This article investigates methods to improve earthquake early warning (EEW) predictions of shaking levels for residents of tall buildings. In the current U.S. Geological Survey ShakeAlert EEW system, regions far from an epicenter will not receive alerts due to low predicted ground‐shaking intensities. However, residents of tall buildings in those areas may still experience significant shaking due to the acceleration amplification caused by tall buildings’ dynamic behavior, as recently experienced by residents of the 52‐story building in downtown Los Angeles (DTLA) during the 2019 M 7.1 Ridgecrest earthquake. Using more than 400 recorded response data acquired from 77 instrumented buildings in California, here we compare the Federal Emergency Management Agency (FEMA) P‐58 and American Society of Civil Engineers (ASCE) 7‐16 simplified equations for peak floor acceleration (PFA), finding that the ASCE estimation is close to the median of data recorded in large and long‐distance events, whereas the current FEMA estimation is not suitable. In the second part of this article, four instrumented tall buildings in DTLA are extensively studied, and the performance of the simplified and response spectrum (RS) methods giving both an estimation of the free‐field horizontal peak ground acceleration (PGA) and pseudospectral acceleration is evaluated. The results show that the RS method is as accurate as the response history analysis as long as the ground‐motion RS is accurate, whereas the ASCE 7‐16 prediction is conservative. However, when ground‐motion RS or PGA is estimated for DTLA using a ground‐motion model (GMM), the performance of the RS method significantly degrades due to underestimation by the GMM at long periods. The results of this study imply that a nonergodic GMM, which may give more accurate prediction in Los Angeles, could improve the results for PFA when the building’s behavior is dominated by a few long‐period fundamental modes, as is the case for the 52‐story building in DTLA.
In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles (Aquila chrysaetos) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66–0.74) for first-year birds to 0.90 (0.88–0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39–0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96–1.05). The PTL model estimated a median allowable take limit of ~2227 (708–4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2544","usgsCitation":"Milsap, B., Zimmerman, G.S., Kendall, W.L., Barnes, J., Braham, M., Bedrosian, B.E., Bell, D.A., Bloom, P.H., Crandall, R.H., Domenech, R., Driscoll, D., Duerr, A.E., Gerhardt, R., Gibbs, S.E., Harmata, A.R., Jacobson, K., Katzner, T., Knight, R., Lockhart, J.M., McIntyre, C., Murphy, R.K., Slater, S.J., Smith, B.W., Smith, J., Stahlecker, D.W., and Watson, J.W., 2022, Age-specific survival rates, causes of death, and allowable take of golden eagles in the western United States: Ecological Applications, v. 32, no. 3, e2544, 22 p., https://doi.org/10.1002/eap.2544.","productDescription":"e2544, 22 p.","ipdsId":"IP-125525","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":449040,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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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 - 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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.
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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.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3913","usgsCitation":"Thorne, E., and Ford, W., 2022, Redundancy analysis reveals complex den use patterns by eastern spotted skunks, a conditional specialist: Ecosphere, v. 13, no. 1, e3913, 20 p., https://doi.org/10.1002/ecs2.3913.","productDescription":"e3913, 20 p.","ipdsId":"IP-120640","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3913","text":"Publisher Index Page"},{"id":480946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Viginia, West 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\"}}]}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorne, Emily D.","contributorId":349621,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":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 Center","active":true,"usgs":true}],"links":[{"id":394456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5135/sir20215135.pdf","text":"Report","size":"3.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5135"},{"id":394455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5135/coverthb.jpg"},{"id":502281,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112151.htm","linkFileType":{"id":5,"text":"html"}},{"id":394458,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QVHDI5","text":"USGS Data Release","linkHelpText":"Geospatial data for groundwater potentiometric-surface maps in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, fall 1992, spring and fall 1993, summer 2008, and spring 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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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
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.
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U.S. Geological Survey
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Menlo Park, CA 94025
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":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"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}],"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":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.
The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3 m. In cases with several models of comparable performance at 3 m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.
Among four extant and declining Chinook salmon (Oncorhynchus tshawytscha) runs in California’s Central Valley, none have declined as precipitously as Sacramento River winter-run Chinook Salmon. In addition to habitat loss, migratory winter-run employ a life history strategy to reside and feed in stopover habitats on their way from freshwaters to the ocean. This life history strategy is widely considered to be a key factor in the continued decline of winter-run. Using acoustic telemetry, we examined conditions that influenced reach-specific movement and survival of outmigrating juveniles during a prolonged, multi-year drought from 2013-2016, followed by one of the wettest years on record in 2017. We modeled how time-varying individual riverine covariates and reach-specific habitat features influenced smolt survival. Model selection favored a model with mean annual flow, intra-annual deviations from the mean flow at the reach scale, reach-specific channel characteristics, and travel time. Mean annual flow had the strongest positive effect on survival. A negative interaction between mean annual flow and intra-annual reach flow indicated that within-year deviations at the reach scale from annual mean flow had larger effects on survival in low flow years. These factors resulted in higher survival in years with pulse flows or high flows. Changes in movement behavior in response to small scale changes in velocity were negatively associated with survival. Covariates of revetment and wooded bank habitat were positively associated with survival but the effect of these fixed habitat features changed depending on whether they were situated in the upper or lower part of the river. Fish exhibited density dependent stopover behavior, with slowed downstream migration in the upper river in the wet years and extending to the lower river in the most critically dry year. This paper contributes two key findings for natural resource managers interested in flow management and targeted habitat restoration. The first is new insight to how the magnitude of pulse flows in dry and wet years affect survival of winter-run. The second is that density dependence influences where stopover habitat is used. Despite this, we identified an area of the river where fish consistently exhibited stopover behavior in all years.
The details and mechanisms for Neogene river reorganization in the U.S. Pacific Northwest and northern Rocky Mountains have been debated for over a century with key implications for how tectonic and volcanic systems modulate topographic development. To evaluate paleo-drainage networks, we produced an expansive data set and provenance analysis of detrital zircon U-Pb ages from Miocene to Pleistocene fluvial strata along proposed proto-Snake and Columbia River pathways. Statistical comparisons of Miocene-Pliocene detrital zircon spectra do not support previously hypothesized drainage routes of the Snake River. We use detrital zircon unmixing models to test prior Snake River routes against a newly hypothesized route, in which the Snake River circumnavigated the northern Rocky Mountains and entered the Columbia Basin from the northeast prior to incision of Hells Canyon. Our proposed ancestral Snake River route best matches detrital zircon age spectra throughout the region. Furthermore, this northerly Snake River route satisfies and provides context for shifts in the sedimentology and fish faunal assemblages of the western Snake River Plain and Columbia Basin through Miocene−Pliocene time. We posit that eastward migration of the Yellowstone Hotspot and its effect on thermally induced buoyancy and topographic uplift, coupled with volcanic densification of the eastern Snake River Plain lithosphere, are the primary mechanisms for drainage reorganization and that the eastern and western Snake River Plain were isolated from one another until the early Pliocene. Following this basin integration, the substantial increase in drainage area to the western Snake River Plain likely overtopped a bedrock threshold that previously contained Lake Idaho, which led to incision of Hells Canyon and establishment of the modern Snake and Columbia River drainage network.
Native forests on tropical islands have been displaced by non-native species, leading to calls for their transformation. Simultaneously, there is increasing recognition that tropical forests can help sequester carbon that would otherwise enter the atmosphere. However, it is unclear if native forests sequester more or less carbon than human-altered landscapes. At Palmyra Atoll, efforts are underway to transform the rainforest composition from coconut palm (Cocos nucifera) dominated to native mixed-species. To better understand how this landscape-level change will alter the atoll’s carbon dynamics, we used field sampling, remote sensing, and parameter estimates from the literature to model the total carbon accumulation potential of Palmyra’s forest before and after transformation. The model predicted that replacing the C. nucifera plantation with native species would reduce aboveground biomass from 692.6 to 433.3 Mg C. However, expansion of the native Pisonia grandis and Heliotropium foertherianum forest community projected an increase in soil carbon to at least 13,590.8 Mg C, thereby increasing the atoll’s overall terrestrial carbon storage potential by 11.6%. Nearshore sites adjacent to C. nucifera canopy had a higher dissolved organic carbon (DOC) concentration (110.0 μMC) than sites adjacent to native forest (81.5 μMC), suggesting that, in conjunction with an increase in terrestrial carbon storage, replacing C. nucifera with native forest will reduce the DOC exported from the forest into in nearshore marine habitats. Lower DOC levels have potential benefits for corals and coral dependent communities. For tropical islands like Palmyra, reverting from C. nucifera dominance to native tree dominance could buffer projected climate change impacts by increasing carbon storage and reducing coral disease.
Shark dermal scale (denticle) accumulation in the fossil record can provide information about the abundance and composition of past shark communities. Denticles are shed continuously, such that a single shark leaves a scattered composite of many isolated denticles in sediments. However, the rate of denticle shedding as well as how these rates vary among shark species with different life modes and their consistency over time are unknown, limiting the interpretation of denticle assemblages. To better understand the process of denticle shedding and calibrate the relationship between absolute shark abundance in the environment and denticle deposition in sediments, we captured denticles shed by 2 shark species in a large aquarium over 9 mo. We then simulated how these aquarium-derived shedding rates shape the relationship between shark abundance and denticle accumulation. Bonnethead sharks Sphyrna tiburo, a more active, benthopelagic species with small, thin denticles, shed 3.6 times faster on average than zebra sharks Stegostoma fasciatum, a more sedentary, demersal species with large, robust denticles. This pattern persisted when shedding rates were corrected by estimated denticle quantities, shark space use, and methodological factors (2.2- to 3.8-fold difference). Over the study, bonnethead shark shedding rates declined while zebra shark shedding rates increased slightly. Finally, denticle assemblage composition corresponded with the relative abundance of denticles on the body of each species, consistent with natural shedding rather than selective loss. Overall, we show that shark taxa contribute unevenly to the denticle record, indicating that shedding rate measurements can help inform and constrain ecological interpretations of denticle assemblages.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13936","usgsCitation":"Dillon, E.M., Bagla, A., Plioplys, K.D., McCauley, D., Lafferty, K.D., and O’Dea, A., 2022, Dermal denticle shedding rates vary between two captive shark species: Marine Ecology Progress Series, v. 682, p. 153-167, https://doi.org/10.3354/meps13936.","productDescription":"15 p.","startPage":"153","endPage":"167","ipdsId":"IP-129960","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":413531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"682","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, Erin M.","contributorId":221878,"corporation":false,"usgs":false,"family":"Dillon","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":34029,"text":"U.C. Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagla, Anshika","contributorId":302734,"corporation":false,"usgs":false,"family":"Bagla","given":"Anshika","email":"","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plioplys, Kiera D.","contributorId":302735,"corporation":false,"usgs":false,"family":"Plioplys","given":"Kiera","email":"","middleInitial":"D.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Dea, Aaron","contributorId":302736,"corporation":false,"usgs":false,"family":"O’Dea","given":"Aaron","affiliations":[{"id":65541,"text":"Smithsonian Tropical Research Institute, University of Bologna","active":true,"usgs":false}],"preferred":false,"id":865282,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227645,"text":"70227645 - 2022 - A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","interactions":[],"lastModifiedDate":"2022-01-24T13:07:15.729427","indexId":"70227645","displayToPublicDate":"2022-01-20T07:02:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","docAbstract":"The Biological Condition Gradient (BCG) is a conceptual model used to describe incremental changes in biological condition along a gradient of increasing anthropogenic stress. As coral reefs collapse globally, scientists and managers are focused on how to sustain the crucial structure and functions, and the benefits that healthy coral reef ecosystems provide for many economies and societies. We developed a numeric (quantitative) BGC model for the coral reefs of Puerto Rico and the US Virgin Islands to transparently facilitate ecologically meaningful management decisions regarding these fragile resources. Here, reef conditions range from natural, undisturbed conditions to severely altered or degraded conditions. Numeric decision rules were developed by an expert panel for scleractinian corals and other benthic assemblages using multiple attributes to apply in shallow-water tropical fore reefs with depths <30 m. The numeric model employed decision rules based on metrics (e.g., % live coral cover, coral species richness, pollution-sensitive coral species, unproductive and sediment substrates, % cover by Orbicella spp.) used to assess coral reef condition. Model confirmation showed the numeric BCG model predicted the panel’s median site ratings for 84% of the sites used to calibrate the model and 89% of independent validation sites. The numeric BCG model is suitable for adaptive management applications and supports bioassessment and criteria development. It is a robust assessment tool that could be used to establish ecosystem condition that would aid resource managers in evaluating and communicating current or changing conditions, protect water and habitat quality in areas of high biological integrity, or develop restoration goals with stakeholders and other public beneficiaries.
The Yucaipa groundwater subbasin (referred to in this report as the Yucaipa subbasin) is located about 75 miles (mi) east of of Los Angeles and about 12 mi southeast of the City of San Bernardino. In the Yucaipa subbasin, as in much of southern California, limited annual rainfall and large water demands can strain existing water supplies; therefore, understanding local surface water and groundwater conditions is essential for managing these resources. To better understand the hydrogeology and water resources in the Yucaipa subbasin, especially groundwater, the San Bernardino Valley Municipal Water District and the U.S. Geological Survey initiated a cooperative study to evaluate the hydrogeologic system of the Yucaipa subbasin and the encompassing Yucaipa Valley watershed. Previous studies of the area provided information on general geologic and hydrologic conditions, but this study provides the first comprehensive definition of the hydrogeology of the subsurface throughout the entire subbasin.
The Yucaipa subbasin is located between the northwest trending San Andreas fault zone and San Jacinto fault. Several northeast-trending dip-slip faults dissect the Yucaipa subbasin, providing the mechanism for structural relief within the sediment-filled subbasin and between the subbasin and surrounding mountains and highlands. Several of these dip-slip faults have been previously identified as potential barriers to groundwater flow. This report provides a synthesis of previous studies and a discussion of the geologic interpretations that were used as the foundation for hydrogeologic classification of the Yucaipa subbasin. Notably, this report (1) adopts the recently named and classified sedimentary deposits of Live Oak Canyon geologic formation and extends the mapped distribution of the formation into the Yucaipa subbasin, and (2) adopts the interpretation that activity along the Banning fault predates the deposition of most basin-fill sedimentary materials in the Yucaipa subbasin.
Four hydrogeologic units were classified in the Yucaipa subbasin: (1) crystalline basement, (2) consolidated sedimentary materials, (3) unconsolidated sediment, and (4) surficial materials. The crystalline basement unit forms the bottom boundary of the aquifer system, and the three other units comprise the basin-fill aquifer system. The four hydrogeologic units vary in extent, thickness, and structural relief across the subbasin, with the unconsolidated sediment unit serving as the primary aquifer unit. A three-dimensional hydrogeologic framework model was developed for the Yucaipa subbasin and surrounding area to characterize the thickness, extent, and hydrogeologic variability of the aquifer system. Geologic maps, borehole geophysical logs, drillers’ lithology logs, and depth-to-basement gravity data were used to map and interpolate the subsurface extent and structure of the hydrogeologic units within the subbasin. Faults and structures of geologic and (or) hydrogeologic importance were included in the model for future evaluation of their potential effects on groundwater flow. The resulting hydrogeologic framework is consistent with existing geologic concepts and the tectonic and structural history of the Yucaipa subbasin and surrounding area. The framework is also suitable for use in basin-scale hydrogeologic investigations.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nrl.northumbria.ac.uk/id/eprint/48317/1/STOTEN_Epele_Manuscript.pdf","text":"External Repository"},{"id":396114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"820","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Epele, Luis B.","contributorId":279551,"corporation":false,"usgs":false,"family":"Epele","given":"Luis B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grech, Marta G.","contributorId":279552,"corporation":false,"usgs":false,"family":"Grech","given":"Marta","email":"","middleInitial":"G.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams-Subiza, Emilio A. 0000-0001-9480-527X","orcid":"https://orcid.org/0000-0001-9480-527X","contributorId":279553,"corporation":false,"usgs":false,"family":"Williams-Subiza","given":"Emilio","email":"","middleInitial":"A.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stenert, Cristina","contributorId":279554,"corporation":false,"usgs":false,"family":"Stenert","given":"Cristina","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greig, Hamish S.","contributorId":279555,"corporation":false,"usgs":false,"family":"Greig","given":"Hamish S.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maltchik, Leonardo 0000-0002-5321-7524","orcid":"https://orcid.org/0000-0002-5321-7524","contributorId":279556,"corporation":false,"usgs":false,"family":"Maltchik","given":"Leonardo","email":"","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835125,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pires, Mateus M. 0000-0002-5728-8733","orcid":"https://orcid.org/0000-0002-5728-8733","contributorId":279557,"corporation":false,"usgs":false,"family":"Pires","given":"Mateus","email":"","middleInitial":"M.","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835126,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bird, Matthew S.","contributorId":279558,"corporation":false,"usgs":false,"family":"Bird","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":57281,"text":"Department of Zoology, University of Johannesburg, Auckland Park 2006, South Africa","active":true,"usgs":false}],"preferred":false,"id":835127,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Boissezon, Aurelie","contributorId":279559,"corporation":false,"usgs":false,"family":"Boissezon","given":"Aurelie","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835128,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Boix, Dani 0000-0001-5468-2236","orcid":"https://orcid.org/0000-0001-5468-2236","contributorId":279560,"corporation":false,"usgs":false,"family":"Boix","given":"Dani","email":"","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835129,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Demierre, Eliane","contributorId":279561,"corporation":false,"usgs":false,"family":"Demierre","given":"Eliane","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835130,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"García, Patricia E.","contributorId":279562,"corporation":false,"usgs":false,"family":"García","given":"Patricia E.","affiliations":[{"id":57284,"text":"Grupo de Ecología de Sistemas Acuáticos a escala de Paisaje (GESAP) INIBIOMA, Universidad Nacional del Comahue, CONICET, Quintral 1250, San Carlos de Bariloche (8400), Argentina","active":true,"usgs":false}],"preferred":false,"id":835131,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gascón, Stephanie","contributorId":279563,"corporation":false,"usgs":false,"family":"Gascón","given":"Stephanie","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835132,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jeffries, Michael","contributorId":279564,"corporation":false,"usgs":false,"family":"Jeffries","given":"Michael","email":"","affiliations":[{"id":57285,"text":"Department of Geography & Environmental Sciences, Northumbria University, Newcastle upon Tune, NE1 8ST, UK","active":true,"usgs":false}],"preferred":false,"id":835133,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kneitel, Jamie M. 0000-0002-7841-1198","orcid":"https://orcid.org/0000-0002-7841-1198","contributorId":279565,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie","email":"","middleInitial":"M.","affiliations":[{"id":57286,"text":"Department of Biological Sciences, California State University-Sacramento, Sacramento, CA 95819-6077, USA","active":true,"usgs":false}],"preferred":false,"id":835134,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Loskutov, Olga","contributorId":279566,"corporation":false,"usgs":false,"family":"Loskutov","given":"Olga","email":"","affiliations":[{"id":57287,"text":"Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences, 28 Kommunisticheskaya Street, 167982 Syktyvkar, Russia","active":true,"usgs":false}],"preferred":false,"id":835135,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Manzo, Luz M.","contributorId":279567,"corporation":false,"usgs":false,"family":"Manzo","given":"Luz","email":"","middleInitial":"M.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835136,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mataloni, Gabriela 0000-0002-6852-6143","orcid":"https://orcid.org/0000-0002-6852-6143","contributorId":279568,"corporation":false,"usgs":false,"family":"Mataloni","given":"Gabriela","email":"","affiliations":[{"id":57288,"text":"Instituto de Investigación e Ingeniería Ambiental -IIIA, UNSAM, CONICET, Campus Miguelete, 1650-San Martín, Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":835137,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Mlambo, Musa 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Adrián Ruiz Leal s/n, Parque General San Martín, 5500, Mendoza, Argentina","active":true,"usgs":false}],"preferred":false,"id":835141,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wu, Haitao","contributorId":279573,"corporation":false,"usgs":false,"family":"Wu","given":"Haitao","email":"","affiliations":[{"id":57291,"text":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, 130012, China","active":true,"usgs":false}],"preferred":false,"id":835142,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Wissinger, Scott A","contributorId":279574,"corporation":false,"usgs":false,"family":"Wissinger","given":"Scott A","affiliations":[{"id":57292,"text":"Biology and Environmental Science Departments, Allegheny College, Meadville, PA 16335, USA","active":true,"usgs":false}],"preferred":false,"id":835143,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Batzer, Darold P.","contributorId":279575,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":57293,"text":"Department of Entomology, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":835144,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70236588,"text":"70236588 - 2022 - Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","interactions":[],"lastModifiedDate":"2022-09-12T14:35:34.563214","indexId":"70236588","displayToPublicDate":"2022-01-19T09:29:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","docAbstract":"Predicting how species respond to changes in climate is critical to conserving biodiversity. Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. We identify both biological uncertainty (unknown diapause survival rate) and climate uncertainty (ambiguity in the sign of precipitation change across climate models), and their interaction as key factors that determine persistence vs. extinction. Despite uncertainty, the most optimistic iteration of the model predicts that Miami blue butterfly populations will decline under the higher emissions scenario (RCP 8.5). The lack of climate model agreement across the projection ensemble suggests that investigations into the effect of climate change on precipitation-driven phenology require a higher level of rigor in the uncertainty analysis compared to analogous studies of temperature. For tropical species, a mechanistic approach that incorporates both biological and climate uncertainty is the best path forward to understand the effect shifting precipitation regimes have on phenology and population dynamics.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2022.100051","usgsCitation":"Henry, E.H., Terando, A., Morris, W., Daniels, J.C., and Haddad, N.M., 2022, Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms: Climate Change Ecology, v. 3, 100051, 10 p., https://doi.org/10.1016/j.ecochg.2022.100051.","productDescription":"100051, 10 p.","ipdsId":"IP-115502","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":449111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2022.100051","text":"Publisher Index Page"},{"id":406534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.18597412109375,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.505893706264033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henry, Erica H","contributorId":296418,"corporation":false,"usgs":false,"family":"Henry","given":"Erica","email":"","middleInitial":"H","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morris, William F.","contributorId":296419,"corporation":false,"usgs":false,"family":"Morris","given":"William F.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Jaret C.","contributorId":223585,"corporation":false,"usgs":false,"family":"Daniels","given":"Jaret","email":"","middleInitial":"C.","affiliations":[{"id":40743,"text":"Florida Museum of Natural History and University of Florida","active":true,"usgs":false}],"preferred":false,"id":851459,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haddad, Nick M.","contributorId":229345,"corporation":false,"usgs":false,"family":"Haddad","given":"Nick","email":"","middleInitial":"M.","affiliations":[{"id":41625,"text":"Kellogg Biological Station and Department of Integrative Biology, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":851460,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}