Habitat degradation combined with climate change increases the threat of extinction for stream fishes. In response to these threats, efforts to reestablish species within formerly occupied streams or translocation to suitable areas may be effective conservation strategies. In the absence of historic species presence data, identifying locations where suitable habitat exists across many fluvial habitats may limit the effectiveness of reestablishments. We present an approach that ranks habitat for stream fish reestablishment over large areas using best available information. Using the locally extirpated Arctic grayling (Thymallus arcticus) in Michigan, USA as an example, we integrate information on species preferences and relationships between species with similar habitat requirements and landscape predictors of habitat to rank stream suitability. We find that unfragmented streams throughout the historical range of Arctic grayling and areas previously unoccupied by the species are potential locations for conservation action. However, we note that projected increases in summer water temperatures may reduce the amount of thermally suitable habitat in some top-ranked locations by up to 30%. Given its inherent flexibility in data requirements, our landscape-level approach may be a valuable tool that supports planning for species reestablishment.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-021-04791-8","usgsCitation":"Tingley, R.W., Infante, D.M., Dean, E., Schemske, D.W., Cooper, A.R., Ross, J., and Daniel, W., 2022, A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan: Hydrobiologia, v. 849, p. 1397-1415, https://doi.org/10.1007/s10750-021-04791-8.","productDescription":"19 p.","startPage":"1397","endPage":"1415","ipdsId":"IP-125422","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Tingley, Ralph William 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":258043,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","email":"","middleInitial":"William","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":840119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Infante, Dana M.","contributorId":146114,"corporation":false,"usgs":false,"family":"Infante","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":16583,"text":"Department of Fisheries and Wildlife, 480 Wilson Rd. 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":840120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dean, Emily M.","contributorId":289990,"corporation":false,"usgs":false,"family":"Dean","given":"Emily M.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":840121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schemske, Douglas W.","contributorId":171953,"corporation":false,"usgs":false,"family":"Schemske","given":"Douglas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":840122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, Arthur R. 0000-0002-0557-8560","orcid":"https://orcid.org/0000-0002-0557-8560","contributorId":220307,"corporation":false,"usgs":false,"family":"Cooper","given":"Arthur","email":"","middleInitial":"R.","affiliations":[{"id":7266,"text":"Michigan State University, Department of Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":840123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ross, Jared 0000-0002-0582-3589","orcid":"https://orcid.org/0000-0002-0582-3589","contributorId":289993,"corporation":false,"usgs":false,"family":"Ross","given":"Jared","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":840124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":840125,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227651,"text":"sir20215118 - 2022 - Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","interactions":[{"subject":{"id":70228448,"text":"sir20215118A - 2022 - Hydrogeologic characterization of the Yucaipa groundwater subbasin","indexId":"sir20215118A","publicationYear":"2022","noYear":false,"chapter":"A","displayTitle":"Hydrogeologic Characterization of the Yucaipa Groundwater Subbasin","title":"Hydrogeologic characterization of the Yucaipa groundwater subbasin"},"predicate":"IS_PART_OF","object":{"id":70227651,"text":"sir20215118 - 2022 - Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","indexId":"sir20215118","publicationYear":"2022","noYear":false,"title":"Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California"},"id":1},{"subject":{"id":70228449,"text":"sir20215118B - 2022 - Yucaipa 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Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5118","displayTitle":"Hydrology of the Yucaipa Groundwater Subbasin: Characterization and Integrated Numerical Model, San Bernardino and Riverside Counties, California","title":"Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","docAbstract":"Water management in the Santa Ana River watershed in San Bernardino and Riverside Counties in southern California is a complex task with various water purveyors navigating geographic, geologic, hydrologic, and political challenges to provide a reliable water supply to stakeholders. As the population has increased throughout southern California, so has the demand for water. The Yucaipa groundwater subbasin (hereafter referred to as “Yucaipa subbasin”), one of nine groundwater subbasins in what the California Department of Water Resources (DWR) refers to as the Upper Santa Ana Valley groundwater basin (California Department of Water Resources, 2016; the DWR naming convention is used within this report), is no exception; steady population growth since the 1940s and changes in water use has forced local water purveyors to regularly adapt their water infrastructure to meet demand. Groundwater has historically been the dominant source of water in the Yucaipa subbasin although recently, imported water via the California State Water Project has augmented the total water supply. Despite the influx of imported water, overall demand for groundwater continues to rise, and there is concern by local water managers that groundwater levels may adversely impact water supply and (or) decline to a point where it will be uneconomical to produce water, severely limiting the ability of local agencies to meet water-supply demand.
To better understand the hydrogeology and water resources in the Yucaipa subbasin, the U.S. Geological Survey (USGS) and the San Bernardino Valley Municipal Water District initiated a cooperative study to understand the hydrogeologic system of the Yucaipa subbasin and in the encompassing Yucaipa Valley watershed (YVW). A three-dimensional hydrogeologic framework model was constructed to quantify the structure and extent of hydrogeologic units. Historical and present-day groundwater conditions were characterized to evaluate the groundwater-flow system. Lastly, the Yucaipa Integrated Hydrological Model (YIHM) was developed to simulate the integrated surface-water and groundwater systems, including natural and anthropogenic (that is, human influenced) recharge and discharge throughout the study area from 1947 to 2014.
The Yucaipa subbasin is an inland groundwater basin located about 12 miles (mi) southeast of the City of San Bernardino and about 75 mi east of Los Angeles, California. The subbasin encompasses about 39 square miles (mi2), including the City of Yucaipa. The geographic extent of the Yucaipa subbasin was established by the California Department of Water Resources, who defined the boundaries of the subbasin based on hydrogeologic transitions between crystalline rock and basin-fill sediments, active fault strands, surface-water drainage divides, and a portion of an adjudicated groundwater management boundary. Two groundwater subbasins of the Upper Santa Ana Valley groundwater basin are adjacent to the Yucaipa subbasin, the San Bernardino groundwater subbasin to the west and the San Timoteo groundwater subbasin to the south.
The Yucaipa subbasin is encompassed by the YVW, which is in turn comprised of three sub-watersheds that represent surface-water flow across and within the Yucaipa subbasin. Although the Yucaipa subbasin is the specific area of interest for this study, the entire YVW was considered for the purposes of characterizing the hydrogeology of the Yucaipa subbasin and for development of the YIHM.
The purposes of this report are to (1) describe the hydrologic and hydrogeologic settings of the Yucaipa subbasin and aquifer system, (2) describe the construction and calibration of the fully coupled groundwater and surface-water flow model for the Yucaipa subbasin and the encompassing YVW, referred to as the YIHM, and (3) present numerical results, including water budgets and hydraulic heads, and the effect of pumping and climate stresses (precipitation and temperature) on water-budget components.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215118","collaboration":"Prepared in cooperation with San Bernardino Valley Municipal Water District","usgsCitation":"Cromwell, G., and Alzraiee, A., eds., 2022, Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California: U.S. Geological Survey Scientific Investigations Report 2021–5118, 4 p., https://doi.org/10.3133/sir20215118.","productDescription":"Executive Summary: vi, 4 p.; Chapter A: viii, 81 p.; Chapter B: xii, 76 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123424","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":394827,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5118/images"},{"id":394823,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F7OYQR","text":"Data release of hydrogeologic data of the Yucaipa groundwater subbasin, San Bernardino and Riverside Counties, California"},{"id":394776,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5118/covrthb.jpg"},{"id":394777,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118.pdf","text":"Executive Summary","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":502121,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112154.htm","linkFileType":{"id":5,"text":"html"}},{"id":394835,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118b.xml"},{"id":394834,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118a.xml"},{"id":394826,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118.xml"},{"id":394825,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K540DV","text":"GSFLOW model to evaluate the effect of groundwater pumpage and climate stresses on the integrated hydrologic system of the Yucaipa subbasin, Yucaipa Valley watershed, San Bernardino and Riverside Counties, California"}],"country":"United States","state":"California","county":"Riverside County, San Bernardino County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.257080078125,\n 33.899486813913285\n ],\n [\n -116.87736511230469,\n 33.899486813913285\n ],\n [\n -116.87736511230469,\n 34.098159345215535\n ],\n [\n -117.257080078125,\n 34.098159345215535\n ],\n [\n -117.257080078125,\n 33.899486813913285\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high‐rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter‐level displacement accuracy via precise point positioning methods. Real‐time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post‐processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high‐rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019
We present a kinematic slip model of the July 8, 2021 Antelope Valley earthquake from a finite-source inversion based on regional seismic waveforms and static offsets from GPS and InSAR. Seismic waveforms are employed at 6s dominant period out to 100 km from the epicenter, and the combined GPS and InSAR datasets cover the near field and far field out to ∼ 100 km and constrain the overall rupture size. The aftershock pattern defines a nearly north-striking, 50◦ east-dipping fault plane. We find a unilateral rupture along this fault plane propagating southward and updip with predominantly normal slip up to ∼ 1.5m. The estimated seismic moment of 8.47 × 10 22 17 Nm is equivalent to Mw 5.92. A finite-source inversion that retains seismic waveforms and GPS static offsets but omits InSAR range changes yields a seismic moment of 1.08 × 10 25 18 Nm (Mw 5.99). Despite vigorous aftershock activity between 10 km and Earth’s surface, coseismic slip is concentrated in the depth interval 7 - 10 km.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320210043","usgsCitation":"Pollitz, F., Wicks, C., and Hammond, W.M., 2022, Kinematic slip model of the July 8, 2021 M6.0 Antelope Valley, California, earthquake: The Seismic Record, v. 2, no. 1, p. 20-28, https://doi.org/10.1785/0320210043.","productDescription":"9 p.","startPage":"20","endPage":"28","ipdsId":"IP-133557","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":449047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320210043","text":"Publisher Index Page"},{"id":421846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.00,\n 39.00\n ],\n [\n -120.00,\n 38.00\n ],\n [\n -119.00,\n 38.00\n ],\n [\n -119.00,\n 39.00\n ],\n [\n -120.00,\n 39.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":885944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":885945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammond, William M","contributorId":292777,"corporation":false,"usgs":false,"family":"Hammond","given":"William","email":"","middleInitial":"M","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":885946,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262545,"text":"70262545 - 2022 - Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist","interactions":[],"lastModifiedDate":"2025-01-22T18:32:02.773642","indexId":"70262545","displayToPublicDate":"2022-01-26T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist","docAbstract":"Wildlife managers tasked with understanding habitat and resource selection at the population level attempt to characterize patterns in nature that aid and inform conservation. Resource selection functions (RSFs), such as discrete choice analyses, are the standard convention to characterize the effects of habitat attributes on resource selection patterns. These tools are invaluable for wildlife management and conservation and have proven successful in numerous studies. However, the analysis of small datasets using RSF becomes problematic when attempting to account for complex sources of variation, and the inclusion of factors such as weather or intrinsic variation on target species' response may produce models with poor predictive ability. We compared the application of generalized linear mixed-effects modeling (GLMM) and redundancy analysis (RDA) on Appalachian spotted skunk (Spilogale putorius putorius) den selection data at four study sites within the George Washington, Jefferson, and Monongahela National Forests, and surrounding private lands in the Appalachian Mountains of western Virginia and northeastern West Virginia. We assessed the need for the inclusion of alternative sources of variation (i.e., weather conditions and individual intrinsic variation) in addition to standard habitat attributes to better identify sources of variation in den selection. The RDA elucidated complex and opposing relationships, whereby den type use was based on reproductive status or weather condition, which were not evident in the GLMM model that relied solely on habitat measures. Our results demonstrated the importance of examining resource selection data using multivariate techniques in addition to conventional discrete choice analyses to better understand intricate habitat–species relationships, especially for small datasets. Furthermore, from our analyses, we proposed that spotted skunks are neither a true generalist nor specialist species. We introduced and define the term “conditional specialist” to represent a species that is conditionally selective of a given resource in response to one or more current environmental or intrinsic conditions.
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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227465,"text":"sir20215135 - 2022 - Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","interactions":[],"lastModifiedDate":"2026-04-08T16:26:04.962799","indexId":"sir20215135","displayToPublicDate":"2022-01-25T13:59:52","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":"2021-5135","displayTitle":"Groundwater Hydrology in the Area of Savannah and Gunstocker Creeks in Northeastern Hamilton, Southern Meigs, and Northwestern Bradley Counties, Tennessee, 2007–09","title":"Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","docAbstract":"The U.S. Geological Survey, in cooperation with the Savannah Valley Utility District, evaluated the groundwater hydrology of the Valley and Ridge carbonate rock aquifer in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, from 2007 through 2009. The evaluation included, and built on, the results of test drilling conducted in the area in 1974 to determine the potential for groundwater as a source of public supply for the utility and the results of an investigation conducted to define recharge areas for wells used by groundwater-source public-supply water systems throughout Hamilton County in the early 1990s.
Groundwater-level data collected from wells open to the aquifer in the study area were used to prepare potentiometric-surface maps for fall 1992, spring and fall 1993, summer 2008, and spring 2009 conditions. Two primary groundwater basins were delineated from the maps—the larger of which coincides with the watershed of Savannah Creek in the southern part of the study area and the smaller of which coincides with the watershed of Gunstocker Creek in the northern part of the study area. Both basins are characterized by potentiometric surfaces that contain a central area of low-altitude groundwater levels and low gradients relative to the basin margins that reflect the orientation of enhanced permeability along dissolution-enlarged features that have developed parallel to strike in the aquifer. The recharge area of the Savannah Creek groundwater basin is estimated to be about 31 square miles, and the recharge area of the Gunstocker Creek groundwater basin is estimated to be about 17 square miles.
Recharge to the aquifer in the Savannah Creek and Gunstocker Creek groundwater basins primarily occurs in the uplands area along White Oak Mountain in the eastern part of the study area and along the western boundaries of the basins. Groundwater flows toward the potentiometric lows in each basin, discharging as base flow to the streams and to springs locally. Groundwater withdrawals for public supply by the utility influence the potentiometric low in the north-central part of the Savannah Creek groundwater basin and disrupt flow in the creek and nearby Anderson Spring, particularly during the summer and fall seasons. No large groundwater withdrawals currently occur in the Gunstocker Creek basin, but there is potential for groundwater supply development in the basin.
A conceptual model of the groundwater hydrology of the area developed from the evaluation indicates that Chickamauga Lake is the base-level control on groundwater discharge from the Savannah Creek and Gunstocker Creek basins and that lake stage affects the potentiometric surfaces and groundwater discharge in the most downgradient parts of the basins as a result of inferred hydraulic connection between the aquifer and the lake. The model also infers that captured surface water from sections of Savannah Creek and the Hiwassee River that are embayed by the lake could recharge the aquifer and serve as a source of water withdrawn by wells in each basin if the potentiometric surfaces were lowered to altitudes less than the stage of the lake, particularly under potential future groundwater-development scenarios in the Gunstocker Creek basin.
Geochemical analysis of samples collected from six wells for the study indicate that groundwater in the Valley and Ridge aquifer in the area generally is a calcium-magnesium-bicarbonate type, and although the water generally is hard, it is suitable for most uses. Trace-element concentrations were less than primary drinking-water criteria in all the samples.
Results of the investigation indicate that options are available for additional groundwater withdrawal in the study area. Water-level data collected since 1975 at the Savannah Valley Utility District Smith Road well site indicate that some additional amount of groundwater is available for withdrawal from the aquifer in the Savannah Creek groundwater basin. The potentiometric low within the Gunstocker Creek groundwater basin indicates that an area with enhanced permeability is present as a northeastern counterpart to the potentiometric low within the Savannah Creek basin. Because the Gunstocker Creek basin is about one-half the total area of the Savannah Creek basin, a commensurate decrease in available groundwater storage is likely. Furthermore, groundwater withdrawal locations in the Gunstocker Creek basin would be closer to—and possibly connected hydraulically to—the Hiwassee River, thus increasing the potential for induced surface-water recharge in the basin if sustained drawdown from pumping lowered groundwater levels to altitudes less than the stage of the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215135","isbn":"978-1-4113-4435-8","collaboration":"Prepared in cooperation with the Savannah Valley Utility District","programNote":"Water Availability and Use Science Program","usgsCitation":"Carmichael, J.K., 2022, Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09: U.S. Geological Survey Scientific Investigations Report 2021–5135, 31 p., 5 pls., https://doi.org/10.3133/sir20215135.","productDescription":"Report: vii, 31 p.; Data Release; 5 Plates: 20.00 x 30.00 inches or smaller","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-104265","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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2009"},{"id":394457,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5135/sir20215135_plates.pdf","text":"Plates 1–5","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5135 Plates"}],"country":"United States","state":"Tennessee","county":"Bradley County, Meigs 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Fluvial systems provide a variety of habitats that support thousands of species including many that are threatened or endangered. Moreover, these habitats, which range from aquatic and riparian to floodplain, are important for the variety of ecosystem services they provide. In addition to water temperature and streamflow change, geomorphic change is important and warrants consideration as one of the several potential threats to these habitats posed by climate change. The geomorphic response of fluvial systems to global warming in temperate environments, for example, caused by an increase in the frequency and magnitude of floods, is important because geomorphology is a primary determinant of habitat availability and quality. Possible geomorphic responses include increased erosion and (or) deposition in the river channel, riparian zone, and floodplain with associated habitat implications. Geomorphic changes caused by global warming can be beneficial (e.g., increased habitat complexity) or detrimental (e.g., mortality caused by scour or burial) to biota. The ability of a species to respond to and survive disturbances, including geomorphic changes, will depend on the nature of the disturbances and the sensitivity and adaptive capabilities of the species. Post-flood recovery often is rapid; however, for certain species (e.g., periphyton, macroinvertebrates), changes in community composition may persist. Increased flood frequency, sediment mobility, and associated geomorphic changes potentially will result in more frequent and persistent changes in habitat and community composition in the affected fluvial systems.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3938","usgsCitation":"Juracek, K.E., and Fitzpatrick, F., 2022, Geomorphic responses of fluvial systems to climate change: A habitat perspective: River Research and Applications, v. 38, no. 4, p. 757-775, https://doi.org/10.1002/rra.3938.","productDescription":"19 p.","startPage":"757","endPage":"775","ipdsId":"IP-127640","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Juracek, Kyle E. 0000-0002-2102-8980 kjuracek@usgs.gov","orcid":"https://orcid.org/0000-0002-2102-8980","contributorId":2022,"corporation":false,"usgs":true,"family":"Juracek","given":"Kyle","email":"kjuracek@usgs.gov","middleInitial":"E.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":835745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835746,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227634,"text":"dr1146 - 2022 - Alaska Volcano Observatory archive of seismic drum records of eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 earthquake swarm at Akutan Peak","interactions":[],"lastModifiedDate":"2026-03-16T19:57:25.979118","indexId":"dr1146","displayToPublicDate":"2022-01-24T12:58:39","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1146","displayTitle":"Alaska Volcano Observatory Archive of Seismic Drum Records of Eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 Earthquake Swarm at Akutan Peak","title":"Alaska Volcano Observatory archive of seismic drum records of eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 earthquake swarm at Akutan Peak","docAbstract":"The advent of continuous digital recording of seismograph stations in Alaska did not occur until the fall of 2002. Continuous records of seismic waveforms prior to 2002 were recorded only in analog form. The Alaska Volcano Observatory (AVO) has a substantial archive of continuous analog records made on helicorders in a collection maintained by the University of Alaska Fairbanks Geophysical Institute. As part of the response to the 2006 Augustine Volcano eruption, the AVO scanned analog drum records of the 1986 Augustine eruption to aid in comparing the progression of volcanic seismicity in 2006 with the seismic record of the 1986 eruption. The scanned records proved useful, prompting subsequent efforts to preserve records from other notable episodes of volcanic unrest as readily available scanned images. The data archive accompanying this report contains scanned images of select drum records for the eruptions at Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), as well as for the 1996 earthquake swarm at Akutan Peak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1146","usgsCitation":"Dixon, J.P., and Power, J.A., 2022, Alaska Volcano Observatory archive of seismic drum records of eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 earthquake swarm at Akutan Peak: U.S. Geological Survey Data Report 1146, 10 p., https://doi.org/10.3133/dr1146.","productDescription":"Report: v, 10 p.; 5 Databases","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117992","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501202,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112152.htm","linkFileType":{"id":5,"text":"html"}},{"id":394675,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_PavlofVolcano.zip","text":"Scanned images of select drum records for the 1996 eruption at Pavlof Volcano","size":"1.35 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394672,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AkutanVolcano.zip","text":"Scanned images of select drum records for the 1996 earthquake swarm at Akutan Volcano","size":"300 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394674,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_MountSpurr.zip","text":"Scanned images of select drum records for the 1992 eruption at Mount Spurr","size":"620 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394673,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AugustineVolcano.zip","text":"Scanned images of select drum records for the 1986 eruption at Augustine Volcano","size":"150 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394676,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_RedoubtVolcano.zip","text":"Scanned images of select drum records for the 1989–90 eruption at Redoubt Volcano","size":"2 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1146/dr1146.pdf","text":"Report","size":"12 MB"},{"id":394670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1146/covrthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano, Redoubt Volcano, Mount Spurr, and Pavlof Volcano, Akutan Peak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.6163330078125,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.30726326510286\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.0999755859375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.212533353918424\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.8143310546875,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.0649302984405\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.2286376953125,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 53.98839506479995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.11013793945312,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.20865504825601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The West Napa Fault has previously been mapped as extending ~45 kilometers (km) from northern Vallejo to southern Saint Helena, California, dominantly running along the western edge of Napa Valley. A zone of fault strands (some previously unmapped) along a ~15-km section of the fault ruptured during the 2014 magnitude 6.0 South Napa earthquake, illustrating the need for further investigation of this little-studied structure. Based on light detection and ranging (lidar) topography and field examination, the fault zone likely extends an additional 10 km or more northward past Saint Helena. In this vicinity, geomorphology suggests two fault strands, one along the range front and another associated with a line of rounded hills that rise 5–10 meters above the middle of the valley. In 2017, we excavated two trenches across an apparent fault scarp on the east side of one elongate hill near Ehlers Lane north of Saint Helena. Examination of the walls revealed three main sedimentary packages. The oldest package, weakly lithified alluvial fan gravels with local sand and silt layers, is tilted 25°–35° to the west. Overlying these tilted strata are two younger sets of strata. On the west side, underlying the crest of the scarp, are alluvial fan gravels with local sand and silt lenses, potentially tilted a few degrees to the west. On the east side, deposited against the scarp, are much finer grained (dominantly fine sand to silt) subhorizontal fluvial strata, likely overbank deposits from the Napa River. We obtained age control on the two younger units through a combination of radiocarbon, infrared-stimulated luminescence, and obsidian hydration dating, establishing that they are latest Pleistocene to modern in age. Although there are no prominent unconformities within the alluvial fan sediments, sample dating indicates there are two generations, one in the 10–20 thousand year (ka) age range and one in the <3 ka age range. Owing to a general lack of well-defined laterally continuous alluvial fan units, it is difficult to distinguish contacts between the two generations except in the immediate proximity of dated samples. The river sediments approximately span the Holocene. No faults were apparent in either trench, indicating that any fault related to the observed surface deformation has not ruptured to the surface at this site during the Holocene and is likely blind.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221002","usgsCitation":"Philibosian, B.E., Sickler, R.R., Prentice, C.S., Pickering, A.J., Gannon, P., Broudy, K.N., Mahan, S.A., Titular, J.N., Turner, E.A., Folmar, C., Patterson, S.F., and Bowman, E.E., 2022, Photomosaics and logs associated with study of West Napa Fault at Ehlers Lane, north of Saint Helena, California: U.S. Geological Survey Open-File Report 2022–1002, 1 sheet, pamphlet 8 p., https://doi.org/10.3133/ofr20221002.","productDescription":"Report: iv, 8 p.; 1 Sheet: 82.00 x 43.00 inches","numberOfPages":"8","additionalOnlineFiles":"Y","ipdsId":"IP-114127","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501537,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112153.htm","linkFileType":{"id":5,"text":"html"}},{"id":394764,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_sheet.pdf","size":"80 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_pamphlet.pdf","text":"Pamphlet","size":"300 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":394762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1002/covrthb.jpg"}],"country":"United States","state":"California","city":"Saint Helena","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.58407592773438,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.47294404791815\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The palila (Loxioides bailleui) population on Mauna Kea Volcano, Hawai‘i Island, was estimated from annual surveys in 2019−2021, and a trend analysis was performed on survey data from 1998−2021. The 2019 population was estimated at 1,030−1,899 birds (point estimate: 1,432), the 2020 population was estimated at 964−1,700 birds (point estimate: 1,312), and the 2021 population was estimated at 452−940 birds (point estimate: 678). Since 1998, a visual inspection of the size of the area containing palila detections on the western slope based on the minimum/maximum elevations has not shown a substantial change, indicating that the range of the species has remained stable; although this area represents only about 5% of its historical extent. During 1998−2005, palila numbers fluctuated between 4,000 and 6,000, followed by a steep decline. After 2010, palila estimates stabilized around an abundance of 2,000 with a much slower rate of decline. The decline during 1998−2021 was on average 229 birds per year with very strong statistical support for an overall downward trend in abundance. Over the 23-year monitoring period, the estimated rate of change equated to an 89% decline in the population.
","language":"English","publisher":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","usgsCitation":"Genz, A., Brinck, K., Asing, C.K., Berry, L., Camp, R.J., and Banko, P.C., 2022, 2019-2021 Palila abundance estimates and trend: Hawaii Cooperative Studies Unit Technical Report 101, iii, 17 p.","productDescription":"iii, 17 p.","ipdsId":"IP-134919","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":395157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395121,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/6858"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.62477111816406,\n 19.728573985770815\n ],\n [\n -155.35,\n 19.728573985770815\n ],\n [\n -155.35,\n 19.91913050246103\n ],\n [\n -155.62477111816406,\n 19.91913050246103\n ],\n [\n -155.62477111816406,\n 19.728573985770815\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Genz, Ayesha 0000-0002-2916-1436","orcid":"https://orcid.org/0000-0002-2916-1436","contributorId":196671,"corporation":false,"usgs":false,"family":"Genz","given":"Ayesha","email":"","affiliations":[],"preferred":false,"id":832294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":832319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":832296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berry, Lainie","contributorId":272646,"corporation":false,"usgs":false,"family":"Berry","given":"Lainie","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":832297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":832298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":832299,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228233,"text":"70228233 - 2022 - Terrestrial ecosystem modeling with IBIS: Progress and future vision","interactions":[],"lastModifiedDate":"2022-03-30T15:19:39.880176","indexId":"70228233","displayToPublicDate":"2022-01-24T09:21:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5535,"text":"Journal of Resources and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem modeling with IBIS: Progress and future vision","docAbstract":"Dynamic Global Vegetation Models (DGVM) are powerful tools for studying complicated ecosystem processes and global changes. This review article synthesizes the developments and applications of the Integrated Biosphere Simulator (IBIS), a DGVM, over the past two decades. IBIS has been used to evaluate carbon, nitrogen, and water cycling in terrestrial ecosystems, vegetation changes, land-atmosphere interactions, land-aquatic system integration, and climate change impacts. Here we summarize model development work since IBIS v2.5, covering hydrology (evapotranspiration, groundwater, lateral routing), vegetation dynamics (plant functional type, land cover change), plant physiology (phenology, photosynthesis, carbon allocation, growth), biogeochemistry (soil carbon and nitrogen processes, greenhouse gas emissions), impacts of natural disturbances (drought, insect damage, fire) and human induced land use changes, and computational improvements. We also summarize IBIS model applications around the world in evaluating ecosystem productivity, carbon and water budgets, water use efficiency, natural disturbance effects, and impacts of climate change and land use change on the carbon cycle. Based on this review, visions of future cross-scale, cross-landscape and cross-system model development and applications are discussed.
","language":"English","publisher":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences","doi":"10.5814/j.issn.1674-764x.2022.01.001","usgsCitation":"Liu, J., Lu, X., Zhu, Q., Yuan, W., Yuan, Q., Zhang, Z., Guo, Q., and Deering, C., 2022, Terrestrial ecosystem modeling with IBIS: Progress and future vision: Journal of Resources and Ecology, v. 13, no. 1, p. 2-16, https://doi.org/10.5814/j.issn.1674-764x.2022.01.001.","productDescription":"15 p.","startPage":"2","endPage":"16","ipdsId":"IP-132711","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":395617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":833489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Xuehe","contributorId":175216,"corporation":false,"usgs":false,"family":"Lu","given":"Xuehe","email":"","affiliations":[{"id":27538,"text":"International Institute for Earth System Science, Nanjing University, Xianlin Avenue 163, Nanjing 210093","active":true,"usgs":false}],"preferred":false,"id":833490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":833491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Wenping","contributorId":274900,"corporation":false,"usgs":false,"family":"Yuan","given":"Wenping","affiliations":[{"id":56683,"text":"Sun Yat-sen University, China","active":true,"usgs":false}],"preferred":false,"id":833492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Quanzhi","contributorId":274901,"corporation":false,"usgs":false,"family":"Yuan","given":"Quanzhi","email":"","affiliations":[{"id":56684,"text":"Sichuan Normal University, China","active":true,"usgs":false}],"preferred":false,"id":833493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":833494,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guo, Qingxi","contributorId":274902,"corporation":false,"usgs":false,"family":"Guo","given":"Qingxi","email":"","affiliations":[{"id":56685,"text":"Northeast Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":833495,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":833496,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227638,"text":"70227638 - 2022 - A novel regression method for harmonic analysis of time series","interactions":[],"lastModifiedDate":"2023-11-08T16:37:20.074235","indexId":"70227638","displayToPublicDate":"2022-01-24T08:51:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A novel regression method for harmonic analysis of time series","docAbstract":"Harmonic analysis of time series is an important technique in remote sensing to reveal seasonal land surface dynamics. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and the time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) in different scenarios using Landsat time series data across the United States. First, we evaluated methods using the Landsat surface reflectance time series within a single year. HAPO showed low and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series. HAPO was a better predictor of relatively short time series (< 4 years) with steady low RMSD values. When a longer time series ( 4 years) was used, all four methods showed similar RMSD values, but HAPO outperformed the other methods if there were temporal gaps. Therefore, for places with large seasonal observation gaps or for time series that are relatively short (less than 4 years), HAPO can provide more consistent and accurate results in harmonic analysis of time series.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2022.01.006","usgsCitation":"Zhou, Q., Zhu, Z., Xian, G.Z., and Li, C., 2022, A novel regression method for harmonic analysis of time series: ISPRS Journal of Photogrammetry and Remote Sensing, v. 185, p. 48-61, https://doi.org/10.1016/j.isprsjprs.2022.01.006.","productDescription":"14 p.","startPage":"48","endPage":"61","ipdsId":"IP-127335","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":449052,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2022.01.006","text":"Publisher Index Page"},{"id":435991,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VYPPLI","text":"USGS data release","linkHelpText":"Harmonic Adaptive Penalty Operator (HAPO)"},{"id":394758,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":831464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":198887,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":831465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":831466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":831467,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233564,"text":"70233564 - 2022 - A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","interactions":[],"lastModifiedDate":"2022-07-26T11:55:11.529587","indexId":"70233564","displayToPublicDate":"2022-01-24T06:49:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","docAbstract":"An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.
Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon (Acipenser fulvescens). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.
Improved understanding of angling behavior in recreational fisheries can help managers account for partial controllability in systems in which angling effort is not directly regulated. Relevant aspects of angling behavior include fishing participation, site choice, and catch-and-release actions and can be considered at scales of both aggregate fishing effort and individual-decision-making. Using creel survey data collected across eight fishery seasons, we predicted aggregate angling effort (e.g., daily number of boating trips engaged in fishing) and characterized influences of individual trip-based site choice for recreational fisheries acting on fall-run Chinook salmon and coho salmon near the mouth of the Columbia River, United States of America. We applied an overdispersed Poisson likelihood-based generalized linear model with an autoregressive structure to aggregate boating effort and a multinomial logit model to trip-based site choice decisions among separate ocean and estuary fishing zones. Predictive models explained 71% and 79% of out-of-sample and in-sample variability in aggregate effort, respectively, and included the covariates weekend status, Chinook salmon catch rate, tidal range, and pre-season expectations of fish abundance. In addition to reinforcing the importance of Chinook salmon catch rate and tidal range, site choice model selection revealed influences of weather, fishery restrictions, expected fishery season lengths, and the individual-specific characteristics of boat length and guide status on decision-making. Model results provide predictive models for short-term fishery planning, highlight heterogeneity in individual decision-making, and illustrate the value of standardized creel data for evaluating angling behavior.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106235","usgsCitation":"Jensen, A.J., Dundas, S., and Peterson, J., 2022, Phenomenological and mechanistic modeling of recreational angling behavior using creel data: Fisheries Research, v. 249, 106235, 15 p., https://doi.org/10.1016/j.fishres.2022.106235.","productDescription":"106235, 15 p.","ipdsId":"IP-125527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Buoy 10 Fishery, Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.02658508278576,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.242723068563095\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationDate":"2022-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jensen, Alexander J.","contributorId":272039,"corporation":false,"usgs":false,"family":"Jensen","given":"Alexander","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":934953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dundas, Steven J.","contributorId":354015,"corporation":false,"usgs":false,"family":"Dundas","given":"Steven J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":934954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934952,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227637,"text":"pp1842C - 2022 - The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","interactions":[{"subject":{"id":70227637,"text":"pp1842C - 2022 - The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","indexId":"pp1842C","publicationYear":"2022","noYear":false,"chapter":"C","displayTitle":"The Effects of Management Practices on Grassland Birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","title":"The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2024-06-26T14:34:28.090181","indexId":"pp1842C","displayToPublicDate":"2022-01-21T18:53:49","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"C","displayTitle":"The Effects of Management Practices on Grassland Birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","title":"The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","docAbstract":"The keys to Greater Prairie-Chicken (Tympanuchus cupido pinnatus) management are maintaining expansive grasslands; preventing populations of Greater Prairie-Chickens from becoming small and isolated; managing grasslands to maintain proper grassland height, density, and vigor; and reducing woody plant invasion and excessive litter buildup. Within these grasslands, areas should contain short herbaceous cover for lek sites; tall residual grasses for nesting; and disturbed habitats for broods with adequate vegetation regrowth that provides insects for food and cover from predators and weather. This account does not address population or harvest management but rather focuses on habitat management. Greater Prairie-Chickens have been reported to use habitats with 5–113 centimeter (cm) average vegetation height, 5–40 cm visual obstruction reading, 18–95 percent grass cover, 1–35 percent forb cover, <45 percent litter cover, <5 percent shrub cover, 3–25 percent bare ground, and <12 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842C","usgsCitation":"Svedarsky, W.D., Toepfer, J.E., Westemeier, R.L., Robel, R.J., Igl, L.D., and Shaffer, J.A., 2022, The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus), chap. C of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 53 p., https://doi.org/10.3133/pp1842C.","productDescription":"v, 53 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096441","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":394716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/c/pp1842c.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–C"},{"id":394715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/c/coverthb.jpg"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the Mojave River Dam study area near Hesperia, California, in 2021. Four vireo surveys were conducted between April 16 and July 16, 2021, and three flycatcher surveys were conducted between May 27 and July 16, 2021.
We detected four territorial male vireos, including two that were paired and two with undetermined breeding status. No juveniles were observed during surveys. Vireo territories were found in three habitat types: (1) riparian scrub, (2) willow-cottonwood, and (3) willow-sycamore, with willow-cottonwood being the most commonly recorded habitat type. Red or arroyo willow (Salix laevigata or lasiolepis) was the dominant plant species in most vireo territories.
No territorial or transient flycatchers were observed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1149","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2021 Data Summary: U.S. Geological Survey Data Report 1149, 7 p., https://doi.org/10.3133/dr1149.","productDescription":"vi, 7 p.","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-135057","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1149/covrthb.jpg"},{"id":394667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394668,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.xml"},{"id":394669,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1149/images"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Mojave River Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.28866577148438,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.32132236979802\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The native Yellowstone Cutthroat Trout (Oncorhynchus clarkii bouvieri Jordan and Gilbert, 1883) population in Yellowstone Lake, Yellowstone National Park, Wyoming, USA, is in decline because of competition from the introduced, invasive Lake Trout (Salvelinus namaycush Walbaum in Artedi, 1792). Gillnetting is used to suppress adult Lake Trout; however, methods are being developed to suppress embryos, including adding Lake Trout carcasses and carcass-analog pellets to spawning sites. Decomposing carcasses and analog pellets cause decreased dissolved oxygen concentrations thereby leading to Lake Trout embryo mortality, but the effects of these methods on primary producers are unknown. We deployed in-situ nutrient diffusing substrates (NDS) at 3 spawning sites. The 1st site was treated with carcasses, the 2nd site was treated with analog pellets, and a 3rd lacked treatment (control). To estimate how suppression measures may alter nutrient limitation, we measured algal biomass in 6 NDS amendments at each site: nothing (control), N, P, N + P, ground carcasses, or pulverized analog pellets. We deployed 5 replicates of each amendment at each site before and after treating whole sites. N and P co-limited periphyton before carcasses or analog pellets were added to spawning sites (p < 0.01); however, nutrients were not limiting after the treatments were added to spawning sites (p = 0.31–1). Algal biomass was 4× higher after whole-site carcass treatments. In contrast, analog pellets appeared to suppress algal biomass in the amendments (20% of NDS at the control site post-treatment) and in the treatment plot (33% of pre-treatment biomass at analog pellet site). We also measured how individual ingredients in analog pellets altered periphyton biomass, which suggested that vitamin E, estrogen, and soybean oil ingredients reduced the growth of primary producers. Suppression methods may stimulate or reduce algal biomass, depending on the methods used, which could have cascading effects on food webs and potentially reduce the success of the control measures. Estimating how different Lake Trout suppression methods may alter basal resources in the littoral zone of Yellowstone Lake will help natural resource agencies develop effective plans to control invasive predators at early life stages while minimally altering ecosystems.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/718647","usgsCitation":"Lujan, D., Tronstad, L., Briggs, M., Albertson, L., Glassic, H., Guy, C.S., and Koel, T.M., 2022, Response of nutrient limitation to invasive fish suppression: How carcasses and analog pellets alter periphyton: Freshwater Science, v. 41, no. 1, p. 88-99, https://doi.org/10.1086/718647.","productDescription":"12 p.","startPage":"88","endPage":"99","ipdsId":"IP-126589","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.61271313918546,\n 44.60032951054663\n ],\n [\n -110.61271313918546,\n 44.26210599708054\n ],\n [\n -110.16635745527641,\n 44.26210599708054\n ],\n [\n -110.16635745527641,\n 44.60032951054663\n ],\n [\n -110.61271313918546,\n 44.60032951054663\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lujan, Dominique R.","contributorId":286901,"corporation":false,"usgs":false,"family":"Lujan","given":"Dominique R.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tronstad, Lusha M.","contributorId":338214,"corporation":false,"usgs":false,"family":"Tronstad","given":"Lusha M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Michelle A.","contributorId":286899,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":903689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Albertson, Lindsey K.","contributorId":337789,"corporation":false,"usgs":false,"family":"Albertson","given":"Lindsey K.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":903690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glassic, Hayley C.","contributorId":288563,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":903691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":903686,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koel, Todd M","contributorId":276047,"corporation":false,"usgs":false,"family":"Koel","given":"Todd","email":"","middleInitial":"M","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":903692,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256741,"text":"70256741 - 2022 - Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass","interactions":[],"lastModifiedDate":"2024-09-04T15:05:32.800422","indexId":"70256741","displayToPublicDate":"2022-01-21T09:47:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass","docAbstract":"Understanding the thermal tolerances of stream fishes, including sport fishes, is important for assessing thermal stressors that are common across the landscape. Our study objectives were to determine the thermal tolerances of 17 stream fishes (15 species and 2 genetically distinct populations of juvenile Smallmouth Bass Micropterus dolomieu: the Neosho subspecies M. dolomieu velox and the Ouachita strain M. sp. cf. dolomieu velox). Fish were collected from the field and acclimated to laboratory conditions at 20°C or 25°C, with dissolved oxygen maintained above 6 mg/L. We determined the critical thermal maximum (CTM) using an incomplete block design with 9–11 replications for each species. During trials, we increased the water temperature at a rate of 2°C per hour until fish experienced loss of equilibrium. The estimated CTM ranged from 32.43°C to 38.26°C among species. The CTM values differed significantly between taxonomic groups and species, including the genetically distinct populations of Smallmouth Bass. The Neosho subspecies of Smallmouth Bass had a significantly lower thermal tolerance than the Ouachita strain at both acclimation temperatures; however, the magnitude of the difference was about 0.5°C greater at the higher acclimation temperature. Closely related species, including the Bigeye Shiner Notropis boops and Kiamichi Shiner N. ortenburgeri, had significantly different thermal tolerances despite occupying similar riverine locations. Our results suggest that our perceptions of a species’ thermal tolerance based on that of closely related species or that of species using similar habitat may be incorrect. Moreover, the differences in thermal tolerances among populations may be an important consideration for conservation and management actions, such as stocking decisions. Laboratory data such as those provided in this study can be integrated with field data to better assess thermal responses of fishes in a changing environment.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10749","usgsCitation":"Brewer, S.K., Mollenhauer, R., Alexander, J., and Moore, D., 2022, Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass: North American Journal of Fisheries Management, v. 42, no. 2, p. 352-360, https://doi.org/10.1002/nafm.10749.","productDescription":"9 p.","startPage":"352","endPage":"360","ipdsId":"IP-128880","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ouachita Mountain ecoregion, Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.8664012156679,\n 36.99681119417036\n ],\n [\n -95.5111277927191,\n 36.20967738751284\n ],\n [\n -95.222344013415,\n 35.57857852939986\n ],\n [\n -94.48359481054393,\n 35.687747319317126\n ],\n [\n -94.62462874927394,\n 36.45854467258876\n ],\n [\n -94.62462874927394,\n 37.01290083943225\n ],\n [\n -94.8664012156679,\n 36.99681119417036\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.43940916621646,\n 34.76329149945565\n ],\n [\n -96.02893897983913,\n 34.719164102780056\n ],\n [\n -96.6840830246433,\n 34.27659649424358\n ],\n [\n -96.35114096908727,\n 33.96540469177903\n ],\n [\n -94.48236943144916,\n 33.992123122916226\n ],\n [\n -94.43940916621646,\n 34.76329149945565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, J.","contributorId":305320,"corporation":false,"usgs":false,"family":"Alexander","given":"J.","email":"","affiliations":[],"preferred":false,"id":908841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, D.E.","contributorId":205713,"corporation":false,"usgs":false,"family":"Moore","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":908842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229821,"text":"70229821 - 2022 - Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","interactions":[],"lastModifiedDate":"2022-05-13T14:55:00.650722","indexId":"70229821","displayToPublicDate":"2022-01-21T09:13:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","title":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","docAbstract":"Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R (mgcv and ggplot2) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (Fratercula cirrhata) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves (Canis lupus ligoni) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3637","usgsCitation":"Thorson, J.T., Arimitsu, M.L., Levi, T., and Roffler, G., 2022, Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie: Ecology, v. 103, no. 5, e3637, 9 p., https://doi.org/10.1002/ecy.3637.","productDescription":"e3637, 9 p.","ipdsId":"IP-128116","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449065,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.3637","text":"External Repository"},{"id":397303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":838473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":838474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levi, Taal","contributorId":191295,"corporation":false,"usgs":false,"family":"Levi","given":"Taal","email":"","affiliations":[],"preferred":false,"id":838475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roffler, Gretchen","contributorId":288945,"corporation":false,"usgs":false,"family":"Roffler","given":"Gretchen","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":838476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227620,"text":"70227620 - 2022 - Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2022-02-15T16:27:30.782829","indexId":"70227620","displayToPublicDate":"2022-01-21T09:10:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","docAbstract":"Sampling method decisions are critical for the effective monitoring and management of fisheries. Deploying the most effective sampling methodologies is particularly important when responding to new invasive species, where early response efforts have the best chances for eradication. In the Laurentian Great Lakes, the invasive Grass Carp Ctenopharyngodon idella is sampled using boat electrofishing and the combination method of boat electrofishing within and around a trammel net enclosure. We conducted a field study to compare the effectiveness of the two methods. We used capture data for surrogate taxa (i.e., Common Carp Cyprinus carpio and buffalo Ictiobus spp.) to compare the two methods because few Grass Carp were collected during the study. The sampling methods were compared within an occupancy modeling framework using an information-criteria model selection approach to evaluate seven alternative models. The base model included sampling method, year, water temperature, and sampling effort as covariates in the detection submodel and assumed that occupancy probability was constant across sites. The other six models built on the base model by including site, water body type (i.e., lentic vs. lotic), and interaction covariates in the detection submodel. The top-performing model, built on the base model, accounted for the influence of water body type and assumed the exchangeability of site effects in the detection submodel. The results indicated that the detection probabilities for both taxa were higher for the combination method than for boat electrofishing, with a median estimated difference in detection probability between the two methods of 0.11 (95% CI: 0.04–0.22) for Common Carp and 0.18 (95% CI: 0.08–0.28) for buffalo. Given that the combination method was more effective for detecting the surrogate taxa, we expect the combination method may be preferable to only boat electrofishing for Grass Carp removal.
Golden eagles (Aquila chrysaetos) are of increasing conservation concern in western North America. Effective conservation measures for this wide-ranging, federally protected raptor species require monitoring frameworks that accommodate strong inference on the status of breeding populations across vast landscapes. We used a broad-scale sampling design to identify relationships between landscape conditions, detection rates, and site occupancy by territorial pairs of golden eagles in coastal southern California, United States. In 2016 and 2017, we surveyed 175 territory-sized sample sites (13.9-km2 randomly selected grid cells) up to four times each year and detected a pair of eagles at least once in 22 (12.6%) sites. The probability of detecting pairs of eagles varied substantially between years and declined with increasing amounts of forest cover at survey sites, which obscured observations of eagles during ground-based surveys. After accounting for variable detection, the mean estimate of expected site occupancy by eagle pairs was 0.156 (SE = 0.081). Site-level estimates of occupancy were greatest (>0.30) at sample sites with more rugged terrain conditions, <20% human development, and lower amounts of scrubland vegetation cover. The proportion of a sample site with open grassland or forest cover was not strongly correlated with occupancy. We estimated that approximately 16% of the 5,338-km2 sampling frame was used by resident pairs of golden eagles, corresponding to a sparsely distributed population of about 60 pairs (95% CI = 19 – 151 pairs). Our study provided baseline data for future surveys of golden eagles along with a widely applicable monitoring framework for identifying spatial conservation priorities in urbanizing landscapes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2021.665792","usgsCitation":"Wiens, D., Bloom, P., Madden, M., Kolar, P., Tracey, J.A., and Fisher, R.N., 2022, Golden eagle occupancy surveys and monitoring strategy in coastal southern California, United States: Frontiers in Ecology and Environment, v. 9, 665792, 11 p., https://doi.org/10.3389/fevo.2021.665792.","productDescription":"665792, 11 p.","ipdsId":"IP-126757","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449068,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.665792","text":"Publisher Index Page"},{"id":435993,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OIPLHH","text":"USGS data release","linkHelpText":"Detection/non-detection data on territorial pairs of golden eagles in coastal southern California, 2016-2017"},{"id":397934,"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 -118.45458984375,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 32.602361666817515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":267230,"corporation":false,"usgs":true,"family":"Wiens","given":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":839325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bloom, Peter H.","contributorId":289557,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter H.","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":839326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madden, Melanie C.","contributorId":289559,"corporation":false,"usgs":false,"family":"Madden","given":"Melanie C.","affiliations":[{"id":36522,"text":"U.S. Navy","active":true,"usgs":false}],"preferred":false,"id":839327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0003-2842-422X rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0003-2842-422X","contributorId":289561,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839330,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227621,"text":"70227621 - 2022 - Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","interactions":[],"lastModifiedDate":"2022-01-21T15:10:11.763046","indexId":"70227621","displayToPublicDate":"2022-01-21T08:57:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","docAbstract":"The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-143-2022","usgsCitation":"Xian, G.Z., Smith, K., Wellington, D., Horton, J., Zhou, Q., Li, C., Auch, R.F., Brown, J.F., Zhu, Z., and Reker, R.R., 2022, Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product: Earth System Science Data, v. 14, p. 143-162, https://doi.org/10.5194/essd-14-143-2022.","productDescription":"20 p.","startPage":"143","endPage":"162","ipdsId":"IP-130588","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-143-2022","text":"Publisher Index Page"},{"id":394657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"14","noUsgsAuthors":false,"publicationDate":"2022-01-19","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":831379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":831380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":831381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":831382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":831383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":831384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":831385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zhu, Zhe 0000-0003-4716-2309","orcid":"https://orcid.org/0000-0003-4716-2309","contributorId":272038,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":831387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reker, Ryan R. 0000-0001-7524-0082 rreker@usgs.gov","orcid":"https://orcid.org/0000-0001-7524-0082","contributorId":174136,"corporation":false,"usgs":true,"family":"Reker","given":"Ryan","email":"rreker@usgs.gov","middleInitial":"R.","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":831388,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70239092,"text":"70239092 - 2022 - Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory","interactions":[],"lastModifiedDate":"2022-12-27T13:21:07.439786","indexId":"70239092","displayToPublicDate":"2022-01-21T07:17:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory","docAbstract":"Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from <1 to 54 km2 in area across the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO) in southwestern Idaho, USA. We found strong relationships between log10SS and log10POC (R2 = 0.38–0.86) that varied across catchments but remained robust across years, one dry and one of the wettest water years on record. Mean annual SS yields varied from 18 to 89 g SS m−2 y−1 and POC from 0.6 to 11.0 g C m−2 y−1 across the four catchments. Water yield explained much of the temporal variation (72%–85%) in SS and POC yields except in a small, snow-dominated headwater catchment where it explained 15%–51%. The largest five water years accounted for 69%–84% of the total SS and POC yields in catchments with 24 y records. All catchments had positive slopes (>0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting greater slopes (0.66–0.97) than smaller ones (0.14–0.16). In addition, most catchments were dominated (80%) by clockwise hysteretic curves. Lack of seasonal exhaustion in the SS-POC relationships, positive C-Q and clockwise relations indicated that these systems were transport-rather than supply limited, and that sediment and POC appeared to be sourced from channel/bank erosion and remobilization. POC yields represent 1%–10% of mean water year net ecosystem exchange depending on elevation; lower elevation catchments may shift from being carbon sinks to sources after accounting for fluvial POC export associated with changes in climate.