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Length-at-age data are commonly used to describe growth of fish, and obtaining these data typically involves estimating ages from calcified structures (e.g., fin spines or rays, otoliths, or cleithra). Verifying the accuracy of age and growth estimates for long-lived fish is often difficult because known-age fish are not available for all ages in a population. Mark–recapture methods offer nonlethal alternatives for estimating growth of fish that do not require age data. However, few studies have compared growth trajectories estimated from mark–recapture data with trajectories estimated using the standard von Bertalanffy growth function (VBGF) incorporating length-at-age data from known-age fish. We used a robust data set of Muskellunge Esox masquinongy sampled from Green Bay, Lake Michigan, during 1990–2018 to compare growth trajectories estimated from three mark–recapture models and a VBGF fitted to length-at-age data from known-age individuals. Growth trajectories estimated with mark–recapture models were similar to trajectories estimated with a VBGF using known-age fish. Our results suggest that using recapture of tagged fish provides a viable alternative for describing Muskellunge growth trajectories compared with using ages estimated from calcified structures, where incorrect age estimates represent an additional source of error.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10757","usgsCitation":"Sheffer, R., Hogler, S., and Isermann, D.A., 2022, Mark-recapture models accurately predict growth trajectories of known-age Muskellunge in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 42, no. 2, p. 410-424, https://doi.org/10.1002/nafm.10757.","productDescription":"15 p.","startPage":"410","endPage":"424","ipdsId":"IP-129450","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan, Sturgeon Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.27706007733632,\n 44.35374357415128\n ],\n [\n -86.88196993224543,\n 44.35374357415128\n ],\n [\n -86.88196993224543,\n 45.190198563184424\n ],\n [\n -88.27706007733632,\n 45.190198563184424\n ],\n [\n -88.27706007733632,\n 44.35374357415128\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sheffer, Robert J.","contributorId":349585,"corporation":false,"usgs":false,"family":"Sheffer","given":"Robert J.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogler, Steven R.","contributorId":349586,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven R.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":924492,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227605,"text":"cir1489 - 2022 - National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results","interactions":[],"lastModifiedDate":"2026-03-16T19:44:20.869218","indexId":"cir1489","displayToPublicDate":"2022-02-01T12:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1489","displayTitle":"National Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources—Results","title":"National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results","docAbstract":"

In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO2-EOR) technologies were applied to amenable oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO2 that could be stored (retained) as a result of CO2-EOR activities. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO2. The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The assessment team estimated that the technically recoverable oil associated with CO2-EOR ranges from approximately 25,000 million barrels (MMbbl) at the P5 percentile to as much as 32,000 MMbbl at the P95 percentile, with a mean of 29,000 MMbbl. The associated CO2 retention ranges from approximately 7,400 million metric tons (Mt) at the P5 percentile to as much as 9,500 Mt at the P95 percentile, with a mean of 8,400 Mt. The West Texas and Eastern New Mexico region and the Gulf Coast region together contain 60 percent of the mean assessed CO2-EOR oil potential and 61 percent of the mean assessed CO2 retention. Other regions with significant resource potential include the Midcontinent region and Rocky Mountains and Northern Great Plains region.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1489","programNote":"Energy Resources Program","usgsCitation":"Warwick, P.D., Attanasi, E.D., Blondes, M.S., Brennan, S.T., Buursink, M.L., Cahan, S.M., Doolan, C.A., Freeman, P.A., Karacan, C.Ö., Lohr, C.D., Merrill, M.D., Olea, R.A., Shelton, J.L., Slucher, E.R., and Varela, B.A., 2022, National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources—Results: U.S. Geological Survey Circular 1489, 39 p., https://doi.org/10.3133/cir1489.","productDescription":"Report: viii, 39 p.; Data release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122375","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":501193,"rank":5,"type":{"id":36,"text":"NGMDB 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Program Coordinator, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Telephone: 703–648–6470

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-02-01","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buursink, Marc L. 0000-0001-6491-386X mbuursink@usgs.gov","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":3362,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc","email":"mbuursink@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cahan, Steven M. 0000-0002-4776-3668 scahan@usgs.gov","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":4529,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven","email":"scahan@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831338,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":831339,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Karacan, C. 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Introduction

In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that might be produced by using current carbon dioxide enhanced oil recovery (CO2-EOR) technologies in amenable conventional oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO2 that could be stored (retained) in the assessed oil reservoirs following the application of the CO2-EOR process. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO2. The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The team estimated that the total technically recoverable oil resulting from the application of the CO2-EOR process ranges from approximately 25,000 million barrels (MMbbl) at the P5 percentile to as much as 32,000 MMbbl at the P95 percentile, with a mean of 29,000 MMbbl. The associated CO2 retention ranges from approximately 7,400 million metric tons (Mt) at the P5 percentile to as much as 9,500 Mt at the P95 percentile, with a mean of 8,400 Mt. The results are summarized in this fact sheet and are provided in more detail in the companion data release and circular.

The West Texas and Eastern New Mexico region (primarily its Permian Basin) and the Gulf Coast region together contain 60 percent of the mean assessed CO2-EOR oil potential and 61 percent of the mean assessed CO2 retention. Other regions with significant resource potential include the Midcontinent region and the Rocky Mountains and Northern Great Plains region.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213057","programNote":"Energy Resources Program","usgsCitation":"Warwick, P.D., Attanasi, E.D., Blondes, M.S., Brennan, S.T., Buursink, M.L., Cahan, S.M., Doolan, C.A., Freeman, P.A., Karacan, C.Ö., Lohr, C.D., Merrill, M.D., Olea, R.A., Shelton, J.L., Slucher, E.R., and Varela, B.A., 2022, National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources—Summary: U.S. Geological Survey Fact Sheet 2021–3057, 6 p., https://doi.org/10.3133/fs20213057.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122387","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science 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Program Coordinator, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
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The Living Shoreline Demonstration Project (PO-148) used five bio-engineered reef technologies (Reef Balls in two configurations; Figure 1) acting as breakwaters to protect vulnerable shorelines. While the primary goal is to attenuate wave energy, the sustainability and success of these products as “living” shorelines are based on their ability to enhance oyster habitat, enabling the reef to maintain elevation within the rapidly changing environment (i.e., sea level rise, subsidence). This report documents the recruitment, survival, and growth of the living components of the reef – oysters and other encrusting organisms (e.g. mussels, barnacles). This final technical report provides data from five years of monitoring (November 2017 – December 2021) of reefs located along the western side of Eloi Bay in Pontchartrain Basin (Figure 2). Monitoring goals included assessment of (1) annual oyster densities and population dynamics on the reefs, (2) annual density and diversity of other encrusting organisms, and (3) comparisons of outcomes by reef technology, exposure, and water quality. Detailed information on technologies used, construction design, as-built elevations are available in Coast & Harbor Engineering (2016) Design Memorandum dated March 25, 2016, submitted to Louisiana Coastal Protection and Restoration Authority.

","language":"English","publisher":"U.S. Fish & Wildlife Service","doi":"10.3996/css70529922","usgsCitation":"Swam, L.M., Marshall, D.A., and La Peyre, M., 2022, Five years of monitoring a bio-engineered living shoreline: Comparison of oyster population development by reef technology.: Cooperator Science Series 139-2022, ii, 19 p., https://doi.org/10.3996/css70529922.","productDescription":"ii, 19 p.","ipdsId":"IP-137095","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.38780223527601,\n 29.790840321829222\n ],\n [\n -89.42069143656458,\n 29.790840321829222\n ],\n [\n -89.42069143656458,\n 29.750906524072846\n ],\n [\n -89.38780223527601,\n 29.750906524072846\n ],\n [\n -89.38780223527601,\n 29.790840321829222\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Swam, Lauren M.","contributorId":341585,"corporation":false,"usgs":false,"family":"Swam","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Danielle Aguilar","contributorId":341509,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle","email":"","middleInitial":"Aguilar","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908646,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249400,"text":"70249400 - 2022 - Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","interactions":[],"lastModifiedDate":"2023-10-05T15:52:18.805799","indexId":"70249400","displayToPublicDate":"2022-02-01T10:40:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","docAbstract":"

Based on numerous studies of the relevant geodetic data, a low-angle thrusting mechanism has been assigned to the 1960 Chile earthquake. Kanamori, Rivera and Lambotte recently suggested that a component of dextral slip comparable to the thrusting be included in the mechanism to satisfy long-period, teleseismic observations. The absence of geodetic evidence for that huge strike-slip component is the subject of this comment. The geodetic data are largely measurements of coseismic uplift associated with the earthquake but include eight measurements of the coseismic change in shear strain. Because strike-slip produces relatively little uplift except near the end points of the rupture, identification of that strike-slip component in the geodetic data depends upon the measured, shear-strain change. I consider elastic, half-space models of oblique slip on the plate interface possibly supplemented by simultaneous dextral slip on the nearby, intra-arc Liquiñe-Ofqui Fault Zone. Slip is assumed to be uniform along strike. The best fits to the geodetic data for these models furnish little evidence for strike-slip on those structures. To satisfy the long-period, teleseismic data, Kanamori et al. proposed six examples, each of which requires a large amount of dextral slip. Because the long-period, teleseismic data do not define the slip distributions, I have used the best fits of those examples to the geodetic data to define those distributions. The large thrusting near the deformation front required by those slip distributions implies large uplift there, contrary to the uplift inferred from the inversion of tsunami data. However, an acceptable fit to the geodetic data and the tsunami data for the six examples suggested by Kanamori et al. can be obtained if the seismic moments specified by them are reduced by a factor ∼1.8, a factor within the uncertainties in estimating seismic moments of the 1960 Chile earthquake. The presence of strike-slip in those reduced-moment examples despite the lack of geodetic evidence for strike-slip is due to a remarkable coincidence that requires careful balancing of contributions from the shallower (depths < 70 km) coseismic sources against those from the deeper coseismic sources to nullify the geodetic evidence for strike-slip. Such balancing is possible, but it is remarkable that the balancing is so nearly perfect that it nullifies the geodetic evidence for strike-slip and thereby confounds the interpretation of the geodetic data.

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Lambotte: Geophysical Journal International, v. 228, no. 2, p. 1171-1183, https://doi.org/10.1093/gji/ggab364.","productDescription":"13 p.","startPage":"1171","endPage":"1183","ipdsId":"IP-125867","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":448933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab364","text":"Publisher Index Page"},{"id":421685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.62290268776883,\n -41.93096350040826\n ],\n [\n -73.34570838839286,\n -41.974387007121464\n ],\n [\n -72.9825894437626,\n -41.75429549982772\n ],\n [\n -72.96763150248628,\n -41.60736935174207\n ],\n [\n -72.78858236142116,\n 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surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","interactions":[],"lastModifiedDate":"2022-05-13T14:39:58.812289","indexId":"70227892","displayToPublicDate":"2022-02-01T10:17:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","docAbstract":"

Augmentation of wild populations with captive-bred individuals presents an inherent risk of co-introducing novel pathogens to naïve species, but it can be an important tool for supplementing small or declining populations. Game species used for human enterprise and recreation such as the ring-necked pheasant (Phasianus colchicus) are commonly raised in captivity and released onto public and private wildlands as a method of augmenting naturalized pheasant populations. This study presents findings on pathogen exposure from three sources of serological data collected in California during 2014–2017 including (a) 71 pen-reared pheasants sampled across seven game bird breeding farms, (b) six previously released pen-reared pheasants captured at two study sites where wild pheasants occurred and (c) 79 wild pheasants captured across six study sites. In both pen-reared and wild pheasants, antibodies were detected against haemorrhagic enteritis virus (HEV), infectious laryngotracheitis (ILT), infectious bursal disease virus (IBDV), paramyxovirus type 1 (PMV-1) and Pasteurella multocida (PM). Previously released pen-reared pheasants were seropositive for HEV, ILT, and PM. Generalized linear mixed models accounting for intraclass correlation within groups indicated that pen-reared pheasants were more than twice as likely to test positive for HEV antibodies. Necropsy and ancillary diagnostics were performed in addition to serological testing on 40 pen-reared pheasants sampled from five of the seven farms. Pheasants from three of these farms tested positive by PCR for Siadenovirus, the causative agent of both haemorrhagic enteritis in turkeys and marble spleen disease of pheasants, which are serologically indistinguishable. Following necropsy, owners from the five farms were surveyed regarding husbandry and biosecurity practices. Farms ranged in size from 10,000 to more than 100,000 birds, two farms raised other game bird species on premises, and two farms used some form of vaccination. Biosecurity practices varied by farm, but the largest farm implemented the strictest practices.

","language":"English","publisher":"Wiley","doi":"10.1111/tbed.14068","usgsCitation":"Dwight, I., Coates, P.S., Stoute, S.T., and Pitesky, M.E., 2022, Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants: Transboundary and Emerging Diseases, v. 69, no. 3, p. 1095-1107, https://doi.org/10.1111/tbed.14068.","productDescription":"13 p.","startPage":"1095","endPage":"1107","ipdsId":"IP-119580","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/tbed.14068","text":"Publisher Index Page"},{"id":395209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento and San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.33349609375,\n 34.88593094075317\n ],\n [\n -118.71826171875,\n 35.191766965947394\n ],\n [\n -118.41064453125,\n 35.871246850027966\n ],\n [\n -119.44335937499999,\n 37.3002752813443\n ],\n [\n -120.80566406250001,\n 39.14710270770074\n ],\n [\n -121.53076171875,\n 40.51379915504413\n ],\n [\n -120.43212890625,\n 41.244772343082076\n ],\n [\n -120.4541015625,\n 42.01665183556825\n ],\n [\n -122.32177734375,\n 42.032974332441405\n ],\n [\n -122.9150390625,\n 41.07935114946899\n ],\n [\n -122.87109375,\n 39.26628442213066\n ],\n [\n -121.88232421875,\n 37.75334401310656\n ],\n [\n -120.73974609374999,\n 36.155617833818525\n ],\n [\n -119.33349609375,\n 34.88593094075317\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"69","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Dwight, Ian 0000-0002-8393-5391 idwight@usgs.gov","orcid":"https://orcid.org/0000-0002-8393-5391","contributorId":192077,"corporation":false,"usgs":true,"family":"Dwight","given":"Ian","email":"idwight@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":832482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":832483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoute, Simone T.","contributorId":202770,"corporation":false,"usgs":false,"family":"Stoute","given":"Simone","email":"","middleInitial":"T.","affiliations":[{"id":36526,"text":"California Animal Health and Food Safety Laboratory","active":true,"usgs":false}],"preferred":false,"id":832484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pitesky, Maurice E.","contributorId":176920,"corporation":false,"usgs":false,"family":"Pitesky","given":"Maurice","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":832485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227893,"text":"70227893 - 2022 - Management foundations for navigating ecological transformation by resisting, accepting, or directing social-ecological change","interactions":[],"lastModifiedDate":"2022-02-02T14:24:42.435166","indexId":"70227893","displayToPublicDate":"2022-02-01T09:58:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Management foundations for navigating ecological transformation by resisting, accepting, or directing social-ecological change","docAbstract":"Despite striking global change, management to ensure healthy landscapes and sustained natural resources has tended to set objectives on the basis of the historical range of variability in stationary ecosystems. Many social–ecological systems are moving into novel conditions that can result in ecological transformation. We present four foundations to enable a transition to future-oriented conservation and management that increases capacity to manage change. The foundations are to identify plausible social–ecological trajectories, to apply upstream and deliberate engagement and decision-making with stakeholders, to formulate management pathways to desired futures, and to consider a portfolio approach to manage risk and account for multiple preferences across space and time. 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Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.

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We present results of slow (multiple day) depressurization experiments of pressure cores recovered from Green Canyon Block 955 in the northern Gulf of Mexico during The University of Texas at Austin Hydrate Pressure Coring Expedition (UT-GOM2-1). These stepwise depressurization experiments monitored the pressure and temperature within the core storage chamber during each pressure step, or “shut-in” period to better understand dissociation behavior and to provide insight on the thermodynamic state of gas hydrate reservoirs during production. The pressure rebound that occurs in response to a depressurization step occurs more slowly during later dissociation steps, likely reflecting a slower heat transfer rate, decreasing salinity gradient, and increased compressibility of the pore and surrounding fluids with progressive dissociation. We demonstrate that displacement of water by gas within the core storage chamber during successive dissociations both insulates the core and increases the compressibility of the pore and chamber fluid. The increased compressibility requires that a larger hydrate volume dissociates per unit of pressure recovery. Pressures observed during progressive dissociation steps are lower than predicted by the sample’s average salinity, with pressures approaching the freshwater phase boundary during frequent dissociation steps, suggesting that local pore-water freshening strongly influences dissociation behavior. To avoid underestimating the magnitude of pressure drawdown required to sustain dissociation in the reservoir, we suggest that hydrate production models use the freshwater phase boundary rather than a phase boundary determined from bulk salinity.

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On the Ground

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Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of flood damage probability for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (n = 71,434) and a suite of geospatial predictors (e.g., flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a flood damage probability for each 100-m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g., forest) resulting in higher error rates than others (e.g., wetlands). Our results identified flood damage probability hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating flood damage probability estimates across the nation.

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workbook","interactions":[],"lastModifiedDate":"2022-03-03T14:52:52.979222","indexId":"70229242","displayToPublicDate":"2022-02-01T08:44:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":8915,"text":"EPA Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"EPA/600/R-21/306","title":"Action plan for restoration of coral reef coastal protection services: Case study example and workbook","docAbstract":"

This report was prepared by the U.S. Environmental Protection Agency (USEPA), Office of Research and Development, as part of the Air, Climate and Energy (ACE) research program, with support from Tetra Tech, Inc., and in collaboration with the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, and The Nature Conservancy. The ACE research program provides scientific information and tools to support USEPA’s commitment to clean air, clean water and sustainable natural resources, even as environmental conditions change. A key component of this is the development of sound science to support adaptation. Adaptation involves preparing for and adjusting to the effects of climate change and its interactions with other global and local stressors. Because these effects are diverse, interactive, and difficult to predict, adapting management of natural resources in this context can be very challenging.

Coral reefs—which provide valued ecosystem services such as fisheries, coastal protection, and tourism—are threatened by the effects of increased sea surface temperatures, sea level rise, and intensifying storms. These large-scale stressors are interacting with local stressors such as pollution, overfishing, and recreational misuse to drive ongoing and accelerating declines in coral reef ecosystems. Thus, there is a rising urgency to design and implement climate change adaptation measures that will enable reef resilience in the face of these changes. This includes accounting for, and adjusting to, the combined effects of climate change and local stressors in coral reef protection and restoration efforts.

The action plan, example case study, and workbook found in this report demonstrate a structured process for integrating climate-smart design considerations into restoration planning using A Manager’s Guide to Coral Reef Restoration Planning and Design. The focus is a hypothetical coral reef restoration project that has a goal of recovering nature-based coastal protection services using restoration interventions. The intent is to provide readers with a completed example of how to use the Guide workbook to inform a draft action plan, centering on the topic of coastal protection as a burgeoning area of interest in coral reef science and management communities. The information in this hypothetical case study is not intended for direct use; rather, it provides a starting point for more detailed planning that would occur in specific places. And while a full review of the current literature on reef restoration methods is outside the scope of this report, readers are encouraged to use the examples herein as well as in the Guide as a jumping-off point for exploring the rapidly growing body of information on methods, techniques, successes, failures, monitoring challenges and future directions of coral reef restoration in a changing world. The workbook, together with the action plan, can serve as a valuable record of the planning thought process as well as a living document for adaptive management, to be updated through time as improved information becomes available.

","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Courtney, C.A., West, J.M., Storlazzi, C.D., Viehman, T.S., Czaplinski, R., Hague, E., and Shaver, E.C., 2022, Action plan for restoration of coral reef coastal protection services: Case study example and workbook: EPA Report EPA/600/R-21/306, 73 p.","productDescription":"73 p.","ipdsId":"IP-130997","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396695,"type":{"id":11,"text":"Document"},"url":"https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=544353&Lab=CPHEA"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Courtney, Catherine A.","contributorId":287687,"corporation":false,"usgs":false,"family":"Courtney","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":61627,"text":"TetraTech, Inc.","active":true,"usgs":false}],"preferred":false,"id":837024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"West, Jordon M.","contributorId":287688,"corporation":false,"usgs":false,"family":"West","given":"Jordon","email":"","middleInitial":"M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":837025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viehman, T. Shay","contributorId":259297,"corporation":false,"usgs":false,"family":"Viehman","given":"T.","email":"","middleInitial":"Shay","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":837027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Czaplinski, Richard","contributorId":287689,"corporation":false,"usgs":false,"family":"Czaplinski","given":"Richard","email":"","affiliations":[{"id":39152,"text":"TetraTech","active":true,"usgs":false}],"preferred":false,"id":837028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hague, Erin","contributorId":287690,"corporation":false,"usgs":false,"family":"Hague","given":"Erin","email":"","affiliations":[{"id":39152,"text":"TetraTech","active":true,"usgs":false}],"preferred":false,"id":837029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaver, Elizabeth C.","contributorId":287691,"corporation":false,"usgs":false,"family":"Shaver","given":"Elizabeth","email":"","middleInitial":"C.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":837030,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227733,"text":"sir20215139 - 2022 - Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","interactions":[],"lastModifiedDate":"2026-04-08T16:30:38.872221","indexId":"sir20215139","displayToPublicDate":"2022-02-01T08:15:45","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5139","displayTitle":"Simulation of Groundwater and Surface-Water Resources of the San Antonio Creek Valley Watershed, Santa Barbara County, California","title":"Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","docAbstract":"

In the San Antonio Creek Valley watershed (SACVW), western Santa Barbara County, California, groundwater is the primary source of water for agricultural irrigation, the town of Los Alamos, and supplemental water to Vandenberg Space Force Base (VSFB). Groundwater pumpage has increased since the 1970s as non-irrigated agricultural land has been converted to irrigated land and as local pumping for municipal use has increased. This increase in groundwater use has resulted in declining groundwater levels, adjustments in surface-water flows and species habitats, and changes in water quality. Water managers are addressing the challenges of meeting this increased demand while maintaining sustainable groundwater supplies. To address these challenges, Santa Barbara County Water Agency, Vandenberg Space Force Base (VSFB), and the U.S. Geological Survey (USGS) undertook a cooperative study to characterize the integrated hydrologic system of the SACVW and develop tools to better understand and manage the groundwater system. The objectives of this study were to improve the understanding of the integrated hydrologic system and incorporate the understanding into an integrated groundwater and surface-water flow model that can be used to help manage the water resources in the SACVW.

The San Antonio Creek integrated model (SACIM) was developed using the USGS coupled groundwater and surface-water flow model to simulate the hydrologic system of the SACVW and provide annual and average water budgets for 1948–2018 water years. Results from the SACIM indicated that between 1948 and 2018, total groundwater from storage (storage depletion) for the period was 453,300 acre-feet (acre-ft). Agricultural pumpage was the largest discharge and accounted for a total of 1,020,000 acre-ft of groundwater. Increased pumpage since the mid-1980s (of which agricultural pumpage is the primary component) is tied to an increased rate of storage depletion and reduced rates of groundwater evapotranspiration and surface leakage (groundwater discharge to the surface and soil zone). The increased pumpage also reduced subsurface inflow to Barka Slough, resulting in a decline in upward flow through the underlying hydrogeologic units and surface leakage. In addition to quantifying historical changes in the integrated hydrologic system, the SACIM is a tool than can be used by water managers to evaluate the effects of different climatic and hydrologic conditions and management strategies.

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Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2022-01-27","noUsgsAuthors":false,"publicationDate":"2022-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Woolfenden, Linda R. 0000-0003-3500-4709 lrwoolfe@usgs.gov","orcid":"https://orcid.org/0000-0003-3500-4709","contributorId":1476,"corporation":false,"usgs":true,"family":"Woolfenden","given":"Linda","email":"lrwoolfe@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engott, John A. 0000-0003-1889-4519 jaengott@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-4519","contributorId":1142,"corporation":false,"usgs":true,"family":"Engott","given":"John","email":"jaengott@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831983,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226588,"text":"70226588 - 2022 - Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling","interactions":[],"lastModifiedDate":"2024-05-17T16:56:00.80531","indexId":"70226588","displayToPublicDate":"2022-02-01T07:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling","docAbstract":"

Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (VTotal) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (V&#x302;Total\">V̂Total) and the variance component attributable to interpreters (V&#x302;1\">V̂1) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e., V&#x302;1/V&#x302;Total\">V̂1/V̂Total) and the relative bias of the standard simple random sampling variance estimator (V&#x302;stand\">V̂stand) as an estimator of VTotal, defined as 100%*(V&#x302;stand&#x2212;V&#x302;Total\">V̂stand−V̂Total)/V&#x302;Total\">V̂Total. For each of five land cover classes, we computed V&#x302;1\">V̂1, V&#x302;Total\">V̂Total, and V&#x302;stand\">V̂stand using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of V&#x302;1/V&#x302;Total\">V̂1/V̂Total and the mean of the estimated relative bias of V&#x302;stand\">V̂stand over these 21 pairs. Based on the mean of V&#x302;1/V&#x302;Total\">V̂1/V̂Total per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator, V&#x302;stand\">V̂stand, underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance (V&#x302;1/V&#x302;Total\">V̂1/V̂Total) and larger negative estimated relative bias of V&#x302;stand\">V̂stand. Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112806","usgsCitation":"Stehman, S.V., Mousoupetros, J., McRoberts, R.E., Naesset, E., Pengra, B., Xing, D., and Horton, J., 2022, Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling: Remote Sensing of Environment, v. 269, 112806, 10 p., https://doi.org/10.1016/j.rse.2021.112806.","productDescription":"112806, 10 p.","ipdsId":"IP-128389","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448953,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112806","text":"Publisher Index Page"},{"id":435985,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HEE6VK","text":"USGS data release","linkHelpText":"Land Cover Assignments of 300 locations in the Pacific Northwest in 2000"},{"id":392301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46435546875,\n 46.92025531537451\n ],\n [\n -121.53076171875,\n 46.92025531537451\n ],\n [\n -121.53076171875,\n 49.009050809382046\n ],\n [\n -123.46435546875,\n 49.009050809382046\n ],\n [\n -123.46435546875,\n 46.92025531537451\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"269","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":827413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mousoupetros, John","contributorId":269542,"corporation":false,"usgs":false,"family":"Mousoupetros","given":"John","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":827414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McRoberts, Ronald E.","contributorId":269543,"corporation":false,"usgs":false,"family":"McRoberts","given":"Ronald","email":"","middleInitial":"E.","affiliations":[{"id":55983,"text":"USFS Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":827415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naesset, Erik","contributorId":269544,"corporation":false,"usgs":false,"family":"Naesset","given":"Erik","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":827416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pengra, Bruce 0000-0003-2497-8284","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":264539,"corporation":false,"usgs":false,"family":"Pengra","given":"Bruce","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":827417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xing, Dingfan 0000-0003-1233-7260","orcid":"https://orcid.org/0000-0003-1233-7260","contributorId":254318,"corporation":false,"usgs":false,"family":"Xing","given":"Dingfan","email":"","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":827418,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":827419,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70226715,"text":"70226715 - 2022 - Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data","interactions":[],"lastModifiedDate":"2024-05-17T16:58:05.876204","indexId":"70226715","displayToPublicDate":"2022-02-01T07:11:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data","docAbstract":"

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4 °C and 3.3 °C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04 °C/year and 0.01 °C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2 °C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112803","usgsCitation":"Xian, G.Z., Shi, H., Zhou, Q., Auch, R.F., Gallo, K., Wu, Z., and Kolian, M., 2022, Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data: Remote Sensing of Environment, v. 269, 112803, 16 p., https://doi.org/10.1016/j.rse.2021.112803.","productDescription":"112803, 16 p.","ipdsId":"IP-127385","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448956,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112803","text":"Publisher Index Page"},{"id":392569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"269","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":827921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua","contributorId":269790,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[{"id":56030,"text":"ASRC Federal Data Solutions (AFDS), under contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":827922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":827923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":827924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallo, Kevin 0000-0001-9162-5011","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":257326,"corporation":false,"usgs":false,"family":"Gallo","given":"Kevin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":827925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":827926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolian, Michael 0000-0002-7134-8317","orcid":"https://orcid.org/0000-0002-7134-8317","contributorId":257327,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":827927,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230381,"text":"70230381 - 2022 - Pathways of productivity and influences on top consumers in forested streams","interactions":[],"lastModifiedDate":"2022-04-11T12:16:36.843874","indexId":"70230381","displayToPublicDate":"2022-02-01T07:09:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Pathways of productivity and influences on top consumers in forested streams","docAbstract":"

Forested stream ecosystems involve complex physical and biotic pathways that can influence fish in numerous ways. Consequently, the responses of fish communities to disturbance can be difficult to understand. In this study, we employed a food web model that links biotic (e.g., physiology, predator–prey interactions) and abiotic (e.g., temperature, sunlight) attributes to address fish responses to changes in stream-riparian ecosystems. We modeled responses to food web dynamics in four streams, using scenarios that included responses to riparian disturbance, climate change, and shifts in top consumers. The two consumers we focused on were coastal cutthroat trout (Oncorhynchus clarkii clarkii) and sculpin (Cottus spp., collectively treated as a functional group). We found the responses to environmental changes varied by fish species and among streams, and that responses were not independent due to exploitative interspecific competition. Simulations based on long-term data indicated that coastal cutthroat trout were responsive to changes in allochthonous resources including terrestrial detritus and invertebrates, whereas sculpin were more responsive to changes to autochthonous resources that included, periphyton and aquatic invertebrates. These results may be, in part, a consequence of species-specific foraging behavior. Trout have a higher propensity to drift feed and therefore receive a substantial subsidy from terrestrial invertebrates, whereas sculpin feed mostly on aquatic insects on the streambed. Simulations of changes in summer temperature and stream discharge suggest decreased biomass of both fish species because of physiological constraints on invertebrate prey which reduce fish foraging opportunities. Exploitative competition also may be important in fish responses: when one fish taxon was removed, the other showed increased biomass. Although the pattern of simulation results was consistent across the four streams, the magnitude of change varied among streams. Streams with food webs fueled by multiple energy sources may be more resilient to changes to riparian forests and climate. Through application of a systems model, we gained insights into pathways of productivity for fish in forested stream ecosystems that provide understanding of processes that influence fish and streams, as well as implications for management of both.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2022.120046","usgsCitation":"Benjamin, J.R., Dunham, J.B., Johnson, S.L., Ashkenas, L., Penaluna, B.E., Bilby, R., Bateman, D.S., Leer, D.W., and Bellmore, J.R., 2022, Pathways of productivity and influences on top consumers in forested streams: Forest Ecology and Management, v. 508, 120046, 11 p., https://doi.org/10.1016/j.foreco.2022.120046.","productDescription":"120046, 11 p.","ipdsId":"IP-134367","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2022.120046","text":"Publisher Index Page"},{"id":398460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":840126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Sherri L 0000-0002-4223-3465","orcid":"https://orcid.org/0000-0002-4223-3465","contributorId":192210,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","email":"","middleInitial":"L","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":840128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ashkenas, Linda","contributorId":289996,"corporation":false,"usgs":false,"family":"Ashkenas","given":"Linda","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Penaluna, Brooke E","contributorId":192212,"corporation":false,"usgs":false,"family":"Penaluna","given":"Brooke","email":"","middleInitial":"E","affiliations":[],"preferred":false,"id":840130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bilby, Robert E","contributorId":146867,"corporation":false,"usgs":false,"family":"Bilby","given":"Robert E","affiliations":[{"id":16757,"text":"Oregon State Univ.","active":true,"usgs":false}],"preferred":false,"id":840131,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bateman, Douglas S. 0000-0002-5609-2085 doug_bateman@usgs.gov","orcid":"https://orcid.org/0000-0002-5609-2085","contributorId":207396,"corporation":false,"usgs":false,"family":"Bateman","given":"Douglas","email":"doug_bateman@usgs.gov","middleInitial":"S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leer, David W.","contributorId":207397,"corporation":false,"usgs":false,"family":"Leer","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bellmore, James R 0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":840134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236988,"text":"70236988 - 2022 - Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","interactions":[],"lastModifiedDate":"2022-09-27T13:30:28.543738","indexId":"70236988","displayToPublicDate":"2022-02-01T07:00:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","docAbstract":"

Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47 km of stream length across the Farmington River watershed (1,570 km2; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2021.104108","usgsCitation":"Barclay, J.R., Briggs, M., Moore, E., Starn, J., Hanson, A.E., and Helton, A., 2022, Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale: Advances in Water Resources, v. 160, 104108, 14 p., https://doi.org/10.1016/j.advwatres.2021.104108.","productDescription":"104108, 14 p.","ipdsId":"IP-111577","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448959,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2021.104108","text":"Publisher Index Page"},{"id":435986,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EIV8L5","text":"USGS data release","linkHelpText":"Thermal Infrared images and field data on areas of groundwater discharge in the Farmington River watershed"},{"id":407396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts","otherGeospatial":"Farmington River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.9327392578125,\n 41.775408403663285\n ],\n [\n -72.77755737304688,\n 41.6944496643259\n ],\n [\n -72.75833129882812,\n 41.73852846935917\n ],\n [\n -72.86819458007811,\n 41.792816561051815\n ],\n [\n -72.88604736328125,\n 41.87671893034394\n ],\n [\n -72.86407470703125,\n 42.173581898327754\n ],\n [\n -73.1085205078125,\n 42.24071874922666\n ],\n [\n -73.1195068359375,\n 41.88387623204765\n ],\n [\n -72.9327392578125,\n 41.775408403663285\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"160","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":852936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Eric","contributorId":216658,"corporation":false,"usgs":false,"family":"Moore","given":"Eric","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":852937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starn, J. Jeffrey 0000-0001-5909-0010 jjstarn@usgs.gov","orcid":"https://orcid.org/0000-0001-5909-0010","contributorId":1916,"corporation":false,"usgs":true,"family":"Starn","given":"J. Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":false,"id":852938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Ann E.H.","contributorId":296947,"corporation":false,"usgs":false,"family":"Hanson","given":"Ann","email":"","middleInitial":"E.H.","affiliations":[],"preferred":false,"id":852939,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helton, Ashley","contributorId":219741,"corporation":false,"usgs":false,"family":"Helton","given":"Ashley","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":852940,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241790,"text":"70241790 - 2022 - Sex‐related differences in aging rate are associated with sex chromosome system in amphibians","interactions":[],"lastModifiedDate":"2023-03-27T11:39:47.353817","indexId":"70241790","displayToPublicDate":"2022-02-01T06:33:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1598,"text":"Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Sex‐related differences in aging rate are associated with sex chromosome system in amphibians","docAbstract":"

Sex‐related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture–recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex‐specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the “unguarded X/Z effect”) or repeat‐rich Y/W chromosome (the “toxic Y/W effect”) could accelerate aging in the heterogametic sex in some vertebrate clades.

","language":"English","publisher":"Oxford Academic","doi":"10.1111/evo.14410","usgsCitation":"Cayuela, H., Lemaître, J., Léna, J., Ronget, V., Martinez-Solano, I., Muths, E.L., Pilliod, D., Schmidt, B., Sanchez-Montes, G., Gutierrez-Rodriguez, J., Pyke, G., Grossenbacher, K., Lenzi, O., Bosch, J., Beard, K.H., Woolbright, L.L., Lambert, B., Green, D.M., Garwood, J.M., Fisher, R., Matthews, K., Dudgeon, D., Lau, A., Speybroeck, J., Homan, R., Jehle, R., Baskale, E., Mori, E., Arntzen, J.W., Joly, P., Stiles, R., Lannoo, M.J., Maerz, J.C., Lowe, W., Valenzuela-Sanchez, A., Christianson, D., Angelini, C., Thirion, J., Merila, J., Colli, G.R., Vasconcellos, M.M., Boas, T.C., Arantes, I.D., Levionnois, P., Reinke, B., Vieira, C., Marais, G.A., Gaillard, J., and Miller, D., 2022, Sex‐related differences in aging rate are associated with sex chromosome system in amphibians: Evolution, v. 76, no. 2, p. 346-356, https://doi.org/10.1111/evo.14410.","productDescription":"10 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B.","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867620,"contributorType":{"id":1,"text":"Authors"},"rank":47},{"text":"Gaillard, Jean-Michael","contributorId":303609,"corporation":false,"usgs":false,"family":"Gaillard","given":"Jean-Michael","email":"","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867621,"contributorType":{"id":1,"text":"Authors"},"rank":48},{"text":"Miller, David A.W.","contributorId":198461,"corporation":false,"usgs":false,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":867622,"contributorType":{"id":1,"text":"Authors"},"rank":49}]}} ,{"id":70262481,"text":"70262481 - 2022 - Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A","interactions":[],"lastModifiedDate":"2025-01-17T15:42:51.223433","indexId":"70262481","displayToPublicDate":"2022-02-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A","docAbstract":"

Amphibians are vital elements of ecosystems, serving as predator and prey. Their biphasic nature makes them dependent on aquatic and terrestrial habitats; as wet-skinned ectotherms, they are vulnerable to a range of environmental threats, including climate change. Yellowstone National Park (YNP) is becoming warmer and drier, and some wetlands important to amphibians have diminished. Continued climate change is predicted to reduce snowpack, soil moisture, and forest cover. We used data from models of future climate and vegetation cover to mechanistically model how climate change might affect the movements of Western Toads (Anaxyrus boreas) across the landscape of three test areas in YNP for the years 2050 and 2090, compared to 2000 as a baseline. Least-cost path analysis produced mixed results: for 2050 and 2090, physiological costs of movement increased in one test area and decreased in another; they were mixed in the third. These changes generally reflect the preference by toads for more open forests. Estimating costs for other species of YNP amphibians produced more negative results. For Columbia Spotted Frogs (Rana luteiventris) and Boreal Chorus Frogs (Pseudacris maculata) (both more aquatic and less adapted to terrestrial habitats), movement costs increased by about 2–15X. Reduced frequency or duration of rain events might limit the nocturnal movements of Western Tiger Salamanders (Ambystoma mavortium). Climate change may not have negative impacts on all amphibians throughout YNP, but increased movement costs for terrestrial habitats will accentuate effects of drying wetlands in at least parts of YNP. Land management actions that preserve habitat structure of both forest and low shrub cover may help mitigate continued drying conditions of climate change.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108575","usgsCitation":"Bartelt, P., Thornton, P., and Klaver, R.W., 2022, Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A: Ecological Indicators, v. 135, 108575, 12 p., https://doi.org/10.1016/j.ecolind.2022.108575.","productDescription":"108575, 12 p.","ipdsId":"IP-134835","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108575","text":"Publisher Index Page"},{"id":480736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.13112916264933,\n 45.42187928380869\n ],\n [\n -111.13112916264933,\n 43.87067545981952\n ],\n [\n -109.15957965370097,\n 43.87067545981952\n ],\n [\n -109.15957965370097,\n 45.42187928380869\n ],\n [\n -111.13112916264933,\n 45.42187928380869\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"135","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bartelt, Paul E.","contributorId":349463,"corporation":false,"usgs":false,"family":"Bartelt","given":"Paul E.","affiliations":[{"id":56262,"text":"Waldorf University","active":true,"usgs":false}],"preferred":false,"id":924323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thornton, Peter E.","contributorId":349464,"corporation":false,"usgs":false,"family":"Thornton","given":"Peter E.","affiliations":[{"id":83486,"text":"Oak Ridge National Laborabory","active":true,"usgs":false}],"preferred":false,"id":924324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263749,"text":"70263749 - 2022 - Galliform exclusion from the Migratory Bird Treaty Act has produced an alternate conservation path, but no evidence for differences in population status","interactions":[],"lastModifiedDate":"2025-02-21T15:41:18.181211","indexId":"70263749","displayToPublicDate":"2022-02-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Galliform exclusion from the Migratory Bird Treaty Act has produced an alternate conservation path, but no evidence for differences in population status","docAbstract":"

The Migratory Bird Treaty Act (MBTA) is critical to avian conservation in the United States, both through its protection of migratory birds and as a catalyst for a century of coordinated avian conservation. While more than 1,000 species are protected by MBTA, of extant bird species native to the continental U.S., only 20 species belonging to the order Galliformes are explicitly excluded. Management of galliforms has occurred largely without direct federal oversight, placing this group on a fundamentally different conservation path during the century following MBTA passage. In this paper, we review the historical context and biological justification for exclusion of galliforms from MBTA and synthesize how their present-day conservation differs from that of migratory birds. We find the most prominent difference between the two groups involves the scope of coordination among stakeholders. The U.S. government, primarily via the Department of Interior, acts as de facto coordinating body for migratory bird conservation and plays the central role in oversight, funding, and administration of management in the United States. In contrast, galliform management falls primarily to individual state wildlife agencies, and coordinated conservation efforts have been more ad hoc and unevenly spread across species. Migratory birds benefit from an almost universally greater scope of research and monitoring, scale of habitat conservation, and sophistication of harvest management compared with galliforms. Galliform harvest management plans, in particular, are less likely to use measurable objectives, reporting of uncertainty in population parameters, and explanation of harvest management techniques. Based on a review of species status lists (e.g., the U.S. Endangered Species Act), we found no evidence that galliforms were more frequently listed than migratory species. Regional trend estimates from the North American Breeding Bird Survey (BBS) were more likely to be negative for galliforms over the period 1966–2015, but this was primarily driven by Northern Bobwhite (Colinus virginianus). Data to assess galliform population status are generally poor, which complicates assessment for roughly half of galliform species. Increased support for coordination among state agencies and other stakeholders, similar to that applied to migratory birds, could help to ensure that galliform conservation is poised to tackle forthcoming challenges associated with global change.

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America\"}}]}","volume":"124","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Blomberg, Erik J.","contributorId":351183,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":928117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cardinal, Casey J.","contributorId":351184,"corporation":false,"usgs":false,"family":"Cardinal","given":"Casey J.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":928119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis-Felege, Susan N.","contributorId":351185,"corporation":false,"usgs":false,"family":"Ellis-Felege","given":"Susan N.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":928120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gibson, Daniel","contributorId":94984,"corporation":false,"usgs":false,"family":"Gibson","given":"Daniel","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":928171,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Monroe, Adrian P.","contributorId":351186,"corporation":false,"usgs":false,"family":"Monroe","given":"Adrian P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":928122,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schwalenberg, P","contributorId":300551,"corporation":false,"usgs":false,"family":"Schwalenberg","given":"P","email":"","affiliations":[{"id":65194,"text":"Alaska Migratory Bird Co-Management Council","active":true,"usgs":false}],"preferred":false,"id":928123,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256689,"text":"70256689 - 2022 - Estimating bee abundance: Can mark-recapture methods validate common sampling protocols?","interactions":[],"lastModifiedDate":"2024-08-01T19:32:23.629013","indexId":"70256689","displayToPublicDate":"2022-01-31T14:04:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18173,"text":"Apidologie","active":true,"publicationSubtype":{"id":10}},"title":"Estimating bee abundance: Can mark-recapture methods validate common sampling protocols?","docAbstract":"Wild bees are essential pollinators in natural and agricultural systems, but populations of some species have declined. Efforts to assess the status of wild bees are hindered by uncertainty in common sampling methods, such as pan traps and aerial netting, which may or may not provide a valid index of abundance across species and habitats. Mark-recapture methods are a common and effective means of estimating population size, widely used in vertebrates but rarely applied to bees. Here we review existing mark-recapture studies of wild bees and present a new case study comparing mark-recapture population estimates to pan trap and net capture for four taxa in a wild bee community. Net, but not trap, capture was correlated with abundance estimates across sites and taxa. Logistical limitations ensure that mark-recapture studies will not fully replace other bee sampling methods, but they do provide a feasible way to monitor selected species and measure the performance of other sampling methods.","language":"English","publisher":"Springer","doi":"10.1007/s13592-022-00919-4","usgsCitation":"Briggs, E.L., Baranski, C., Munzer Schaetz, O., Garrison, G., Youngsteadt, E., and Collazo, J.A., 2022, Estimating bee abundance: Can mark-recapture methods validate common sampling protocols?: Apidologie, v. 53, no. 10, 24 p., https://doi.org/10.1007/s13592-022-00919-4.","productDescription":"24 p.","ipdsId":"IP-134206","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":448971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13592-022-00919-4","text":"Publisher Index Page"},{"id":432054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Emma L.","contributorId":341588,"corporation":false,"usgs":false,"family":"Briggs","given":"Emma","email":"","middleInitial":"L.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":908660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baranski, Christopher","contributorId":341592,"corporation":false,"usgs":false,"family":"Baranski","given":"Christopher","email":"","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":908664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munzer Schaetz, Olivia","contributorId":341589,"corporation":false,"usgs":false,"family":"Munzer Schaetz","given":"Olivia","email":"","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":908661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrison, Gabriela","contributorId":341590,"corporation":false,"usgs":false,"family":"Garrison","given":"Gabriela","email":"","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":908662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Youngsteadt, Elsa","contributorId":341591,"corporation":false,"usgs":false,"family":"Youngsteadt","given":"Elsa","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":908663,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908659,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227700,"text":"ofr20211103 - 2022 - Climate change adaptation thinking for managed wetlands","interactions":[],"lastModifiedDate":"2026-03-25T17:44:25.037268","indexId":"ofr20211103","displayToPublicDate":"2022-01-31T12:17:25","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1103","displayTitle":"Climate Change Adaptation Thinking for Managed Wetlands","title":"Climate change adaptation thinking for managed wetlands","docAbstract":"

Climate change presents new and ongoing challenges to natural resource management. To confront these challenges effectively, managers need to develop proactive adaptation strategies to prepare for and deal with the effects of climate change. We engaged managers and biologists from several midwestern U.S. Fish and Wildlife Service field stations to understand recent and future climate change effects, identify adaptation barriers and opportunities, and pilot an approach for integrating adaptation thinking into management planning. To start, three structured discussions informed our understanding of how managers currently deal with climate change effects, the strategies being implemented to cope, and the barriers that limit climate change adaptation efforts. We used these insights to develop a multiday virtual workshop geared toward identifying potential adaptation strategies for managed wetlands. First, we developed a conceptual model to visualize how management actions are used to meet habitat objectives within wetland management systems. Next, we discussed how climate change may affect management actions and objectives; we used this understanding of potential effects to spatially assess vulnerability of managed wetlands to climate change. Using a scenario planning approach, we incorporated multiple potential future conditions and identified effects and adaptation strategies that could be considered for each scenario. As a result, several adaptation strategies for managed wetlands under dry and wet future scenarios were identified that can be applied when developing site-specific adaptation plans. Based on our piloted approach, we determined it would be important to have an adaptation team composed of scientists and managers to facilitate discussions, develop appropriate scenarios, and identify realistic adaptation options. We document the tools, findings, and adaptation thinking process taken to enhance adaptation efforts of managed wetlands. The adaptation thinking process can be applied to advance adaptation efforts in other habitats, ecosystems, and site-specific land management.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211103","usgsCitation":"Delaney, J.T., Bouska, K.L., and Eash, J.D., 2021, Climate Change Adaptation Thinking for Managed Wetlands: U.S. Geological Survey Open-File Report 2021–1103, 25 p., https://doi.org/10.3133/ofr20211103.","productDescription":"Report: vi, 25 p.; 3 Data Releases","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-128227","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":394943,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Watershed-based Midwest Climate Change Vulnerability Assessment Tool"},{"id":394942,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"R code: Scripts used to analyze data for the Midwest Climate Change Vulnerability Assessment"},{"id":394941,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Model inputs: Midwest climate change vulnerability assessment for the U.S. Fish and Wildlife Service"},{"id":394938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.pdf","text":"Report","size":"44.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1103"},{"id":394937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1103/coverthb.jpg"},{"id":501530,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112325.htm","linkFileType":{"id":5,"text":"html"}},{"id":394940,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1103/images"},{"id":394939,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1103 XML"}],"contact":"

Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2022-01-31","noUsgsAuthors":false,"publicationDate":"2022-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John T. 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eash, Josh D.","contributorId":193103,"corporation":false,"usgs":false,"family":"Eash","given":"Josh","email":"","middleInitial":"D.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":true,"id":831831,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230019,"text":"70230019 - 2022 - Characterizing the oral and distal gut microbiota of the threatened southern sea otter (Enhydra lutris nereis) to enhance conservation practice","interactions":[],"lastModifiedDate":"2022-04-12T14:11:00.91825","indexId":"70230019","displayToPublicDate":"2022-01-31T11:22:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Characterizing the oral and distal gut microbiota of the threatened southern sea otter (Enhydra lutris nereis) to enhance conservation practice","title":"Characterizing the oral and distal gut microbiota of the threatened southern sea otter (Enhydra lutris nereis) to enhance conservation practice","docAbstract":"

The southern sea otter (Enhydra lutris nereis) is a threatened sub-species in coastal ecosystems. To understand better the role of diet, monitor health, and enhance management of this and other marine mammal species, we characterized the oral (gingival) and distal gut (rectal and fecal) microbiota of 158 wild southern sea otters living off the coast of central California, USA, and 12 captive sea otters, some of which were included in a diet shift experiment. We found that the sea otter fecal microbiota was distinct from that of three other otter species, and that captivity does not significantly alter the community structure of the sea otter gingival or distal gut microbiota. Metagenomic analysis unexpectedly revealed that the majority of sea otter fecal DNA is derived from prey, rather than from indigenous bacteria or host cells as with most other mammals. We speculate that a reduced bacterial biomass in the sea otter gut reflects rapid gut transit time and a particular strategy for foraging and energy harvest. This study establishes a reference for the healthy sea otter microbiota, highlights how a marine lifestyle may shape the mammalian microbiota, and may inform future health assessments and conservation management of sea otter populations.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12640","usgsCitation":"Dudek, N.K., Switzer, A.D., Costello, E.K., Murray, M.J., Tomoleoni, J.A., Staedler, M.M., Tinker, M., and Relman, D.A., 2022, Characterizing the oral and distal gut microbiota of the threatened southern sea otter (Enhydra lutris nereis) to enhance conservation practice: Conservation Science and Practice, v. 4, no. 4, e12640, 17 p., https://doi.org/10.1111/csp2.12640.","productDescription":"e12640, 17 p.","ipdsId":"IP-136885","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448974,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12640","text":"Publisher Index Page"},{"id":397535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 34.252676117101515\n ],\n [\n -119.520263671875,\n 34.252676117101515\n ],\n [\n -119.520263671875,\n 36.92793899776678\n ],\n [\n -122,\n 36.92793899776678\n ],\n [\n -122,\n 34.252676117101515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Dudek, Natasha K","contributorId":289198,"corporation":false,"usgs":false,"family":"Dudek","given":"Natasha","email":"","middleInitial":"K","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":838688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Switzer, Alexandra D","contributorId":289199,"corporation":false,"usgs":false,"family":"Switzer","given":"Alexandra","email":"","middleInitial":"D","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":838689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costello, Elizabeth K","contributorId":289200,"corporation":false,"usgs":false,"family":"Costello","given":"Elizabeth","email":"","middleInitial":"K","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":838690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, Michael J.","contributorId":206852,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37418,"text":"Monterey Bay Aquarium, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":838691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":838693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":221787,"corporation":false,"usgs":false,"family":"Tinker","given":"M. Tim","affiliations":[{"id":40428,"text":"University of California, Santa Cruz; former USGS PI","active":true,"usgs":false}],"preferred":false,"id":838694,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Relman, David A.","contributorId":289201,"corporation":false,"usgs":false,"family":"Relman","given":"David","middleInitial":"A.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":838695,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}