IP-093855","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyz197","text":"Publisher Index Page"},{"id":372851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Haase, Catherine G. 0000-0002-7682-0625 chaase@usgs.gov","orcid":"https://orcid.org/0000-0002-7682-0625","contributorId":195794,"corporation":false,"usgs":true,"family":"Haase","given":"Catherine","email":"chaase@usgs.gov","middleInitial":"G.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fletcher, Robert J. Jr.","contributorId":41294,"corporation":false,"usgs":true,"family":"Fletcher","given":"Robert","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":783668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slone, Daniel H. 0000-0002-9903-9727 dslone@usgs.gov","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":205617,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel","email":"dslone@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":783670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783671,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207945,"text":"70207945 - 2020 - A comparison of the Trojan Y Chromosome strategy to harvesting models for eradication of nonnative species","interactions":[],"lastModifiedDate":"2020-06-05T11:52:02.919194","indexId":"70207945","displayToPublicDate":"2019-12-12T15:44:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2827,"text":"Natural Resource Modeling","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of the Trojan Y Chromosome strategy to harvesting models for eradication of nonnative species","docAbstract":"<p>The Trojan Y Chromosome strategy (TYC) is a promising eradication method for biological control of nonnative species. The strategy works by manipulating the sex ratio of a population through the introduction of<span>&nbsp;</span><i>supermales</i><span>&nbsp;</span>that guarantee male offspring. In the current study, we compare the TYC method with a pure harvesting strategy. We also analyze a hybrid harvesting model that mirrors the TYC strategy. The dynamic analysis leads to results on stability of solutions and bifurcations of the model. Several conclusions about the different strategies are established via optimal control methods. In particular, the results affirm that either a pure harvesting or hybrid strategy may work better than the TYC method at controlling a nonnative species population.</p><p><strong>Recommendations for resource managers</strong></p><p><br></p><ul class=\"unordered-list\"><li><p>Where harvesting is feasible, it is as effective if not more effective than the classical TYC method. Therein managers may attempt harvesting female fish while stocking males or harvesting both male and female fishes.</p></li><li><p>Managers may attempt linear harvesting, saturating density‐dependent harvesting, and unbounded density‐dependent harvesting. Linear harvesting is seen to be the most effective.</p></li><li><p>We caution against the outright use of harvesting due to various density‐dependent effects that may arise. To this end hybrid models that involve a combination of harvesting and TYC‐type methods might be a better strategy.</p></li><li><p>One may also use harvesting as a tool in mesocosm settings to predict the efficacy of the TYC strategy in the wild.</p></li></ul>","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12252","usgsCitation":"Lyu, J., Schofield, P.J., Reaver, K., Beauregard, M., and Parshad, R.D., 2020, A comparison of the Trojan Y Chromosome strategy to harvesting models for eradication of nonnative species: Natural Resource Modeling, v. 33, no. 2, e12252, 43 p.; Data Release, https://doi.org/10.1111/nrm.12252.","productDescription":"e12252, 43 p.; Data Release","ipdsId":"IP-102664","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458375,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1810.08279","text":"External Repository"},{"id":371399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374930,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WXHRGQ","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Trojan Y chromosome strategy for eradication of non-native species data"}],"volume":"33","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyu, Jingjing","contributorId":221684,"corporation":false,"usgs":false,"family":"Lyu","given":"Jingjing","email":"","affiliations":[],"preferred":false,"id":779841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Pamela J. 0000-0002-8752-2797 pschofield@usgs.gov","orcid":"https://orcid.org/0000-0002-8752-2797","contributorId":168659,"corporation":false,"usgs":true,"family":"Schofield","given":"Pamela","email":"pschofield@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":779842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reaver, Kristen 0000-0003-2304-4674 kwoodling@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-4674","contributorId":204132,"corporation":false,"usgs":true,"family":"Reaver","given":"Kristen","email":"kwoodling@usgs.gov","affiliations":[{"id":36862,"text":"Cherokee Nations","active":true,"usgs":false}],"preferred":true,"id":779843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beauregard, Matthew","contributorId":221685,"corporation":false,"usgs":false,"family":"Beauregard","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":779844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parshad, Rana D.","contributorId":221686,"corporation":false,"usgs":false,"family":"Parshad","given":"Rana","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":779845,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70212646,"text":"70212646 - 2020 - Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-25T14:46:14.53336","indexId":"70212646","displayToPublicDate":"2019-12-11T09:39:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, M<sub>w</sub> 7.1 subduction zone earthquake sequence","title":"Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence","docAbstract":"<p><span>Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska (</span><span class=\"inline-formula no-formula-id\">⁠<span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>M</mi><mi mathvariant=&quot;normal&quot;>w</mi></msub></math>\"><span id=\"MathJax-Span-11\" class=\"math\"><span><span id=\"MathJax-Span-12\" class=\"mrow\"><span id=\"MathJax-Span-13\" class=\"msub\"><span id=\"MathJax-Span-14\" class=\"mi\">M</span><span id=\"MathJax-Span-15\" class=\"mi\">w</span></span></span></span></span><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span class=\"MJX_Assistive_MathML\"></span></span></span></span><span>&nbsp;7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10&nbsp;s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations&nbsp;</span><i><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mi xmlns=&quot;&quot;>&amp;#x3C3;</mi></math>\"><span id=\"MathJax-Span-16\" class=\"math\"><span><span id=\"MathJax-Span-17\" class=\"mrow\"><span id=\"MathJax-Span-18\" class=\"mi\">σ</span></span></span></span></span></span></i><span>&nbsp;of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50&nbsp;km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190188","usgsCitation":"McNamara, D.E., Wolin, E., Powers, P.M., Shumway, A., Moschetti, M.P., Rekoske, J., Thompson, E.M., Mueller, C., and Petersen, M.D., 2020, Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence: Seismological Research Letters, v. 91, no. 1, p. 183-194, https://doi.org/10.1785/0220190188.","productDescription":"12 p.","startPage":"183","endPage":"194","ipdsId":"IP-113669","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -150.88623046875,\n              60.67855911666429\n            ],\n            [\n              -149.007568359375,\n              60.67855911666429\n            ],\n            [\n              -149.007568359375,\n              61.66120280305204\n            ],\n            [\n              -150.88623046875,\n              61.66120280305204\n            ],\n            [\n              -150.88623046875,\n              60.67855911666429\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolin, Emily 0000-0003-1610-1191","orcid":"https://orcid.org/0000-0003-1610-1191","contributorId":221834,"corporation":false,"usgs":true,"family":"Wolin","given":"Emily","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797205,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rekoske, John 0000-0003-0539-2069","orcid":"https://orcid.org/0000-0003-0539-2069","contributorId":220108,"corporation":false,"usgs":true,"family":"Rekoske","given":"John","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797206,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797207,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797208,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797209,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70227282,"text":"70227282 - 2020 - Seasonal variation in environmental and behavioural drivers of annual-cycle habitat selection in a nearshore seabird","interactions":[],"lastModifiedDate":"2022-01-07T14:26:37.217941","indexId":"70227282","displayToPublicDate":"2019-12-10T08:22:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal variation in environmental and behavioural drivers of annual-cycle habitat selection in a nearshore seabird","docAbstract":"<h3 id=\"ddi13015-sec-0001-title\" class=\"article-section__sub-title section1\">Aim</h3><p>Conservation of highly mobile species often requires identifying locations or time periods of elevated vulnerability. Since both extrinsic habitat conditions and intrinsic behavioural and energetic requirements contribute to habitat use at the landscape scale, identifying spatial or temporal foci for conservation intervention requires understanding how habitat needs and distributions vary across the annual cycle. Nearshore marine birds inhabit highly dynamic systems and have widely varying habitat needs among breeding, moult and non-breeding seasons, making them a useful case study for testing the relative contributions of individual resource requirements and environmental conditions in driving annual variation in distribution patterns.</p><h3 id=\"ddi13015-sec-0002-title\" class=\"article-section__sub-title section1\">Location</h3><p>Northern Gulf of Mexico (USA).</p><h3 id=\"ddi13015-sec-0003-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We tracked Brown Pelicans using bird-borne GPS transmitters and used a combination of Hidden Markov Models and multivariate selectivity analysis to compare the characteristics of preferred resident habitats used throughout the annual cycle.</p><h3 id=\"ddi13015-sec-0004-title\" class=\"article-section__sub-title section1\">Results</h3><p>Habitat selection was driven by dynamic oceanographic variables during all stages of the annual cycle. Key habitat characteristics varied between seasons, with particularly strong selection on high productivity, low temperature and low salinity during the breeding and post-breeding moult periods. The post-breeding moult also corresponded to a time of limited availability of preferred habitats, resulting in extensive overlap between breeding populations from different administrative planning areas.</p><h3 id=\"ddi13015-sec-0005-title\" class=\"article-section__sub-title section1\">Main conclusions</h3><p>By incorporating seasonal variation in individual behaviour and resource requirements into our habitat models, we were able to identify the post-breeding moult as a period of high selectivity and restricted availability of preferred habitats for Brown Pelicans. Locations meeting preferred habitat criteria during the post-breeding period, particularly estuarine habitats with high productivity and low salinity, would therefore be high-value targets for management and restoration. Our analysis demonstrates the importance of accounting for both intrinsic and extrinsic temporal variation in evaluating habitat selection.</p>","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13015","usgsCitation":"Lamb, J., Satgé, Y., and Jodice, P.G., 2020, Seasonal variation in environmental and behavioural drivers of annual-cycle habitat selection in a nearshore seabird: Diversity and Distributions, v. 26, no. 2, p. 254-266, https://doi.org/10.1111/ddi.13015.","productDescription":"13 p.","startPage":"254","endPage":"266","ipdsId":"IP-107894","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":458397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13015","text":"Publisher Index Page"},{"id":394015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.734375,\n              26.07652055985697\n            ],\n            [\n              -82.63916015625,\n              26.07652055985697\n            ],\n            [\n              -82.63916015625,\n              30.600093873550072\n            ],\n            [\n              -97.734375,\n              30.600093873550072\n            ],\n            [\n              -97.734375,\n              26.07652055985697\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"26","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, J. S.","contributorId":270975,"corporation":false,"usgs":false,"family":"Lamb","given":"J. S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":830263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Satgé, Y. G.","contributorId":265430,"corporation":false,"usgs":false,"family":"Satgé","given":"Y. G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":830264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":830265,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208438,"text":"70208438 - 2020 - Successful molecular detection studies require clear communication among diverse research partners","interactions":[],"lastModifiedDate":"2020-02-10T18:12:55","indexId":"70208438","displayToPublicDate":"2019-12-09T18:05:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Successful molecular detection studies require clear communication among diverse research partners","docAbstract":"<p><span>Molecular detection techniques are powerful tools used in ecological applications ranging from diet analyses to pathogen surveillance. Research partnerships that use these tools often involve collaboration among professionals with expertise in field biology, laboratory techniques, quantitative modeling, wildlife disease, and natural resource management. However, in many cases, each of these collaborators lacks specific knowledge about the approaches, decisions, methods, and terminology used by their research partners, which can impede effective communication and act as a barrier to the efficient use of molecular data for ecological inferences and subsequent conservation decision making. We outline a collaborative framework to assist colleagues with diverse types of expertise to effectively translate their scientific and management needs to research partners from other specialties. The molecular techniques used to detect organisms will continue to advance both in sophistication and in the breadth of ecological applications. Our objective is to enable ecologists to harness the full utility of these methods by developing effective collaborative partnerships.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2141","usgsCitation":"Mosher, B.A., Bernard, R.F., Lorch, J.M., Miller, D., Richgels, K.L., White, C.L., and Campbell Grant, E.H., 2020, Successful molecular detection studies require clear communication among diverse research partners: Frontiers in Ecology and the Environment, v. 18, no. 1, p. 43-51, https://doi.org/10.1002/fee.2141.","productDescription":"9 p.","startPage":"43","endPage":"51","ipdsId":"IP-104082","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":372203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Mosher, B. A.","contributorId":216927,"corporation":false,"usgs":false,"family":"Mosher","given":"B.","email":"","middleInitial":"A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":781881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernard, R. F.","contributorId":216081,"corporation":false,"usgs":false,"family":"Bernard","given":"R.","email":"","middleInitial":"F.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":781882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":781883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, D. A. W.","contributorId":201361,"corporation":false,"usgs":false,"family":"Miller","given":"D. A. W.","affiliations":[],"preferred":false,"id":781988,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richgels, Katherine L. D. 0000-0003-2834-9477 krichgels@usgs.gov","orcid":"https://orcid.org/0000-0003-2834-9477","contributorId":151205,"corporation":false,"usgs":true,"family":"Richgels","given":"Katherine","email":"krichgels@usgs.gov","middleInitial":"L. D.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":781885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, C. LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":781886,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":781880,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70207821,"text":"70207821 - 2020 - Dimensional effects of inter-phase mass transfer on attenuation of structurally trapped gaseous carbon dioxide in shallow aquifers","interactions":[],"lastModifiedDate":"2020-12-14T13:14:53.68955","indexId":"70207821","displayToPublicDate":"2019-12-09T15:50:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2228,"text":"Journal of Computational Physics","active":true,"publicationSubtype":{"id":10}},"title":"Dimensional effects of inter-phase mass transfer on attenuation of structurally trapped gaseous carbon dioxide in shallow aquifers","docAbstract":"<p><span>Based on experimental evidence and using mathematical modeling, inter-phase mass transfer processes of CO</span><sub>2</sub><span>&nbsp;exsolving from and dissolving into water in heterogeneous porous media are investigated under two fundamentally different flow conditions: in a quasi one dimensional vertical column and in a two-dimensional tank with a lateral background water flow, both at laboratory scale. In both cases, the CO</span><sub>2</sub><span>&nbsp;dissolved in water under a given overpressure is injected for a certain period at the bottom of the tank, exsolves, and migrates upwards. A layer of fine sand is present in the tanks designed to mimic geological scenarios of accumulation and trapping of exsolved CO</span><sub>2</sub><span>&nbsp;in shallow aquifers. Then, clean water is injected and the accumulated CO</span><sub>2</sub><span>&nbsp;is dissolved back into the flowing water. The study aims to point out the differences in the mass transfer processes between the quasi-1D and 2D cases using a mathematical model of two-phase compositional flow in heterogeneous porous media calibrated to the experimental datasets, and expose strategies that should be explored in future research. Additionally, temperature variations observed during the 2D experiments allow for analysis of isothermal versus non-isothermal effects on the processes of multiphase CO</span><sub>2</sub><span>&nbsp;evolution. The mathematical model is discretized and solved using the mixed hybrid finite element method in 2D that allows for the simulation of both advection- and diffusion-dominated processes accurately.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jcp.2019.109178","usgsCitation":"Jakub Solovsky, Radek Fucik, Plampin, M.R., Illangasekare, T.H., and Jiri Mikyska, 2020, Dimensional effects of inter-phase mass transfer on attenuation of structurally trapped gaseous carbon dioxide in shallow aquifers: Journal of Computational Physics, v. 405, 109178, https://doi.org/10.1016/j.jcp.2019.109178.","productDescription":"109178","ipdsId":"IP-104741","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":458403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1580146","text":"Publisher Index Page"},{"id":371236,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"405","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jakub Solovsky","contributorId":217696,"corporation":false,"usgs":false,"family":"Jakub Solovsky","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":779439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Radek Fucik","contributorId":217697,"corporation":false,"usgs":false,"family":"Radek Fucik","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":779440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":779441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Illangasekare, Tissa H.","contributorId":194933,"corporation":false,"usgs":false,"family":"Illangasekare","given":"Tissa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":779442,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jiri Mikyska","contributorId":217700,"corporation":false,"usgs":false,"family":"Jiri Mikyska","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":779443,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70209005,"text":"70209005 - 2020 - Declining aluminum toxicity and the role of exposure duration on brook trout mortality in acidified streams of the Adirondack Mountains, New York, USA","interactions":[],"lastModifiedDate":"2021-01-08T14:19:41.180432","indexId":"70209005","displayToPublicDate":"2019-12-09T14:00:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Declining aluminum toxicity and the role of exposure duration on brook trout mortality in acidified streams of the Adirondack Mountains, New York, USA","docAbstract":"<p><span>Mortality of brook trout&nbsp;</span><i>Salvelinus fontinalis</i><span>&nbsp;and water chemistry were characterized in 6 headwater streams in the western Adirondacks of New York during spring 2015, 2016, and 2017 and compared with results from analogous tests done between 1980 and 2003 in many of the same streams, to assess temporal changes in toxicity and inorganic monomeric aluminum (Al</span><sub>i</sub><span>) concentrations, and the role of Al</span><sub>i</sub><span>&nbsp;exposure duration on brook trout survival. The Al</span><sub>i</sub><span>&nbsp;concentrations of 2 and 4 µmol L</span><sup>–1</sup><span>&nbsp;corresponded to low‐to‐moderate and high mortality thresholds, but prolonged exposure to ≥1 µmol Al</span><sub>i</sub><span> L</span><sup>–1</sup><span>&nbsp;also produced mortality. The variability, mean, and highest Al</span><sub>i</sub><span>&nbsp;concentrations in Buck Creek year round, and in several other streams during spring, have decreased significantly over the past 3 decades. Logistic models indicate that Al</span><sub>i</sub><span>&nbsp;surpassed highly toxic concentrations in Buck Creek for 3 to 4 mo annually during 2001 to 2003 and for 2 to 3 wk annually during 2015 to 2017. The loss of extremely high Al</span><sub>i</sub><span>&nbsp;episodes indicates that toxicity has declined markedly between the 1989 to 1990, 2001 to 2003, and 2015 to 2017 test periods, yet Al</span><sub>i</sub><span>&nbsp;concentrations can still cause moderate‐to‐high and complete (100%) mortality. The logistic models illustrate how mortality of brook trout in several Adirondack streams likely decreased in response to the 1990 Amendments to the United States' Clean Air Act (which decreased acidity, Al</span><sub>i</sub><span>&nbsp;concentrations, and duration of toxic episodes) and offer a means to predict how changes in US regulations that limit emissions of NO</span><sub><i>x</i></sub><span>&nbsp;and SO</span><sub><i>x</i></sub><span>&nbsp;(and N and S deposition loads) could affect fish survival and stream ecosystems in this region and across the Northeast.&nbsp;</span></p>","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4645","usgsCitation":"Baldigo, B.P., George, S., Lawrence, G.B., and Paul, E.A., 2020, Declining aluminum toxicity and the role of exposure duration on brook trout mortality in acidified streams of the Adirondack Mountains, New York, USA: Environmental Toxicology and Chemistry, v. 39, no. 3, p. 623-636, https://doi.org/10.1002/etc.4645.","productDescription":"14 p.","startPage":"623","endPage":"636","ipdsId":"IP-110828","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":458405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.4645","text":"Publisher Index Page"},{"id":373103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.98306274414062,\n              43.6912114102249\n            ],\n            [\n              -74.66514587402344,\n              43.6912114102249\n            ],\n            [\n              -74.66514587402344,\n              43.830068853318785\n            ],\n            [\n              -74.98306274414062,\n              43.830068853318785\n            ],\n            [\n              -74.98306274414062,\n              43.6912114102249\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"39","issue":"3","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott 0000-0002-2111-6817 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-2111-6817","contributorId":223202,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul, Eric A. 0000-0003-0706-0076","orcid":"https://orcid.org/0000-0003-0706-0076","contributorId":223203,"corporation":false,"usgs":false,"family":"Paul","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":784508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208802,"text":"70208802 - 2020 - Toward ecosystem accounts for Rwanda: Tracking 25 years of change in potential supply and flows of ecosystem services","interactions":[],"lastModifiedDate":"2021-10-22T19:44:22.967675","indexId":"70208802","displayToPublicDate":"2019-12-09T12:36:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5936,"text":"People and Nature","active":true,"publicationSubtype":{"id":10}},"title":"Toward ecosystem accounts for Rwanda: Tracking 25 years of change in potential supply and flows of ecosystem services","docAbstract":"<p>1. Rwanda, a small but rapidly developing central African nation, has undertaken development of natural capital accounts to better inform its economic development through the World Bank’s Wealth Accounting and Valuation of Ecosystem Services (WAVES) Partnership. In this paper, we develop ecosystem service (ES) models to quantify the physical supply components of ecosystem accounts in Rwanda from 1990 to 2015. </p><p>2. We applied the InVEST carbon storage, sediment delivery ratio, and annual and seasonal water yield models to map changes in potential ES supply nationwide. We also quantified flows of sediment and water to 96 hydroelectric dam, irrigation dam, and water treatment plant sites. </p><p>3. Over a 25-year period, we found declines in all ES, which were most strongly driven by conversion of forests to cropland. Declines were most pronounced from 1990 to 2000 and 2010 to 2015; ES were relatively stable from 2000 to 2010. From 2010 to 2015, over 42% of Rwanda’s water-use sites (representing 17% of the nation’s hydroelectric generation capacity and 69% of its water treatment capacity) had increases in sediment export and quick flow greater than the national average. </p><p>4. Our results quantify nationwide ES trends, their implications for key water-dependent industries, and the importance of protected areas in safeguarding ES potential supply and flows in Rwanda. They also provide data that can be integrated with existing land, water, and economic accounts for Rwanda, as well as a baseline to inform development strategies that better link economic and environmental goals.</p>","language":"English","publisher":"British Ecological Society","doi":"10.1002/pan3.10062","usgsCitation":"Bagstad, K.J., Ingram, J.C., Lange, G., Masozera, M.K., Ancona, Z.H., Bana, M., Kabogo, D., Musana, B., Nabahungu, N.L., Rukundo, E., Rutebuka, E., Polasky, S., Rugege, D., and Uwera, C., 2020, Toward ecosystem accounts for Rwanda: Tracking 25 years of change in potential supply and flows of ecosystem services: People and Nature, v. 1, no. 2, p. 163-188, https://doi.org/10.1002/pan3.10062.","productDescription":"26 p.","startPage":"163","endPage":"188","ipdsId":"IP-090139","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":458407,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70209788,"text":"70209788 - 2020 - Parsing the effects of demography, climate, and management on recurrent brucellosis outbreaks in elk","interactions":[],"lastModifiedDate":"2020-05-04T18:18:52.416224","indexId":"70209788","displayToPublicDate":"2019-12-01T07:27:54","publicationYear":"2020","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":"Parsing the effects of demography, climate, and management on recurrent brucellosis outbreaks in elk","docAbstract":"1. Zoonotic pathogens can harm human health and well-being directly or by impacting livestock. Pathogens that spillover from wildlife can also impair conservation efforts if humans perceive wildlife as pests. Brucellosis, caused by the bacterium Brucella abortus, circulates in elk and bison herds of the Greater Yellowstone Ecosystem and poses a risk to cattle and humans. Our goal was to understand the relative effects of climatic drivers, host demography, and management control programs on disease dynamics. \n2. Using >20 years of serologic, demographic, and environmental data on brucellosis in elk, we built stochastic compartmental models to assess the influences of climate forcing, herd immunity, population turnover, and management interventions on pathogen transmission. Data were collected at feedgrounds visited in winter by free-ranging elk in Wyoming, USA.\n3. Snowpack, hypothesized as a driver of elk aggregation and thus brucellosis transmission, was strongly correlated across feedgrounds. We expected this variable to drive synchronized disease dynamics across herds. Instead, we demonstrate asynchronous epizootics driven by variation in demographic rates.\n4. We evaluated the effectiveness of test-and-slaughter of seropositive female elk at two feedgrounds. Test-and-slaughter temporarily reduced herd-level seroprevalence but likely reduced herd immunity while removing few infectious individuals, resulting in subsequent outbreaks once the intervention ceased. We simulated an alternative strategy of removing seronegative female elk and found it would increase herd immunity, yielding fewer infections. We evaluated a second experimental treatment wherein feeding density was reduced at one feedground, but we found no evidence for an effect despite a decade of implementation.\n5. Synthesis and applications: Positive serostatus is often weakly correlated with infectiousness but is nevertheless used to make management decisions including lethal removal in wildlife disease systems. We show how this can have adverse consequences whereas efforts that maintain herd immunity can have longer-term protective effects. Climatic drivers may not result in synchronous disease dynamics across populations unless vital rates are also similar because demographic factors have a large influence on disease patterns.","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13553","collaboration":"","usgsCitation":"Cotterill, G., Cross, P., Merkle, J., Rogerson, J., Scurlock, B., and Du Toit, J.T., 2020, Parsing the effects of demography, climate, and management on recurrent brucellosis outbreaks in elk: Journal of Applied Ecology, v. 2, no. 57, p. 379-389, https://doi.org/10.1111/1365-2664.13553.","productDescription":"11 p.","startPage":"379","endPage":"389","ipdsId":"IP-106553","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13553","text":"Publisher Index Page"},{"id":374346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.3134765625,\n              42.98857645832184\n            ],\n            [\n              -108.19335937499999,\n              42.98857645832184\n            ],\n            [\n              -108.19335937499999,\n              45.27488643704891\n            ],\n            [\n              -111.3134765625,\n              45.27488643704891\n            ],\n            [\n              -111.3134765625,\n              42.98857645832184\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"2","issue":"57","noUsgsAuthors":false,"publicationDate":"2020-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Cotterill, Gavin G.","contributorId":203301,"corporation":false,"usgs":false,"family":"Cotterill","given":"Gavin G.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":788017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":218820,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merkle, Jerod 0000-0003-0100-1833","orcid":"https://orcid.org/0000-0003-0100-1833","contributorId":224370,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":788019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogerson, JD","contributorId":224371,"corporation":false,"usgs":false,"family":"Rogerson","given":"JD","email":"","affiliations":[{"id":40869,"text":"WYGFD","active":true,"usgs":false}],"preferred":false,"id":788020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scurlock, BM","contributorId":199582,"corporation":false,"usgs":false,"family":"Scurlock","given":"BM","email":"","affiliations":[],"preferred":false,"id":788021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Du Toit, Johan T. 0000-0003-0705-7117","orcid":"https://orcid.org/0000-0003-0705-7117","contributorId":210266,"corporation":false,"usgs":false,"family":"Du Toit","given":"Johan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":788022,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70207997,"text":"70207997 - 2020 - Drivers of landscape change in the northwest boreal region","interactions":[],"lastModifiedDate":"2025-05-16T19:48:44.635521","indexId":"70207997","displayToPublicDate":"2019-11-30T17:44:10","publicationYear":"2020","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Drivers of landscape change in the northwest boreal region","docAbstract":"The northwest boreal region (NWB) of North America is a land of extremes. Extending more than 1.3 million square kilometers (330 million acres), it encompasses the entire spectrum between inundated wetlands below sea level to the tallest peak in North America. Permafrost gradients span from nearly continuous to absent. Boreal ecosystems are inherently dynamic and continually change over decades to millennia. The braided rivers that shape the valleys and wetlands continually change course, creating and removing vast wetlands and peatlands. Glacial melt, erosion, fires, permafrost dynamics, and wind-blown loess are among the shaping forces of the landscape. As a result, species interactions and ecosystem processes are shifting across time.\nThe NWB is a data-poor region, and the intention of the NWB Landscape Conservation Cooperative is to determine what data are not available and what data are available. For instance, historical baseline data describing the economic and social relationships in association with the ecological condition of the NWB landscape are often lacking. Likewise, the size and remoteness of this region make it challenging to measure basic biological information, such as species population sizes or trends. The paucity of weather and climate monitoring stations also compound the ability to model future climate trends and impacts, which is part of the nature of working in the north. The purpose of this volume is to create a resource for regional land and resource managers and researchers by synthesizing the latest research on the historical and current status of landscape-scale drivers (including anthropogenic activities) and ecosystem processes, future projected changes of each, and the effects of changes on important resources. Generally, each chapter is coauthored by researchers and land and natural resource managers from the United States and Canada.","language":"English","publisher":"University of Alaska Press","isbn":"9781602233973","usgsCitation":"2020, Drivers of landscape change in the northwest boreal region, 225 p.","productDescription":"225 p.","ipdsId":"IP-074868","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":371546,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":371479,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.jstor.org/stable/j.ctv21fqdwb","linkFileType":{"id":5,"text":"html"}}],"country":"Canada, United States","otherGeospatial":"Northwest Boreal Region","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Markon, Carl","contributorId":212151,"corporation":false,"usgs":false,"family":"Markon","given":"Carl","affiliations":[{"id":38437,"text":"Retired, U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":780080,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Sesser, Amanda","contributorId":221730,"corporation":false,"usgs":false,"family":"Sesser","given":"Amanda","email":"","affiliations":[],"preferred":false,"id":780081,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Rockhill, Aimee P.","contributorId":221731,"corporation":false,"usgs":false,"family":"Rockhill","given":"Aimee","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":780082,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Magness, Dawn R","contributorId":221735,"corporation":false,"usgs":false,"family":"Magness","given":"Dawn","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":780086,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Reid, Don","contributorId":221733,"corporation":false,"usgs":false,"family":"Reid","given":"Don","email":"","affiliations":[],"preferred":false,"id":780084,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"DeLapp, John","contributorId":221732,"corporation":false,"usgs":false,"family":"DeLapp","given":"John","email":"","affiliations":[],"preferred":false,"id":780083,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Burton, Phil","contributorId":221736,"corporation":false,"usgs":false,"family":"Burton","given":"Phil","email":"","affiliations":[],"preferred":false,"id":780087,"contributorType":{"id":2,"text":"Editors"},"rank":7},{"text":"Schroff, Eric","contributorId":192772,"corporation":false,"usgs":false,"family":"Schroff","given":"Eric","email":"","affiliations":[],"preferred":false,"id":780088,"contributorType":{"id":2,"text":"Editors"},"rank":8},{"text":"Barber, Valerie","contributorId":221734,"corporation":false,"usgs":false,"family":"Barber","given":"Valerie","email":"","affiliations":[],"preferred":false,"id":780085,"contributorType":{"id":2,"text":"Editors"},"rank":9}]}}
,{"id":70228287,"text":"70228287 - 2020 - Predicting wildlife distribution patterns in New England USA with expert elicitation techniques","interactions":[],"lastModifiedDate":"2022-02-09T12:04:24.462319","indexId":"70228287","displayToPublicDate":"2019-11-30T15:56:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Predicting wildlife distribution patterns in New England USA with expert elicitation techniques","docAbstract":"<p><span>Understanding the impacts of landscape change on species distributions can help inform decision-making and&nbsp;conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop&nbsp;species distribution models&nbsp;(SDMs) for harvested wildlife species (n&nbsp;=&nbsp;10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. The models were in general agreement with the literature and provided effect sizes for variables that shape species occurrence. With the exception of gray fox, models performed well when validated against crowdsourced empirical data. We applied models to&nbsp;rasters&nbsp;(30&nbsp;×&nbsp;30&nbsp;m cells) of the New England region to map each species’ distribution. Average regional occurrence probability was highest for coyote (0.92) and white-tailed&nbsp;deer&nbsp;(0.89) and lowest for gray fox (0.42) and moose (0.52). We then stacked distribution maps of each species to estimate and map focal species richness. Species richness (</span><i>s</i><span>) varied across New England, with highest average richness in the least developed states of Vermont (</span><i>s</i><span>&nbsp;=&nbsp;7.47) and Maine (s&nbsp;=&nbsp;7.32), and lowest average richness in the most developed states of Rhode Island (s&nbsp;=&nbsp;6.13) and Massachusetts (s&nbsp;=&nbsp;6.61). Our expert-based approach provided relatively inexpensive, comprehensive information that would have otherwise been difficult to obtain given the spatial extent and range of species being assessed. The results provide valuable information about the current distribution of wildlife species and offer a means of exploring how climate and land-use change may impact wildlife in the future.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2019.e00853","usgsCitation":"Pearman-Gillman, S.B., Katz, J.E., Mickey, R., Murdoch, J.D., and Donovan, T.M., 2020, Predicting wildlife distribution patterns in New England USA with expert elicitation techniques: Global Ecology and Conservation, v. 21, e00853, 19 p., https://doi.org/10.1016/j.gecco.2019.e00853.","productDescription":"e00853, 19 p.","ipdsId":"IP-110199","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":458424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00853","text":"Publisher Index Page"},{"id":395658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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 \"}}]}","volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pearman-Gillman, Schuyler B. spearman-gillman@usgs.gov","contributorId":275070,"corporation":false,"usgs":false,"family":"Pearman-Gillman","given":"Schuyler","email":"spearman-gillman@usgs.gov","middleInitial":"B.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":833613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Jonathan E.","contributorId":275072,"corporation":false,"usgs":false,"family":"Katz","given":"Jonathan","email":"","middleInitial":"E.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":833614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mickey, Ruth M.","contributorId":275073,"corporation":false,"usgs":false,"family":"Mickey","given":"Ruth M.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":833615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James D.","contributorId":275075,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":833616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833612,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70228329,"text":"70228329 - 2020 - Energetic status of Alaskan Chinook Salmon: Interpopulation comparisons and predictive modeling using bioelectrical impedance analysis","interactions":[],"lastModifiedDate":"2022-02-10T12:17:31.332188","indexId":"70228329","displayToPublicDate":"2019-11-29T15:41:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Energetic status of Alaskan Chinook Salmon: Interpopulation comparisons and predictive modeling using bioelectrical impedance analysis","docAbstract":"<p>Adult Pacific Salmon <i>Oncorhynchus spp</i>. undertake energetically demanding migrations wherein they must have adequate energy reserves to survive to spawning locations and reproduce. Proximate analysis provides insight into available energy stores (e.g., lipids), but the ability to non-lethally monitor energetic status may be useful for managers to better understand how energetic status affects salmon populations in light of population declines and threats from climate change and habitat alteration. Chinook Salmon <i>Oncorhynchus tshawytscha</i> (N = 129) were sampled for proximate analysis from four populations in Alaska to examine variation in energetic status pre- and post-spawning migration and to create predictive bioelectrical impedance analysis (BIA) models for this species. We also tested two BIA devices (Q2 and CQR), whether models were generalizable to a con-specific (Chum Salmon Oncorhynchus keta), and the feasibility of integrating BIA into field studies. Populations sampled pre- spawning migration had higher percent lipid (N = 77; mean = 42.57%) than those collected post spawning migration (N = 52; mean = 19.71%). Total percent lipid and water were more accurately predicted from the Q2 device based on BIA measurements (RMSE = 5.33; RMSE = 2.43, respectively), relative to CQR device measurements (RMSE = 6.27; RMSE = 2.66). Between-species (Chinook to Chum RMSE = 19.47; Chum to Chinook RMSE = 7.69) models were less accurate than species specific models created for Chinook and Chum Salmon, therefore single species models should be used. We field-tested the BIA model to predict Chinook Salmon %lipid and %water on a remote Southeast Alaska river. Techniques were quickly taught to field crews and predictions were similar to other pre-spawning migration estimates. Our results indicate that integration of BIA into population monitoring could be a valuable tool to assess spatial and temporal patterns of energetic status of Chinook Salmon.</p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10398","usgsCitation":"Courtney, K.R., Falke, J.A., Cox, M., and Nichols, J., 2020, Energetic status of Alaskan Chinook Salmon: Interpopulation comparisons and predictive modeling using bioelectrical impedance analysis: North American Journal of Fisheries Management, v. 40, no. 1, p. 209-224, https://doi.org/10.1002/nafm.10398.","productDescription":"16 p.","startPage":"209","endPage":"224","ipdsId":"IP-098108","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Chena River, Delta River, Emmonak, Nushagak River, Stikine River, Tanana River, Yukon River, Whitman Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -528.7939453125,\n              52.96187505907603\n            ],\n            [\n              -486.8701171875,\n              52.96187505907603\n            ],\n            [\n              -486.8701171875,\n              62.61356210229029\n            ],\n            [\n              -528.7939453125,\n              62.61356210229029\n            ],\n            [\n              -528.7939453125,\n              52.96187505907603\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Courtney, Kristin R.","contributorId":275181,"corporation":false,"usgs":false,"family":"Courtney","given":"Kristin","email":"","middleInitial":"R.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":833780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cox, M. Keith","contributorId":275182,"corporation":false,"usgs":false,"family":"Cox","given":"M. Keith","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":833782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, Jeff","contributorId":275183,"corporation":false,"usgs":false,"family":"Nichols","given":"Jeff","email":"","affiliations":[{"id":54573,"text":"AK FG","active":true,"usgs":false}],"preferred":false,"id":833783,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227251,"text":"70227251 - 2020 - Brook trout (Salvelinus fontinalis) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin","interactions":[],"lastModifiedDate":"2022-01-05T15:12:55.633931","indexId":"70227251","displayToPublicDate":"2019-11-28T08:44:16","publicationYear":"2020","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":"Brook trout (<i>Salvelinus fontinalis</i>) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin","title":"Brook trout (Salvelinus fontinalis) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin","docAbstract":"<p><span>Dam removals allow fish to access habitats that may provide ecological benefits and risks, but the extent of fish movements through former dam sites has not been thoroughly evaluated for many species. We installed stationary PIT antennas in 2016 and 2017 to evaluate movements and survival of brook trout&nbsp;</span><i>Salvelinus fontinalis</i><span>&nbsp;in the West Branch of the Wolf River (WBWR) in central Wisconsin following removal of two dams and channel modifications designed to promote fish movement. These changes provided access to lacustrine habitats that might provide suitable winter habitat or act as ecological sinks. We used multistate models to estimate transition probabilities between river sections, to determine whether brook trout: (a) moved between multiple river sections and (b) entered lacustrine habitats as seasonal refuges, but eventually returned to lotic habitat. We also used a Cormack-Jolly-Seber model to evaluate whether apparent survival of brook trout in the WBWR was comparable to other populations. Few fish moved among river sections or used lacustrine habitat (&lt;5% of tagged fish); most brook trout remained in sections where they were initially tagged, potentially due to quality habitat located throughout the river. Like other studies, brook trout in the WBWR appear to experience high mortality based on low number of detections, few physical recaptures and an estimated eight-month apparent survival rate of 0.27. In scenarios where fish can already access suitable habitat, removal of dams may not result in substantial increases in fish movement and colonisation of newly accessible habitat may not occur immediately.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/eff.12516","usgsCitation":"Easterly, E., Isermann, D.A., Raabe, J.K., and Pyatskowit, J.W., 2020, Brook trout (Salvelinus fontinalis) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin: Ecology of Freshwater Fish, v. 29, no. 2, p. 311-324, https://doi.org/10.1111/eff.12516.","productDescription":"14 p.","startPage":"311","endPage":"324","ipdsId":"IP-107712","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":393914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"West Branch of the Wolf River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.93192291259766,\n              44.9643120983638\n            ],\n            [\n              -88.76300811767578,\n              44.9643120983638\n            ],\n            [\n              -88.76300811767578,\n              45.11859928315532\n            ],\n            [\n              -88.93192291259766,\n              45.11859928315532\n            ],\n            [\n              -88.93192291259766,\n              44.9643120983638\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"29","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Easterly, Emma G.","contributorId":270907,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma G.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":830114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":830113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raabe, Joshua K.","contributorId":270908,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua","email":"","middleInitial":"K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":830115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pyatskowit, Joshua W.","contributorId":270909,"corporation":false,"usgs":false,"family":"Pyatskowit","given":"Joshua","email":"","middleInitial":"W.","affiliations":[{"id":56220,"text":"Menominee Indian Tribe of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":830116,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70216111,"text":"70216111 - 2020 - Cost-effective fuel treatment planning: A theoretical justification and case-study","interactions":[],"lastModifiedDate":"2020-11-05T15:40:42.049467","indexId":"70216111","displayToPublicDate":"2019-11-26T09:32:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Cost-effective fuel treatment planning: A theoretical justification and case-study","docAbstract":"<p><span>Modelling the spatial prioritisation of fuel treatments and their net effect on values at risk is an important area for applied work as economic damages from wildfire continue to grow. We model and demonstrate a cost-effective fuel treatment planning algorithm using two ecosystem services as benefits for which fuel treatments are prioritised. We create a surface of expected fuel treatment costs to incorporate the heterogeneity in factors affecting the revenue and costs of fuel treatments, and then prioritise treatments based on a cost-effectiveness ratio to maximise the averted loss of ecosystem services from fire. We compare treatment scenarios that employ cost-effectiveness with those that do not, and use common tools and models in a case study of the Sisters Ranger District on the Deschutes National Forest in central Oregon, USA. Using cost-effectiveness not only increases the expected averted losses from fuel treatments, but it also allows a larger area to be treated for the same cost, simply by incorporating costs and cost-effectiveness into the prioritisation routine. These results have considerable implications for policymakers and land managers trying to minimise risk. Incorporating costs into the spatial planning of treatments could allow more effective outcomes without increasing fuel treatment budgets.</span></p>","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF18187","usgsCitation":"Kreitler, J.R., Thompson, M., Vaillant, N., and Hawbaker, T., 2020, Cost-effective fuel treatment planning: A theoretical justification and case-study: International Journal of Wildland Fire, v. 29, no. 1, p. 42-56, https://doi.org/10.1071/WF18187.","productDescription":"15 p.","startPage":"42","endPage":"56","ipdsId":"IP-070393","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":380195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Deschutes National Forest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.0416259765625,\n              43.35514118114017\n            ],\n            [\n              -120.69305419921874,\n              43.35514118114017\n            ],\n            [\n              -120.69305419921874,\n              44.40042951858466\n            ],\n            [\n              -122.0416259765625,\n              44.40042951858466\n            ],\n            [\n              -122.0416259765625,\n              43.35514118114017\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"29","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":804149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Matthew","contributorId":177098,"corporation":false,"usgs":false,"family":"Thompson","given":"Matthew","affiliations":[],"preferred":false,"id":804150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaillant, Nicole","contributorId":140987,"corporation":false,"usgs":false,"family":"Vaillant","given":"Nicole","affiliations":[{"id":13638,"text":"Western Wildland environmental threat assessment Center","active":true,"usgs":false}],"preferred":false,"id":804151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":804152,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208075,"text":"70208075 - 2020 - Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California","interactions":[],"lastModifiedDate":"2020-01-27T20:03:27","indexId":"70208075","displayToPublicDate":"2019-11-25T20:02:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California","docAbstract":"Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage‐grouse Centrocercus urophasianus (hereafter, sage‐grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage‐grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage‐grouse across multiple study sites (1,084 sage‐grouse; 30,690 locations) in the Great Basin. We developed broad‐scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage‐grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage‐grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint–index framework by combining seasonal habitat indices with a probabilistic index of sage‐grouse abundance and space use to produce habitat maps useful for sage‐grouse management. These products can serve as conservation planning tools that help predict expected benefits of restoration activities, while highlighting areas most critical to sustaining sage‐grouse populations. Our joint–index framework can be applied to other species that exhibit seasonal shifts in habitat requirements to help better guide conservation actions.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5842","usgsCitation":"Coates, P.S., Brussee, B.E., Ricca, M.A., Severson, J., Casazza, M.L., Gustafson, K.B., Espinosa, S.P., Gardner, S.C., and Delahunty, D.J., 2020, Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California: Ecology and Evolution, v. 10, no. 1, p. 104-118, https://doi.org/10.1002/ece3.5842.","productDescription":"15 p.","startPage":"104","endPage":"118","ipdsId":"IP-106342","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458449,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5842","text":"Publisher Index Page"},{"id":371630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.134765625,\n              36.61552763134925\n            ],\n            [\n              -114.06005859375,\n              36.61552763134925\n            ],\n            [\n              -114.06005859375,\n              41.96765920367816\n            ],\n            [\n              -123.134765625,\n              41.96765920367816\n            ],\n            [\n              -123.134765625,\n              36.61552763134925\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Severson, John","contributorId":221819,"corporation":false,"usgs":true,"family":"Severson","given":"John","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780366,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":780367,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":780368,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delahunty, David J","contributorId":221820,"corporation":false,"usgs":false,"family":"Delahunty","given":"David","email":"","middleInitial":"J","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":780369,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70207946,"text":"70207946 - 2020 - Dispersal asymmetry in a two-patch system with source–sink populations","interactions":[],"lastModifiedDate":"2020-02-13T12:53:00","indexId":"70207946","displayToPublicDate":"2019-11-25T15:53:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3593,"text":"Theoretical Population Biology","active":true,"publicationSubtype":{"id":10}},"title":"Dispersal asymmetry in a two-patch system with source–sink populations","docAbstract":"<p><span>This paper analyzes source–sink systems with asymmetric dispersal between two patches. Complete analysis on the models demonstrates a mechanism by which the dispersal asymmetry can lead to either an increased total size of the species population in two patches, a decreased total size with persistence in the patches, or even extinction in both patches. For a large growth rate of the species in the source and a fixed dispersal intensity, (i) if the asymmetry is small, the population would persist in both patches and reach a density higher than that without dispersal, in which the population approaches its maximal density at an appropriate asymmetry; (ii) if the asymmetry is intermediate, the population persists in both patches but reaches a density less than that without dispersal; (iii) if the asymmetry is large, the population goes to extinction in both patches; (iv) asymmetric dispersal is more favorable than symmetric dispersal under certain conditions. For a fixed asymmetry, similar phenomena occur when the dispersal intensity varies, while a thorough analysis is given for the low growth rate of the species in the source. Implications for populations in heterogeneous landscapes are discussed, and numerical simulations confirm and extend our results.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.tpb.2019.11.004","usgsCitation":"Wu, H., Wang, Y., Li, Y., and DeAngelis, D.L., 2020, Dispersal asymmetry in a two-patch system with source–sink populations: Theoretical Population Biology, v. 131, p. 54-65, https://doi.org/10.1016/j.tpb.2019.11.004.","productDescription":"12 p.","startPage":"54","endPage":"65","ipdsId":"IP-110121","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":371400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wu, Hong","contributorId":21443,"corporation":false,"usgs":true,"family":"Wu","given":"Hong","affiliations":[],"preferred":false,"id":779846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Yuanshi","contributorId":207814,"corporation":false,"usgs":false,"family":"Wang","given":"Yuanshi","email":"","affiliations":[{"id":37637,"text":"School of Mathematics and Computational Science Sun Yat-sen University","active":true,"usgs":false}],"preferred":false,"id":779847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Yufeng","contributorId":221687,"corporation":false,"usgs":false,"family":"Li","given":"Yufeng","email":"","affiliations":[],"preferred":false,"id":779848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":779849,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206909,"text":"70206909 - 2020 - The future of barriers and trapping methods in the sea lamprey (Petromyzon marinus) control program in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2020-03-11T14:17:15","indexId":"70206909","displayToPublicDate":"2019-11-25T08:20:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The future of barriers and trapping methods in the sea lamprey (<i>Petromyzon marinus</i>) control program in the Laurentian Great Lakes","title":"The future of barriers and trapping methods in the sea lamprey (Petromyzon marinus) control program in the Laurentian Great Lakes","docAbstract":"<p><span>A major duty of the Great Lakes Fishery Commission (GLFC), created in 1955, was the development a program of eradication or management of sea lamprey populations in the Great Lakes for the protection of the Great Lakes fishery. Beginning in the 1980s the GLFC shifted sea lamprey control to an integrated pest management model seeking to deploy control measures which target multiple life stages. Currently control efforts focus on limiting the area of infestation using barriers to migratory adults and eradication of larvae from streams using selective pesticides. Feedback on program effectiveness is obtained by trapping migratory adult lamprey at a series of index sites around the basin. The GLFC continues to support multiple research initiatives to develop additional control, improve current control measures, and further advance the sea lamprey control program. During the past six decades sea lamprey control in the Great Lakes has evolved as the research program has identified technological advances. Here we summarize the current state and recent advancements for two of the sea lamprey control program’s core elements, barriers and traps, highlight challenges to be addressed to continue the advancement of these program elements, and provide a series of research questions to spur interest within the research community. Further, because considerable information about these program elements is scattered among grey literature and technical reports, we summarize the history of barriers and traps in sea lamprey control in the included appendices to provide relevant program background to anyone interested in pursuing these research topics.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11160-019-09587-7","usgsCitation":"Miehls, S.M., Paul Sullivan, Michael Twohey, Barber, J., and McDonald, R., 2020, The future of barriers and trapping methods in the sea lamprey (Petromyzon marinus) control program in the Laurentian Great Lakes: Reviews in Fish Biology and Fisheries, v. 30, p. 1-24, https://doi.org/10.1007/s11160-019-09587-7.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-102162","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":458454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11160-019-09587-7","text":"Publisher Index Page"},{"id":369691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Great Lakes","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.07617187499999,\n              41.11246878918088\n            ],\n            [\n              -75.8056640625,\n              41.11246878918088\n            ],\n            [\n              -75.8056640625,\n              49.35375571830993\n            ],\n            [\n              -93.07617187499999,\n              49.35375571830993\n            ],\n            [\n              -93.07617187499999,\n              41.11246878918088\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":776224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paul Sullivan","contributorId":141118,"corporation":false,"usgs":false,"family":"Paul Sullivan","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":776225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael Twohey","contributorId":220931,"corporation":false,"usgs":false,"family":"Michael Twohey","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":776226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barber, Jessica","contributorId":220932,"corporation":false,"usgs":false,"family":"Barber","given":"Jessica","email":"","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":776227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Rodney","contributorId":220933,"corporation":false,"usgs":false,"family":"McDonald","given":"Rodney","email":"","affiliations":[{"id":40297,"text":"Retired Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":776228,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207371,"text":"70207371 - 2020 - History and sources of co-occurring pesticides in an abstraction well unravelled by age distributions of depth specific groundwater samples","interactions":[],"lastModifiedDate":"2020-01-08T14:42:44","indexId":"70207371","displayToPublicDate":"2019-11-24T19:42:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"History and sources of co-occurring pesticides in an abstraction well unravelled by age distributions of depth specific groundwater samples","docAbstract":"When groundwater-based drinking water supply becomes contaminated, the timing and source of contamination are obvious questions. However, contaminants often have diffuse sources and different contaminants may have different sources even in a single groundwater well, making these questions complicated to answer. Age dating of groundwater has been used to reconstruct contaminant travel times to wells; however, critics have highlighted that groundwater flow is often complex with mixing of groundwater of different ages. In drinking water wells, where water is typically abstracted from a large depth interval, such mixing is even more problematic. We present a way to overcome some of the obstacles in identifying the source and age of\n contaminants in drinking water wells by combining depth-specific sampling with age tracer modeling, particle tracking simulations, geological characterization, and contaminant properties. This multitool approach was applied to a drinking water well, where bentazon and dichlorprop contamination was found to have different pollutant sources and release histories, even though both pesticides can be associated with the same land use. Bentazon was derived from recent\napplication to a golf course, while dichlorprop was derived from agricultural use more than 30 years ago. The advantages, limitations, and pitfalls of the proposed course of action are then further discussed.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b03996","usgsCitation":"Jakobsen, R., Hinsby, K., Aamand, J., van der Keur, P., Kidmose, J., Purtschert, R., Jurgens, B., Sultenfuss, J., and Albers, C.N., 2020, History and sources of co-occurring pesticides in an abstraction well unravelled by age distributions of depth specific groundwater samples: Environmental Science & Technology, v. 54, no. 1, p. 158-165, https://doi.org/10.1021/acs.est.9b03996.","productDescription":"8 p.","startPage":"158","endPage":"165","ipdsId":"IP-110509","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":370438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jakobsen, Rasmus 0000-0003-1882-2961","orcid":"https://orcid.org/0000-0003-1882-2961","contributorId":221322,"corporation":false,"usgs":false,"family":"Jakobsen","given":"Rasmus","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hinsby, Klaus 0000-0003-1190-4550","orcid":"https://orcid.org/0000-0003-1190-4550","contributorId":221323,"corporation":false,"usgs":false,"family":"Hinsby","given":"Klaus","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aamand, Jens 0000-0002-4641-639X","orcid":"https://orcid.org/0000-0002-4641-639X","contributorId":221324,"corporation":false,"usgs":false,"family":"Aamand","given":"Jens","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van der Keur, Peter 0000-0001-6988-6266","orcid":"https://orcid.org/0000-0001-6988-6266","contributorId":221325,"corporation":false,"usgs":false,"family":"van der Keur","given":"Peter","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kidmose, Jacob 0000-0001-8577-2197","orcid":"https://orcid.org/0000-0001-8577-2197","contributorId":221326,"corporation":false,"usgs":false,"family":"Kidmose","given":"Jacob","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Purtschert, Roland 0000-0002-4734-7664","orcid":"https://orcid.org/0000-0002-4734-7664","contributorId":221327,"corporation":false,"usgs":false,"family":"Purtschert","given":"Roland","email":"","affiliations":[{"id":38843,"text":"University of Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":777849,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777843,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sultenfuss, Jurgen","contributorId":221328,"corporation":false,"usgs":false,"family":"Sultenfuss","given":"Jurgen","email":"","affiliations":[{"id":40351,"text":"University of Bremen, Germany","active":true,"usgs":false}],"preferred":true,"id":777850,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Albers, Christian N. 0000-0001-7253-3509","orcid":"https://orcid.org/0000-0001-7253-3509","contributorId":221329,"corporation":false,"usgs":false,"family":"Albers","given":"Christian","email":"","middleInitial":"N.","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":777851,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70211498,"text":"70211498 - 2020 - Interactive range‐limit theory (iRLT): An extension for predicting range shifts","interactions":[],"lastModifiedDate":"2020-07-29T13:41:27.897824","indexId":"70211498","displayToPublicDate":"2019-11-23T19:08:42","publicationYear":"2020","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":"Interactive range‐limit theory (iRLT): An extension for predicting range shifts","docAbstract":"<ol class=\"\"><li>A central theme of range‐limit theory (RLT) posits that abiotic factors form high‐latitude/altitude limits, whereas biotic interactions create lower limits. This hypothesis, often credited to Charles Darwin, is a pattern widely assumed to occur in nature. However, abiotic factors can impose constraints on both limits and there is scant evidence to support the latter prediction. Deviations from these predictions may arise from correlations between abiotic factors and biotic interactions, as a lack of data to evaluate the hypothesis, or be an artifact of scale. Combining two tenets of ecology—niche theory and predator–prey theory—provides an opportunity to understand how biotic interactions influence range limits and how this varies by trophic level.</li><li>We propose an expansion of RLT, interactive RLT (iRLT), to understand the causes of range limits and predict range shifts. Incorporating the main predictions of Darwin's hypothesis, iRLT hypothesizes that abiotic and biotic factors can interact to impact both limits of a species’ range. We summarize current thinking on range limits and perform an integrative review to evaluate support for iRLT and trophic differences along range margins, surveying the mammal community along the boreal‐temperate and forest‐tundra ecotones of North America.</li><li>Our review suggests that range‐limit dynamics are more nuanced and interactive than classically predicted by RLT. Many (57 of 70) studies indicate that biotic factors can ameliorate harsh climatic conditions along high‐latitude/altitude limits. Conversely, abiotic factors can also mediate biotic interactions along low‐latitude/altitude limits (44 of 68 studies). Both scenarios facilitate range expansion, contraction or stability depending on the strength and the direction of the abiotic or biotic factors. As predicted, biotic interactions most often occurred along lower limits, yet there were trophic differences. Carnivores were only limited by competitive interactions (<i>n<span>&nbsp;</span></i>&nbsp;=&nbsp;25), whereas herbivores were more influenced by predation and parasitism (77%; 55 of 71 studies). We highlight how these differences may create divergent range patterns along lower limits.</li><li>We conclude by (a) summarizing iRLT; (b) contrasting how our model system and others fit this hypothesis and (c) suggesting future directions for evaluating iRLT.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13150","usgsCitation":"Siren, A., and Morelli, T.L., 2020, Interactive range‐limit theory (iRLT): An extension for predicting range shifts: Journal of Animal Ecology, v. 89, no. 4, p. 940-954, https://doi.org/10.1111/1365-2656.13150.","productDescription":"15 p.","startPage":"940","endPage":"954","ipdsId":"IP-112467","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":458458,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.13150","text":"Publisher Index Page"},{"id":376817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Siren, Alexej P. K.","contributorId":236810,"corporation":false,"usgs":false,"family":"Siren","given":"Alexej P. K.","affiliations":[],"preferred":false,"id":794352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794353,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218479,"text":"70218479 - 2020 - Deposition potential and flow-response dynamics of emergent sandbars in a braided river","interactions":[],"lastModifiedDate":"2021-03-02T13:01:45.819116","indexId":"70218479","displayToPublicDate":"2019-11-23T08:35:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Deposition potential and flow-response dynamics of emergent sandbars in a braided river","docAbstract":"<p><span>Sandbars are ubiquitous in sandy‐braided rivers throughout the world. In the Great Plains of the United States, recovery and expansion of emergent sandbar habitat (ESH) has been a priority in lowland rivers where the natural extent of sandbars has been degraded. Recovery efforts are aimed at protection of populations of the interior least tern (</span><i>Sterna antillarum</i><span>) and piping plover (</span><i>Charadrius melodus</i><span>). But quantitative observations of deposition and erosion dynamics of populations of sandbars across long segments of rivers are rare. We present a three‐part case study which used Bayesian regression models to examine relations between hydrology, channel morphology, and ESH responses in the Platte River, eastern Nebraska. Logistic regression indicates presence of ESH is positively related to the Parker, (1976) stability criterion and a gradient in sediment transport mode, and negatively related to presence of vegetation. Hierarchical linear regression modeling shows direct coupling between sandbar top‐surface height and formative flood magnitude, but the gap between formative flood stage and sandbar top‐surface increases with increasing discharge. Finally, linear regression modeling of sandbar erosion demonstrates rates of ESH erosion are on the order of 10</span><sup>−1</sup><span>&nbsp;ha/day during high‐flow periods and 10</span><sup>−2</sup><span>&nbsp;during low‐flow periods, but sandbar persistence is largely a function of sandbar starting size. The collective observations highlight the importance of large floods (&gt;3‐year recurrence) in creating very large sandbars that persist as high‐quality ESH over periods of years whereas lower‐magnitude, more‐frequent flood events create lower‐quality ESH that typically does not persist into the following nesting season.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR024107","usgsCitation":"Alexander, J., McElroy, B., Huzurbazar, S., Elliott, C.M., and Murr, M.L., 2020, Deposition potential and flow-response dynamics of emergent sandbars in a braided river: Water Resources Research, v. 56, no. 1, e2018WR024107, 23 p., https://doi.org/10.1029/2018WR024107.","productDescription":"e2018WR024107, 23 p.","ipdsId":"IP-098093","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":383680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Platte River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -99.11865234374999,\n              40.66397287638688\n            ],\n            [\n              -95.8502197265625,\n              40.66397287638688\n            ],\n            [\n              -95.8502197265625,\n              42.11859868281563\n            ],\n            [\n              -99.11865234374999,\n              42.11859868281563\n            ],\n            [\n              -99.11865234374999,\n              40.66397287638688\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":204220,"corporation":false,"usgs":false,"family":"Alexander","given":"Jason S.","affiliations":[{"id":39297,"text":"former U.S. Geological Survey employee","active":true,"usgs":false},{"id":36881,"text":"Department of Geology and Geophysics, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":811168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McElroy, Brandon","contributorId":198820,"corporation":false,"usgs":false,"family":"McElroy","given":"Brandon","affiliations":[],"preferred":false,"id":811169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huzurbazar, Snehalata","contributorId":85903,"corporation":false,"usgs":false,"family":"Huzurbazar","given":"Snehalata","email":"","affiliations":[],"preferred":false,"id":811171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":811170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217731,"text":"70217731 - 2020 - Nitrogen budgets of the Long Island Sound estuary","interactions":[],"lastModifiedDate":"2021-02-01T14:33:51.98955","indexId":"70217731","displayToPublicDate":"2019-11-22T10:02:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen budgets of the Long Island Sound estuary","docAbstract":"<p><span>Nitrogen (N) inputs to coastal ecosystems have significant impacts on coastal community structure. In N limited systems, increases in N inputs may lead to excess productivity and hypoxia. Like many temperate estuaries, Long Island Sound (LIS), a major eastern U.S. estuary, is a N limited system which has experienced seasonal hypoxia since the 1800s. This study is the first effort to constrain the total N cycle in this estuary. The approach utilizes data collected over the last two decades in the LIS time series with hydrodynamic model results to generate both monthly and annual N budgets between 1995 and 2016. Of the total N that is delivered to LIS through rivers and atmospheric inputs, 40% is exported to the adjacent continental shelf on the order of 10.8&nbsp;±&nbsp;8.9&nbsp;×&nbsp;10</span><sup>6</sup><span>&nbsp;kg&nbsp;N/year. Of this export, 41% is dissolved organic N, 29% is particulate organic N, 32% is nitrate&nbsp;+&nbsp;nitrite, and −3% is ammonium. The remaining 60% of the N delivered to LIS is either buried in sediments or lost through denitrification. This inferred internal loss rate is equivalent to 5.4&nbsp;g&nbsp;N/(m</span><sup>2</sup><span>year). This study serves as an example of the significant inter-annual variations that estuarine budgets undergo as efforts to understand coastal biogeochemical cycles move forward.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2019.106493","usgsCitation":"Vlahos, P., Whitney, M., Menniti, C., Mullaney, J., Morrison, J., and Jia, Y., 2020, Nitrogen budgets of the Long Island Sound estuary: Estuarine, Coastal and Shelf Science, v. 232, 106493, 9 p., https://doi.org/10.1016/j.ecss.2019.106493.","productDescription":"106493, 9 p.","ipdsId":"IP-109478","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437196,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVXGBB","text":"USGS data release","linkHelpText":"Nitrogen concentrations and loads and seasonal nitrogen loads in selected Long Island Sound tributaries, water years 1995-2016"},{"id":382808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, New York","otherGeospatial":"Long Island Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -73.8336181640625,\n              40.77638178482896\n            ],\n            [\n              -73.63037109375,\n              40.81796653313175\n            ],\n            [\n              -73.17993164062499,\n              40.88029480552824\n            ],\n            [\n              -72.61962890625,\n              40.9218144123785\n            ],\n            [\n              -72.3834228515625,\n              40.896905775860006\n            ],\n            [\n              -71.8670654296875,\n              41.05864414643029\n            ],\n            [\n              -71.553955078125,\n              41.15384235711447\n            ],\n            [\n              -71.4605712890625,\n              41.413895564677304\n            ],\n            [\n              -72.1856689453125,\n              41.31907562295139\n            ],\n            [\n              -72.784423828125,\n              41.290189955885644\n            ],\n            [\n              -72.9656982421875,\n              41.269549502842565\n            ],\n            [\n              -73.3447265625,\n              41.1455697310095\n            ],\n            [\n              -73.7677001953125,\n              40.97160353279909\n            ],\n            [\n              -73.8720703125,\n              40.834593138080244\n            ],\n            [\n              -73.8336181640625,\n              40.77638178482896\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"232","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vlahos, Penny","contributorId":191277,"corporation":false,"usgs":false,"family":"Vlahos","given":"Penny","email":"","affiliations":[],"preferred":false,"id":809411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitney, Michael 0000-0002-2048-7755","orcid":"https://orcid.org/0000-0002-2048-7755","contributorId":248577,"corporation":false,"usgs":false,"family":"Whitney","given":"Michael","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":809412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Menniti, Christina","contributorId":248578,"corporation":false,"usgs":false,"family":"Menniti","given":"Christina","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":809413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullaney, John R. 0000-0003-4936-5046","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":203254,"corporation":false,"usgs":true,"family":"Mullaney","given":"John R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809414,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, Jonathan 0000-0002-1756-4609 jmorriso@usgs.gov","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":2274,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","email":"jmorriso@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jia, Yan","contributorId":248579,"corporation":false,"usgs":false,"family":"Jia","given":"Yan","email":"","affiliations":[],"preferred":false,"id":809415,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206881,"text":"70206881 - 2020 - Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","interactions":[],"lastModifiedDate":"2020-04-06T21:07:55.202827","indexId":"70206881","displayToPublicDate":"2019-11-22T06:58:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","docAbstract":"The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2019.1690780","usgsCitation":"Gumma, M.K., Thenkabail, P., Pardhasaradhi Teluguntla, and Oliphant, A., 2020, Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud: GIScience and Remote Sensing, v. 57, no. 3, p. 302-322, https://doi.org/10.1080/15481603.2019.1690780.","productDescription":"21 p.","startPage":"302","endPage":"322","ipdsId":"IP-111091","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2019.1690780","text":"Publisher Index Page"},{"id":369607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India, Pakistan, Bangladesh, Nepal, Sri Lanka, 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Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gumma, Murali Krishna","contributorId":127590,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":776137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pardhasaradhi Teluguntla 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":214457,"corporation":false,"usgs":false,"family":"Pardhasaradhi Teluguntla","affiliations":[{"id":39046,"text":"Bay Area Environmental Research Institute at USGS","active":true,"usgs":false}],"preferred":false,"id":776138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776139,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230128,"text":"70230128 - 2020 - Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River","interactions":[],"lastModifiedDate":"2022-03-30T16:07:49.15533","indexId":"70230128","displayToPublicDate":"2019-11-21T11:02:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River","docAbstract":"<p><span>The Tecopa basin in eastern California was a terminal basin that episodically held lakes during most of the Quaternary until the basin and its modern stream, the Amargosa River, became tributary to Death Valley. Although long studied for its sedimentology, diagenesis, and paleomagnetism, the basin’s lacustrine and paleoclimate history has not been well understood, and conflicting interpretations exist concerning the relations of Tecopa basin to the Amargosa River and to pluvial Lake Manly in Death Valley. Previous studies also did not recognize basinwide tectonic effects on lake-level history. In this study, we focused on: (1) establishing a chronology of shoreline deposits, as the primary indicator of lake-level history, utilizing well-known ash beds and new uranium-series and luminescence dating; (2) using ostracodes as indicators of water chemistry and water source(s); and (3) correlating lake transgressions to well-preserved fluvial-deltaic sequences. During the early Pleistocene, the Tecopa basin hosted small shallow lakes primarily fed by low-alkalinity water sourced mainly from runoff and (or) a groundwater source chemically unlike the modern springs. The first lake that filled the basin occurred just prior and up to the eruption of the 765 ka Bishop ash during marine isotope stage (MIS) 19; this lake heralded the arrival of the Amargosa River, delivering high-alkalinity water. Two subsequent lake cycles, coeval with MIS 16 (leading up to eruption of 631 ka Lava Creek B ash) and MIS 14 and (or) MIS 12, are marked by prominent accumulations of nearshore and beach deposits. The timing of the youngest of these three lakes, the High lake, is constrained by a uranium-series age of ca. 580 ± 120 ka on tufa-cemented beach gravel and by estimates from sedimentation rates. Highstand deposits of the Lava Creek and High lakes at the north end of the basin are stratigraphically tied to distinct sequences of fluvial-deltaic deposits fed by alkaline waters of the Amargosa River. The High lake reached the highest level achieved in the Tecopa basin, and it may have briefly discharged southward but did not significantly erode its threshold. The High lake was followed by a long hiatus of as much as 300 k.y., during which there is evidence for alluvial, eolian, and groundwater-discharge deposition, but no lakes. We attribute this hiatus, as have others, to blockage of the Amargosa River by an alluvial fan upstream near Eagle Mountain. A final lake, the Terminal lake, formed when the river once again flowed south into Tecopa basin, but it was likely short-lived due to rapid incision of the former threshold south of Tecopa. Deposits of the Terminal lake are inset below, and are locally unconformable on, deposits of the High lake and the nonlacustrine deposits of the hiatus. The Terminal lake reached its highstand at ca. 185 ± 21 ka, as dated by infrared-stimulated luminescence on feldspar in beach sand, a time coincident with perennial lake mud and alkaline-tolerant ostracodes in the Badwater core of Lake Manly during MIS 6. A period of stillstand occurred as the Terminal lake drained when the incising river encountered resistant Stirling Quartzite near the head of present-day Amargosa Canyon. Our studies significantly revise the lacustrine and drainage history of the Tecopa basin, show that the MIS 6 highstand was not the largest lake in the basin as previously published (with implications for potential nuclear waste storage at Yucca Mountain, Nevada), and provide evidence from shoreline elevations for ∼20 m of tectonic uplift in the northern part of the basin across an ENE-trending monoclinal flexure.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35282.1","usgsCitation":"Reheis, M.C., Caskey, J., Bright, J., Paces, J.B., Mahan, S.A., and Wan, E., 2020, Pleistocene lakes and paleohydrologic environments of the Tecopa basin, California: Constraints on the drainage integration of the Amargosa River: GSA Bulletin, v. 132, no. 7-8, p. 1537-1565, https://doi.org/10.1130/B35282.1.","productDescription":"29 p.","startPage":"1537","endPage":"1565","ipdsId":"IP-105957","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":397866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Tecopa basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.25,\n              35\n            ],\n            [\n              -115.5,\n              35\n            ],\n            [\n              -115.5,\n              37\n            ],\n            [\n              -117.25,\n              37\n            ],\n            [\n              -117.25,\n              35\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"132","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Reheis, Marith C. 0000-0002-8359-323X mreheis@usgs.gov","orcid":"https://orcid.org/0000-0002-8359-323X","contributorId":138571,"corporation":false,"usgs":true,"family":"Reheis","given":"Marith","email":"mreheis@usgs.gov","middleInitial":"C.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caskey, John","contributorId":289506,"corporation":false,"usgs":false,"family":"Caskey","given":"John","email":"","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":839196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bright, Jordon","contributorId":63981,"corporation":false,"usgs":false,"family":"Bright","given":"Jordon","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":839197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839199,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":3434,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":839200,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208919,"text":"70208919 - 2020 - The ecology of chronic wasting disease in wildlife","interactions":[],"lastModifiedDate":"2020-03-05T10:26:47","indexId":"70208919","displayToPublicDate":"2019-11-21T10:26:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"The ecology of chronic wasting disease in wildlife","docAbstract":"<p><span>Prions are misfolded infectious proteins responsible for a group of fatal neurodegenerative diseases termed transmissible spongiform encephalopathy or prion diseases. Chronic Wasting Disease (CWD) is the prion disease with the highest spillover potential, affecting at least seven Cervidae (deer) species. The zoonotic potential of CWD is inconclusive and cannot be ruled out. A risk of infection for other domestic and wildlife species is also plausible. Here, we review the current status of the knowledge with respect to CWD ecology in wildlife. Our current understanding of the geographic distribution of CWD lacks spatial and temporal detail, does not consider the biogeography of infectious diseases, and is largely biased by sampling based on hunters' cooperation and funding available for each region. Limitations of the methods used for data collection suggest that the extent and prevalence of CWD in wildlife is underestimated. If the zoonotic potential of CWD is confirmed in the short term, as suggested by recent results obtained in experimental animal models, there will be limited accurate epidemiological data to inform public health. Research gaps in CWD prion ecology include the need to identify specific biological characteristics of potential CWD reservoir species that better explain susceptibility to spillover, landscape and climate configurations that are suitable for CWD transmission, and the magnitude of sampling bias in our current understanding of CWD distribution and risk. Addressing these research gaps will help anticipate novel areas and species where CWD spillover is expected, which will inform control strategies. From an ecological perspective, control strategies could include assessing restoration of natural predators of CWD reservoirs, ultrasensitive CWD detection in biotic and abiotic reservoirs, and deer density and landscape modification to reduce CWD spread and prevalence.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/brv.12568","usgsCitation":"Escobar, L.E., Pritzkow, S., Winter, S.N., Grear, D.A., Kirchgessner, M.S., Dominguez-Villegas, E., Machado, G., Peterson, A.T., and Soto, C., 2020, The ecology of chronic wasting disease in wildlife: Biological Reviews, v. 95, no. 2, p. 393-408, https://doi.org/10.1111/brv.12568.","productDescription":"16 p.","startPage":"393","endPage":"408","ipdsId":"IP-107301","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":458473,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/brv.12568","text":"External Repository"},{"id":372946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Finland, Norway, South Korea, Sweden, United States","volume":"95","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Escobar, Luis E.","contributorId":178962,"corporation":false,"usgs":false,"family":"Escobar","given":"Luis","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":784013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pritzkow, Sandra","contributorId":223075,"corporation":false,"usgs":false,"family":"Pritzkow","given":"Sandra","email":"","affiliations":[{"id":40666,"text":"University of Texas Medical School at Houston","active":true,"usgs":false}],"preferred":false,"id":784014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winter, Steven N","contributorId":223076,"corporation":false,"usgs":false,"family":"Winter","given":"Steven","email":"","middleInitial":"N","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":784015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":784012,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirchgessner, Megan S.","contributorId":173866,"corporation":false,"usgs":false,"family":"Kirchgessner","given":"Megan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":784016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dominguez-Villegas, Ernesto","contributorId":223077,"corporation":false,"usgs":false,"family":"Dominguez-Villegas","given":"Ernesto","email":"","affiliations":[{"id":37079,"text":"Wildlife Center of Virginia","active":true,"usgs":false}],"preferred":false,"id":784017,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Machado, Gustavo","contributorId":223078,"corporation":false,"usgs":false,"family":"Machado","given":"Gustavo","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":784018,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peterson, A Townsend","contributorId":223079,"corporation":false,"usgs":false,"family":"Peterson","given":"A","email":"","middleInitial":"Townsend","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":784019,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Soto, Claudio","contributorId":223080,"corporation":false,"usgs":false,"family":"Soto","given":"Claudio","email":"","affiliations":[{"id":40666,"text":"University of Texas Medical School at Houston","active":true,"usgs":false}],"preferred":false,"id":784020,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
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