Historically, adult summer steelhead Oncorhynchus mykiss returning to hatcheries on the lower Cowlitz River were sometimes transported and released in the river (recycled) to provide additional angling opportunity for the popular sport fishery in the basin. However, this practice has not been used in recent years because of concerns associated with interactions between hatchery fish and wild fish. Fishery managers were interested in resuming recycling but lacked information regarding effects of this practice on wild steelhead so we conducted a study during 2012–2013 to: (1) enumerate recycled steelhead that returned to the hatchery or were removed by anglers; and (2) determine if steelhead that were not removed from the river remained in the system where they could interact with wild fish.
During June–August 2012, a total of 549 summer steelhead were captured at the Cowlitz Salmon Hatchery, tagged, and released downstream near the Interstate 5 Bridge. All recycled steelhead were tagged with a white Floy® tag and opercle-punched; 109 (20 percent) of these fish also were radio-tagged. All adult steelhead that return to the hatchery were handled by hatchery staff so recycled steelhead that returned to the hatchery were enumerated daily. A creel survey and voluntary angler reports were used to determine the number of recycled steelhead that were caught by anglers. We established three fixed telemetry monitoring sites on the mainstem Cowlitz River and eight additional sites were deployed on tributaries to the lower Cowlitz River where wild winter steelhead are known to spawn. We also conducted mobile tracking from a boat during October 2012, November 2012, and January 2013 to locate radio-tagged fish.
A total of 10,722 summer steelhead were captured at the Cowlitz Salmon Hatchery in 2012, which was the largest return since 2008. River flows during much of the study period were similar to 2008–2011 average flows, however, high-flow periods in July and November 2012 were nearly twice as high as the 2008–2011 average flows. We determined that 50 percent (273 fish) of the recycled steelhead returned to the hatchery and 18 percent (102 fish) of the recycled steelhead were caught by anglers. Most (243 fish; 89 percent) of the recycled steelhead that returned to the hatchery were recollected during July–August. The average elapsed time from release to recapture at the hatchery was 9 days (d) and 72 percent (182 fish) of the fish returned to the hatchery within 14 d of release. These trends were similar for recycled steelhead that were caught by anglers. Most fish were caught during July–August and the median time from release to capture was 10 d. We determined that 65 percent (70 fish) of the angler-caught fish returned to the hatchery within 14 d of release. River flows appeared to affect both hatchery returns and angler catch. The daily number of recycled steelhead that were recollected at the hatchery were low during periods when river flows were decreasing and high during periods when river flows were increasing. Conversely, daily angler catch of recycled steelhead generally was low when flows were increasing and high when flows were decreasing.
We determined that 32 percent of the recycled steelhead (174 fish) were not removed from the lower Cowlitz River, based on observations from hatchery returns and angler reports, but results from the radio-tagged fish were insightful for understanding what may have happened to these fish. By comparison, we determined that 24 percent of the radio-tagged fish were not known to have been removed from the river. We determined that 12 percent of these fish were actively moving in the lower Cowlitz River during October 2012–January 2013. None of the radio-tagged fish were detected in tributaries during the study period except for a single fish that spent approximately 7 d in the Toutle River during early September. During October 2012–January 2013, 10 percent of the radio-tags from recycled steelhead were detected near popular fishing areas, and 2 percent of the radio-tagged steelhead were never detected during the study period. We suspect that a large proportion of these fish may have been harvested and not reported, or died.
Detection patterns of radio-tagged steelhead showed that most fish (82 percent) moved upstream from the release site and were detected at the Trout Hatchery and the Barrier Dam sites. The median time from release to detection at these sites was 3.7 d and many of these fish made multiple trips between the two sites. Nearly one-third (29 percent) of the recycled steelhead that were detected at the Trout Hatchery and the Barrier Dam made at least two trips between the sites and some fish made as many as six trips. Radio-tagged fish that remained in the lower Cowlitz River during the spawning period (December 2012–January 2013) were observed in the river reach between the mouth of Ostrander Creek (river mile 10) and the Trout Hatchery (river mile 44).
During this study, we collected data on opercle punch regrowth rates to understand the temporal effectiveness of this marking technique. We took opercle measurements for a total of 190 fish during the study. Fresh opercle punches were measured for 63 fish at the time of marking, and the remaining 127 fish were measured when fish returned to the hatchery. We determined that opercle punches remained open for about 30 d. The holes appeared to regrow slowly in the first 20 d after marking, but regrowth accelerated during the 20–30 d post-marking period. After 30 d, all opercle punches that we observed had completely closed due to tissue regrowth.
Our study showed that a large proportion (68 percent) of the recycled steelhead were removed from the lower Cowlitz River. These fish primarily entered the hatchery or were caught by anglers within 14 d of release, which suggests that they present minimal risk to wild fish in the system. However, the remaining fish (32 percent) could not be accounted for, which may complicate fisheries management decisions associated with recycling summer steelhead. Findings from the radiotelemetry study suggest that unreported harvest or mortality could explain a large proportion of those fish that were not reported as having been removed from the river. Furthermore, intensive monitoring of the key spawning tributaries failed to detect a single fish during the spawning period. These findings were supported by observations from weir traps operated by the Washington Department of Fish and Wildlife. Our findings indicate that additional research may be warranted to further examine the effects of recycling hatchery summer steelhead in the lower Cowlitz River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131116","usgsCitation":"Kock, T.J., Liedtke, T.L., Ekstrom, B.K., Rondorf, D.W., Gleizes, C., Dammers, W., Gibson, S., and Murphy, J., 2013, Behavior and movement of adult summer steelhead following collection and release, lower Cowlitz River, Washington, 2012--2013: U.S. Geological Survey Open-File Report 2013-1116, iv, 22 p., https://doi.org/10.3133/ofr20131116.","productDescription":"iv, 22 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2012-01-01","temporalEnd":"2013-12-31","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131116.jpg"},{"id":272341,"type":{"id":15,"text":"Index 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Center","interactions":[],"lastModifiedDate":"2013-05-16T11:52:13","indexId":"gip149","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"149","title":"Seventy-five years of science—The U.S. Geological Survey’s Western Fisheries Research Center","docAbstract":"As of January 2010, 75 years have elapsed since Dr. Frederic Fish initiated the pioneering research program that would evolve into today’s Western Fisheries Research Center (WFRC). Fish began his research working alone in the basement of the recently opened Fisheries Biological Laboratory on Lake Union in Seattle, Washington. WFRC’s research began under the aegis of the U.S. Fish and Wildlife Service and ends its first 75 years as part of the U.S. Geological Survey with a staff of more than 150 biologists and support personnel and a heritage of fundamental research that has made important contributions to our understanding of the biology and ecology of the economically important fish and fish populations of the Nation. Although the current staff may rarely stop to think about it, WFRC’s antecedents extend many years into the past and are intimately involved with the history of fisheries conservation in the Western United States. Thus, WFRC Director Lyman Thorsteinson asked me to write the story of this laboratory “while there are still a few of you around who were here for some of the earlier years” to document the rich history and culture of WFRC by recognizing its many famous scientists and their achievements. This historyalso would help document WFRC’s research ‘footprint’ in the Western United States and its strategic directions. Center Director Thorsteinson concluded that WFRC’s heritage told by an emeritus scientist also would add a texture of legitimacy based on personal knowledge that will all-to-soon be lost to the WFRC and to the USGS. The WFRC story is important for the future as well as for historical reasons. It describes how we got to the place we are today by documenting the origin, original mission, and our evolving role in response to the constantly changing technical information requirements of new environmental legislation and organizational decision-making. The WFRC research program owes its existence to the policy requirements of Federal conservation legislation originating with the construction of Grand Coulee Dam in 1933. The research program was shaped by laws enacted in subsequent years such as the Federal Water Pollution Control Act (1972), National Environmental Policy Act (1973), Endangered Species Act (1974), and Northwest Power Planning Act (1980), to name only a few. The WFRC has not been constrained by direct management or regulatory responsibility for a particular fishery (such as providing sustainable catch limits data to a resource management structure). Thus, WFRC has been able to concentrate on scientific pursuits and information needs required by contemporary environmental legislation. Over the years, we have pioneered in several important areas of fisheries research including the diagnoses and control of diseases in economically important fish, effects of environmental alterations on the physiological quality and survival of Pacific salmon released from federal mitigation hatcheries, applications in biotelemetry, and the bioenergetics of predator-prey interactions in the Columbia River. The WFRC of today is a widely distributed organization in the Western United States. Knowledge of the historical connections and accomplishments of our predecessors is important beyond the sense of pride and unity it instills in the WFRC family of today. For example, a discerning reader will note the evolution of WFRC’s research from a single disciplinary focus (early era—hatchery disease problems), to multiple disciplines (middle to late era—species, populations, habitats; threatened and endangered species), to the present era (multidisciplinary and with increasing process focus). For the benefit of the current WFRC staff, more emphasis has been placed on the early years rather than on the present day because people are quite naturally more familiar with the recent past than with the research done during the first decades of WFRC’s existence. By every rational measure, the WFRC has evolved into a fisheries research organization well positioned to provide the biological information needed to support the continued conservation and management of our Nation’s living aquatic natural resources. The high standard of excellence that connects WFRC’s past to our present research program provides a firm foundation on which to base the work yet to be done. In another 75 years, WFRC will undoubtedly be a very different place than it is today, but its evolution will be forever rooted in the story of the research and of the people related here. More about the diverse fisheries research projects WFRC scientists are conducting today is available at WFRC’s website: http://wfrc.usgs.gov/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip149","usgsCitation":"Wedemeyer, G.A., 2013, Seventy-five years of science—The U.S. Geological Survey’s Western Fisheries Research Center: U.S. Geological Survey General Information Product 149, vi, 46 p., https://doi.org/10.3133/gip149.","productDescription":"vi, 46 p.","numberOfPages":"54","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/gip149.jpg"},{"id":272320,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/gip/149/"},{"id":272321,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/149/pdf/gip149.pdf"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Western Fisheries Research Center","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.4,47.5 ], [ -122.4,47.7 ], [ -122.2,47.7 ], [ -122.2,47.5 ], [ -122.4,47.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51955816e4b0a933d82c4c8d","contributors":{"authors":[{"text":"Wedemeyer, Gary A.","contributorId":30668,"corporation":false,"usgs":true,"family":"Wedemeyer","given":"Gary","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":478644,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045979,"text":"ofr20131064 - 2013 - Geochemical results from stream-water and stream-sediment samples collected in Colorado and New Mexico","interactions":[],"lastModifiedDate":"2013-05-16T11:28:55","indexId":"ofr20131064","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","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":"2013-1064","title":"Geochemical results from stream-water and stream-sediment samples collected in Colorado and New Mexico","docAbstract":"Scientists from the U.S. Geological Survey are studying the relationship between watershed lithology and stream-water chemistry. As part of this effort, 60 stream-water samples and 43 corresponding stream-sediment samples were collected in 2010 and 2011 from locations in Colorado and New Mexico. Sample sites were selected from small to midsize watersheds composed of a high percentage of one rock type or geologic unit. Stream-water and stream-sediment samples were collected, processed, preserved, and analyzed in a consistent manner. This report releases geochemical data for this phase of the study.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131064","usgsCitation":"Hageman, P.L., Todd, A., Smith, K.S., DeWitt, E., and Zeigler, M.P., 2013, Geochemical results from stream-water and stream-sediment samples collected in Colorado and New Mexico: U.S. Geological Survey Open-File Report 2013-1064, Report: iii, 11 p.; 6 Appendices, https://doi.org/10.3133/ofr20131064.","productDescription":"Report: iii, 11 p.; 6 Appendices","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":272316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131064.gif"},{"id":272310,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%201_Bulk%20chemistry%20for%20stream%20sediments.xlsx"},{"id":272308,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1064/"},{"id":272311,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%202_Stream%20water%20(FA).xlsx"},{"id":272312,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%203_Stream%20water%20(RA).xlsx"},{"id":272309,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1064/OF13-1064.pdf"},{"id":272313,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%204_QAQC%20Stream%20sediments.xlsx"},{"id":272314,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%205_QAQC%20Stream%20water%20(FA).xlsx"},{"id":272315,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2013/1064/Appendix%206_QAQC%20Stream%20water%20(RA).xlsx"}],"country":"United States","state":"Colorado;New Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.06,32.81 ], [ -109.06,41.0 ], [ -102.79,41.0 ], [ -102.79,32.81 ], [ -109.06,32.81 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51955815e4b0a933d82c4c89","contributors":{"authors":[{"text":"Hageman, Philip L. 0000-0002-3440-2150 phageman@usgs.gov","orcid":"https://orcid.org/0000-0002-3440-2150","contributorId":811,"corporation":false,"usgs":true,"family":"Hageman","given":"Philip","email":"phageman@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Todd, Andrew S.","contributorId":33162,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew S.","affiliations":[],"preferred":false,"id":478639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeWitt, Ed","contributorId":65081,"corporation":false,"usgs":true,"family":"DeWitt","given":"Ed","affiliations":[],"preferred":false,"id":478640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":478641,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045984,"text":"sir20135066 - 2013 - Estimating irrigation water use in the humid eastern United States","interactions":[],"lastModifiedDate":"2013-05-16T13:41:25","indexId":"sir20135066","displayToPublicDate":"2013-05-16T00:00:00","publicationYear":"2013","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":"2013-5066","title":"Estimating irrigation water use in the humid eastern United States","docAbstract":"Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. 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