Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.
One of the key factors in obtaining precise and accurate 230Th ages of corals, especially for corals with ages less than a few thousand years, is the correction for non-radiogenic 230Th based on an initial 230Th/232Th value (230Th/232Th0). Studies that consider coral 230Th/232Th0 values in intertidal environments are limited, and it is in these environments that corals have Th concentrations 100–1000 times greater than open-ocean corals. Here we present 66 reconstructed U–Th isochrons of modern and Holocene Porites lutea and lobata corals from throughout the Sumatran forearc islands (SFI). Mean square of weighted deviates (MSWD) is used to evaluate the variabilities of 28 location-specific 230Th/232Th0 values before the regional mean. Results show no significant spatial difference between the means of 3.1 ± 3.2 × 10−6 for the northern SFI and 4.7 ± 1.3 × 10−6 for the southern SFI. Using the weighted mean of 4.3 ± 2.5 × 10−6 for all islands has the advantage that no data need be subjectively excluded in the calculation of reliable 230Th ages, where a local initial value is unknown. There were no clear centennial to millennial time-scale changes in 230Th/232Th0 in the past 2500 years. This study, therefore, suggests a reliable value of initial 230Th/232Th ratio in intertidal environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2023.108005","usgsCitation":"Chiang, H., Philibosian, B.E., Meltzner, A.J., Wu, C., Shen, C., Edwards, R.L., Chuang, C., Suwargadi, B.W., and Natawidjaja, D.H., 2023, Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals: Quaternary Science Reviews, v. 307, 108005, 11 p., https://doi.org/10.1016/j.quascirev.2023.108005.","productDescription":"108005, 11 p.","ipdsId":"IP-125955","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2023.108005","text":"Publisher Index Page"},{"id":415422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sumatran forearc islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94,\n 3.5\n ],\n [\n 94,\n -3.5\n ],\n [\n 103,\n -3.5\n ],\n [\n 103,\n 3.5\n ],\n [\n 94,\n 3.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"307","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chiang, Hong-Wei","contributorId":193421,"corporation":false,"usgs":false,"family":"Chiang","given":"Hong-Wei","email":"","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":868669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chung-Che","contributorId":193422,"corporation":false,"usgs":false,"family":"Wu","given":"Chung-Che","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chuan-Chou","contributorId":193424,"corporation":false,"usgs":false,"family":"Shen","given":"Chuan-Chou","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence 0000-0002-7027-5881","orcid":"https://orcid.org/0000-0002-7027-5881","contributorId":223143,"corporation":false,"usgs":false,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":868672,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chuang, Chih-Kai","contributorId":303947,"corporation":false,"usgs":false,"family":"Chuang","given":"Chih-Kai","email":"","affiliations":[{"id":30216,"text":"National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868673,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Suwargadi, Bambang W.","contributorId":150205,"corporation":false,"usgs":false,"family":"Suwargadi","given":"Bambang","email":"","middleInitial":"W.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Natawidjaja, Danny H.","contributorId":150204,"corporation":false,"usgs":false,"family":"Natawidjaja","given":"Danny","email":"","middleInitial":"H.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70242647,"text":"70242647 - 2023 - Population dynamics and harvest management of eastern mallards","interactions":[],"lastModifiedDate":"2023-06-09T15:15:15.702993","indexId":"70242647","displayToPublicDate":"2023-04-03T07:03:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics and harvest management of eastern mallards","docAbstract":"Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision-making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2-season banding and recovery data, and hunter-harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub-model for survival including estimating the degree of harvest additivity and any age-specific trends. The top survival sub-model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.
Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.
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\"}}]}","volume":"120","issue":"15","noUsgsAuthors":false,"publicationDate":"2023-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schliep, Erin M.","contributorId":349360,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"North, Joshua S.","contributorId":349361,"corporation":false,"usgs":false,"family":"North","given":"Joshua S.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":924259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kundel, Holly","contributorId":341087,"corporation":false,"usgs":false,"family":"Kundel","given":"Holly","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Custer, Christopher A.","contributorId":341088,"corporation":false,"usgs":false,"family":"Custer","given":"Christopher A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruzich, Jenna K.","contributorId":349365,"corporation":false,"usgs":false,"family":"Ruzich","given":"Jenna K.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hansen, Gretchen J.A.","contributorId":349370,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924263,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270840,"text":"70270840 - 2023 - Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","interactions":[],"lastModifiedDate":"2026-03-16T14:53:10.311939","indexId":"70270840","displayToPublicDate":"2023-04-01T09:35:57","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","docAbstract":"To predict future status of the northwestern pond turtle (Actinemys marmorata) and southwestern pond turtle (Actinemys pallida) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was
included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation.
The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Species status assessment report for Northwestern Pond Turel (Actinemys marmorata) and Southwestern Pond Turtle (Actinemys pallida)","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Gregory, K.M., and McGowan, C.P., 2023, Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida ) (version 1.1), 43 p.","productDescription":"43 p.","ipdsId":"IP-146555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494872,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/node/5110701"},{"id":501175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Kaili M.","contributorId":360548,"corporation":false,"usgs":false,"family":"Gregory","given":"Kaili","middleInitial":"M.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":947202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":947203,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266579,"text":"70266579 - 2023 - Automated soft pressure sensor array-based sea lamprey detection using machine learning","interactions":[],"lastModifiedDate":"2025-05-09T14:14:32.73205","indexId":"70266579","displayToPublicDate":"2023-04-01T09:10:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Automated soft pressure sensor array-based sea lamprey detection using machine learning","docAbstract":"Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.
","language":"English","publisher":"IEEE","doi":"10.1109/JSEN.2023.3249625","usgsCitation":"Shi, H., Mei, Y., González-Afanador, I., Chen, C., Miehls, S.M., Holbrook, C., Sepulveda, N., and Tan, X., 2023, Automated soft pressure sensor array-based sea lamprey detection using machine learning: IEEE Sensors Journal, v. 23, p. 7546-7557, https://doi.org/10.1109/JSEN.2023.3249625.","productDescription":"12 p.","startPage":"7546","endPage":"7557","ipdsId":"IP-147136","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang","contributorId":354871,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mei, Yu","contributorId":354872,"corporation":false,"usgs":false,"family":"Mei","given":"Yu","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"González-Afanador, Ian","contributorId":354873,"corporation":false,"usgs":false,"family":"González-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Claudia","contributorId":354874,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936606,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo","contributorId":354875,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936607,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70242019,"text":"70242019 - 2023 - The NEON Ecological Forecasting Challenge","interactions":[],"lastModifiedDate":"2023-04-04T13:29:05.572624","indexId":"70242019","displayToPublicDate":"2023-04-01T08:26:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"The NEON Ecological Forecasting Challenge","docAbstract":"The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2616","usgsCitation":"Thomas, R.Q., Boettiger, C., Carey, C.C., Dietze, M., Johnson, L.R., Kenney, M.A., McLachlan, J.S., Peters, J.A., Sokol, E.R., Weltzin, J., Willson, A., and Woelmer, W., 2023, The NEON Ecological Forecasting Challenge: Frontiers in Ecology and the Environment, v. 21, no. 3, p. 112-113, https://doi.org/10.1002/fee.2616.","productDescription":"2 p.","startPage":"112","endPage":"113","ipdsId":"IP-145337","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":443983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2616","text":"Publisher Index Page"},{"id":415161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boettiger, Carl","contributorId":201833,"corporation":false,"usgs":false,"family":"Boettiger","given":"Carl","affiliations":[{"id":36267,"text":"Dept of Environmental Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":868550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":868551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":868552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Leah R.","contributorId":139035,"corporation":false,"usgs":false,"family":"Johnson","given":"Leah","email":"","middleInitial":"R.","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":868553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kenney, Melissa A.","contributorId":202414,"corporation":false,"usgs":false,"family":"Kenney","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":868554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLachlan, Jason S.","contributorId":245535,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peters, Jody A.","contributorId":303908,"corporation":false,"usgs":false,"family":"Peters","given":"Jody","email":"","middleInitial":"A.","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sokol, Eric R.","contributorId":303909,"corporation":false,"usgs":false,"family":"Sokol","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":55597,"text":"National Ecological Observatory Network","active":true,"usgs":false}],"preferred":false,"id":868557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":868558,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Willson, Alyssa","contributorId":303910,"corporation":false,"usgs":false,"family":"Willson","given":"Alyssa","email":"","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868559,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Woelmer, Whitney M.","contributorId":303911,"corporation":false,"usgs":false,"family":"Woelmer","given":"Whitney M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868560,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70242039,"text":"70242039 - 2023 - National preparedness strategy & action plan for potentially hazardous near-Earth objects and planetary defense","interactions":[],"lastModifiedDate":"2023-04-05T12:08:16.766945","indexId":"70242039","displayToPublicDate":"2023-04-01T07:03:10","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"National preparedness strategy & action plan for potentially hazardous near-Earth objects and planetary defense","docAbstract":"Near-Earth Objects (NEOs) are asteroids and comets that orbit the Sun, but have orbits that can bring them into Earth’s neighborhood—within 30 million miles of Earth’s orbit. Planetary defense is “applied planetary science” to address the NEO impact risks on Earth.
This National Preparedness Strategy and Action Plan for Near-Earth Objects and Planetary Defense (2023 Planetary Defense Strategy) updates the United States’ first comprehensive Near-Earth Object Preparedness Strategy and Action Plan, released in 2018. The 2023 Planetary Defense Strategy builds on existing efforts by Federal Departments and Agencies to address the hazard of near-Earth object impacts, includes evaluation of where progress has been made since 2018, and focuses future work on planetary defense across the U.S. government. The 2023 Planetary Defense Strategy maintains and updates the core five goals of the 2018 Strategy and Action Plan, while adding a sixth goal to improve governance and interagency coordination on planetary defense issues across Federal Departments and Agencies for the decade ahead.
The 2023 Planetary Defense Strategy focuses on six goals:
• Goal 1: Enhance NEO detection, tracking, and characterization capabilities.
• Goal 2: Improve NEO modeling, prediction, and information integration.
• Goal 3: Develop technologies for NEO deflection and disruption missions.
• Goal 4: Increase international cooperation on NEO preparation.
• Goal 5: Strengthen and routinely exercise NEO impact emergency procedures and action protocols.
• Goal 6: Improve U.S. governance of planetary defense through new interagency collaboration.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","usgsCitation":"Daniels, M., Johnson, L., Kommel, R., Besha, P., Brody, P., Conole, K., Fast, K., Fernandez, A., Gaume, R., Greenaugh, K., Guglietta, R., Howard, D., Hu, G., Joseph, C., Keuker-Murphy, B.G., Lewis, L., Millard, L., Mozer, J., Poster, D., Titus, T.N., and Vanderley, A., 2023, National preparedness strategy & action plan for potentially hazardous near-Earth objects and planetary defense, iv, 34 p.","productDescription":"iv, 34 p.","ipdsId":"IP-150721","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":415223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415217,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.whitehouse.gov/wp-content/uploads/2023/04/2023-NSTC-National-Preparedness-Strategy-and-Action-Plan-for-Near-Earth-Object-Hazards-and-Planetary-Defense.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniels, Matthew","contributorId":303927,"corporation":false,"usgs":false,"family":"Daniels","given":"Matthew","email":"","affiliations":[{"id":65936,"text":"Assistant Director, White House OSTP","active":true,"usgs":false}],"preferred":false,"id":868644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Lindley","contributorId":303928,"corporation":false,"usgs":false,"family":"Johnson","given":"Lindley","email":"","affiliations":[{"id":65937,"text":"Planetary Defense Officer, NASA","active":true,"usgs":false}],"preferred":false,"id":868645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kommel, Renata","contributorId":303929,"corporation":false,"usgs":false,"family":"Kommel","given":"Renata","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Besha, Patrick","contributorId":303930,"corporation":false,"usgs":false,"family":"Besha","given":"Patrick","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brody, Perry","contributorId":303931,"corporation":false,"usgs":false,"family":"Brody","given":"Perry","email":"","affiliations":[{"id":65939,"text":"DOC/NOAA","active":true,"usgs":false}],"preferred":false,"id":868648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conole, Kevin","contributorId":303932,"corporation":false,"usgs":false,"family":"Conole","given":"Kevin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868649,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fast, Kelly","contributorId":303933,"corporation":false,"usgs":false,"family":"Fast","given":"Kelly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868650,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fernandez, Angelo","contributorId":303934,"corporation":false,"usgs":false,"family":"Fernandez","given":"Angelo","email":"","affiliations":[{"id":65941,"text":"JCS","active":true,"usgs":false}],"preferred":false,"id":868651,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaume, Ralph","contributorId":303935,"corporation":false,"usgs":false,"family":"Gaume","given":"Ralph","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868652,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Greenaugh, Kevin","contributorId":303936,"corporation":false,"usgs":false,"family":"Greenaugh","given":"Kevin","email":"","affiliations":[{"id":34199,"text":"DOE","active":true,"usgs":false}],"preferred":false,"id":868653,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Guglietta, Ryan","contributorId":303937,"corporation":false,"usgs":false,"family":"Guglietta","given":"Ryan","email":"","affiliations":[{"id":65943,"text":"State","active":true,"usgs":false}],"preferred":false,"id":868654,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Howard, Diane","contributorId":303938,"corporation":false,"usgs":false,"family":"Howard","given":"Diane","email":"","affiliations":[{"id":65944,"text":"NSpC","active":true,"usgs":false}],"preferred":false,"id":868655,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hu, Grace","contributorId":303939,"corporation":false,"usgs":false,"family":"Hu","given":"Grace","email":"","affiliations":[{"id":65945,"text":"OMB","active":true,"usgs":false}],"preferred":false,"id":868656,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Joseph, Christine","contributorId":303940,"corporation":false,"usgs":false,"family":"Joseph","given":"Christine","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":868657,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Keuker-Murphy, Brig Gen Traci","contributorId":303941,"corporation":false,"usgs":false,"family":"Keuker-Murphy","given":"Brig","email":"","middleInitial":"Gen Traci","affiliations":[{"id":65946,"text":"USSPACECOM","active":true,"usgs":false}],"preferred":false,"id":868658,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lewis, L.A.","contributorId":303942,"corporation":false,"usgs":false,"family":"Lewis","given":"L.A.","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":868659,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Millard, Lindsay","contributorId":303943,"corporation":false,"usgs":false,"family":"Millard","given":"Lindsay","email":"","affiliations":[{"id":65947,"text":"OSD(R&E)","active":true,"usgs":false}],"preferred":false,"id":868660,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mozer, Joel","contributorId":303944,"corporation":false,"usgs":false,"family":"Mozer","given":"Joel","email":"","affiliations":[{"id":65948,"text":"DOD/USSF","active":true,"usgs":false}],"preferred":false,"id":868661,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Poster, Dianne","contributorId":303945,"corporation":false,"usgs":false,"family":"Poster","given":"Dianne","email":"","affiliations":[{"id":65949,"text":"DOC","active":true,"usgs":false}],"preferred":false,"id":868662,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":868663,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Vanderley, Ashley","contributorId":303946,"corporation":false,"usgs":false,"family":"Vanderley","given":"Ashley","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868664,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70242968,"text":"70242968 - 2023 - Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations","interactions":[],"lastModifiedDate":"2023-04-25T11:53:37.989298","indexId":"70242968","displayToPublicDate":"2023-04-01T06:47:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations","docAbstract":"The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d−1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.
Nuisance levels of benthic filamentous green algae are becoming increasingly common in surface waters of Colorado and the western United States. In 2018 the U.S. Geological Survey began a study in cooperation with the White River and Douglas Creek Conservation Districts, Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District to collect and analyze physical, chemical, and biological information for the upper White River Basin in Colorado and investigate causes of benthic algal blooms in the basin. This report (1) presents site-specific data including water temperature, riparian canopy cover, streambed particle size, and algal biomass and community composition; (2) describes the potential for streambed movement during spring runoff using physical channel characteristics and peak streamflow velocities; and (3) explains the results of a linear mixed-effects model used to test hypotheses about the influence of physical and chemical factors in explaining the occurrence of algal blooms across the basin.
Benthic algal biomass ranged from 0.7 to 309 milligrams per square meter during the summer (July–August) from 2018 through 2021 and exceeded the Colorado Department of Public Health and Environment criteria of 150 milligrams per square meter on four occasions, in 2018. Four genera of filamentous green algae were identified in the upper White River Basin, including Cladophora, Stigeoclonium, Ulothrix, and Spirogyra. Many genera of cyanobacteria were present, including some capable of producing toxins and taste and odor compounds. The nuisance diatom Didymosphenia geminata, commonly referred to as didymo, was found at two sites on the South Fork White River and along the main stem White River.
Hypotheses pertaining to the influence of measured variables on algal biomass were tested with a linear mixed-effects model. Median rock size and mean August water temperature had significant positive effects, meaning that greater bed stability and higher mean August water temperatures result in greater algal biomass. Total nitrogen to total phosphorus ratios had a significant negative effect on algal biomass, meaning that more nitrogen-limiting conditions, or greater phosphorus availability, corresponded to greater algal biomass.
Streamflow and water temperature data at White River above Coal Creek near Meeker, Colo., were used to assess possible causes of bloom conditions across years, including when algal blooms were first studied in the basin during 2016 and 2017. Early or low-magnitude peak streamflow conditions were not prerequisites for algal bloom occurrence. Conversely, relatively large, late, and long-lasting peak streamflows, such as those measured in 2019, may limit algal blooms during the same year and into subsequent years, as evidenced by extremely low algal biomass in 2019 and 2020. The broad spatial extent of bloom conditions indicates that the factors contributing to the occurrence of algal blooms are likely basinwide. Findings from this multiyear study indicate that the effects caused by larger peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of an algal bloom. The findings also indicate that in the absence of disturbance other resources, including substrate size, water temperature, and nutrient availability, moderate algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Nuisance levels of benthic algae are becoming increasingly common in surface waters of the western United States and can compromise aesthetic quality, limit recreational activities, block water infrastructure, and negatively affect aquatic life. In cooperation with the White River and Douglas Creek Conservation Districts, the Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District, the U.S. Geological Survey studied physical, chemical, and biological factors potentially controlling the occurrence of benthic algae in the upper White River Basin, Colorado, from 2018 through 2021. Multiple approaches were used to assess nutrients and physical conditions in the upper White River Basin. A linear mixed-effects model was used to evaluate the relative effect of different factors on algal biomass across water-quality sites.
The frequency and severity of algal blooms in the upper White River Basin may be affected by long-term changes in nutrient availability and streamflow, specifically changes in the timing and magnitude of high and low streamflow. The effects of large peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of algal blooms through years. Large, late, and long-lasting peak streamflow may limit algal blooms during the same year and into subsequent years. Without streambed disturbance, other factors such as nutrients and water temperature may have a larger effect on algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Bathymetric change analyses document historical patterns of sediment deposition and erosion, providing valuable insight into the sediment dynamics of coastal systems, including pathways of sediment and sediment-bound contaminants. In 2014 and 2015, the Office for Coastal Management, in partnership with the National Oceanic and Atmospheric Administration (NOAA) Office of Coastal Management, provided funding for new bathymetric surveys of large portions of San Francisco Bay. A total of 93 bathymetric surveys were conducted during this 2-year period, using a combination of interferometric sidescan and multibeam sonar systems. These data, along with recent NOAA, U.S. Geological Survey (USGS), U.S. Army Corps of Engineers, and private contractor surveys collected from 1999 to 2020 (hereinafter referred to as 2010s), were used to create the most comprehensive bathymetric digital elevation models (DEMs) of San Francisco Bay since the 1980s. Comparing DEMs created from these 2010s surveys with USGS DEMs created from NOAA’s 1971–1990 (hereinafter referred to as 1980s) surveys provides information on the quantities and patterns of erosion and deposition in San Francisco Bay during the 9 to 47 years between surveys. This analysis reveals that in the areas surveyed in both the 1980s and 2010s, the bay floor lost about 34 million cubic meters of sediment since the 1980s. Results from this study can be used to assess how San Francisco Bay has responded to changes in the system, such as sea-level rise and variation in sediment supply from the Sacramento-San Joaquin Delta and local tributaries, and supports the creation of a new, system-wide sediment budget. This report provides data on the quantities and patterns of sediment volume change in San Francisco Bay for ecosystem managers that are pertinent to various sediment-related issues, including restoration of tidal marshes, exposure of legacy contaminated sediment, and strategies for the beneficial use of dredged sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231031","collaboration":"Prepared in cooperation with the Regional Monitoring Program for Water Quality in San Francisco Bay","usgsCitation":"Fregoso, T.A., Foxgrover, A.C., and Jaffe, B.E., 2023, Sediment deposition, erosion, and bathymetric change in San Francisco Bay, California, 1971–1990 and 1999–2020 (ver. 1.1, June 2024): U.S. Geological Survey Open-File Report 2023–1031, 19 p., https://doi.org/ 10.3133/ ofr20231031.","productDescription":"Report: vi, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-135389","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":435389,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1332UUW","text":"USGS data release","linkHelpText":"Bathymetric change analysis in San Francisco Bay, California, from 1971 to 2020"},{"id":430608,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1031/versionHist.txt","size":"10.7 KB","linkFileType":{"id":2,"text":"txt"}},{"id":415025,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1031/images"},{"id":415024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1031/ofr20231031.pdf","text":"Report","size":"15.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1031/coverthb2.jpg"},{"id":499186,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114619.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.84417858005195,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 38.240616044555935\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 31, 2023; Version 1.1: June 28, 2024","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Development of Seismic Source Characterization (SSC) models, which is an essential part of Probabilistic Seismic Hazard Analyses (PSHA), can help forecast the temporal and spatial distribution of future damaging earthquakes (\uD835\uDC40w≥ 5) in seismically active regions. Because it is impossible to associate all earthquakes with known faults, seismic source models for PSHA often include sources of diffuse seismicity in which future earthquake scenarios are not localized on mapped faults. These sources of diffuse seismicity are referred to as area source zones, distributed seismicity zones, or just source zones. During the early years of PSHA studies, it was assumed that earthquakes in seismotectonic zones have (1) uniform spatial distribution, (2) Poisson temporal distribution, and (3) exponential magnitude distribution (NRC, 2012). In seismically active regions (e.g., the Western United States), where active faults are readily identified, models of the spatial distribution of earthquakes include both the fault source geometries and the distributed seismicity (background) source zones. Source characterization of active faults is complemented by paleoseismic studies with estimates of earthquake magnitudes, dates of occurrences, and slip rates, which provide important information for PSHA studies.
In the Central and Eastern United States (CEUS) very few Quaternary-active faults have the requisite information for use in PSHA (i.e., fault geometry and dimensions, event rates or slip rates, etc.), and we lack knowledge about the causative faults for most observed seismicity in the region. As a result, area source zones are frequently used in site-specific PSHA in the CEUS to represent diffuse seismicity that cannot be associated with faults. However, there are examples of active fault sources in the CEUS, such as the Meers fault, the Cheraw fault, and New Madrid region, where individual faults can be characterized.
The source characterization models for background seismicity are based, to a large extent, on an assumption that spatial distribution of historical and recorded seismicity will not change substantially for time periods of interest for PSHA (approximately the next 50-100 years for engineered structures). Furthermore, studies such as those by Kafka (2007, 2009) found a correlation between the locations of small- to moderate-magnitude earthquakes and the locations of large-magnitude earthquakes, indicating that we can, with some level of confidence, use the spatial pattern of smaller earthquakes to forecast the future pattern of damaging earthquakes.
Within background seismicity zones, the earthquake rate forecast is developed using spatial smoothing of the small to moderate magnitude events in earthquake catalogs. Different methodologies are used for this purpose and can predict varying distributions of seismicity rates. This in turn affects the results of a seismic hazard analysis. The U.S. Geological Survey (USGS) and Nuclear Regulatory Commission (NRC) use different methods for computing spatially smoothed seismicity rates in the CEUS; the USGS uses kernel-based spatial smoothing methods in developing the National Seismic Hazard Model (NSHM), and the method adopted in the Central and Eastern United States Seismic Source Characterization (CEUS-SSC) project is used when evaluating seismic hazard for nuclear power plant siting. These methods are described and the impact on seismic hazard are evaluated in this Research Information Letter (RIL).
Another important input to estimating the rate of distributed seismicity is event magnitudes listed in earthquake catalogs. A substantial source of uncertainty in catalogs is the magnitude assigned to a given earthquake. Numerous different magnitude types exist, with each magnitude type computed in a different way. Therefore, for the sake of consistency, both the CEUS-SSC and the USGS NSHM have attempted to assemble a complete catalog with a uniform magnitude determination. To this end, moment magnitude, \uD835\uDC40w, which is a physics-based measurement, has been adopted as the standard. However, \uD835\uDC40w was not computed routinely until the past few decades. To address this issue, the CEUS-SSC conducted extensive analyses to determine conversion equations from which to take a routinely computed network (e.g., \uD835\uDC40L or \uD835\uDC5AbLg ) and convert it into \uD835\uDC40w. Another issue with using \uD835\uDC40w is that it becomes increasingly difficult to compute for earthquakes with \uD835\uDC40 less than ~4.
This study investigates the effects of moment magnitude estimation and spatial smoothing methods on estimation of the earthquake rate forecast and on seismic hazard. We investigate the validity of the magnitude conversion equations and their associated uncertainties by applying them to a case study for induced earthquakes in southern Kansas and northern Oklahoma, and summarize the use of the decay of the seismic coda to estimate \uD835\uDC40w for small earthquakes (\uD835\uDC40w < 4. Furthermore, the study documents a comparison and assessment of background seismicity smoothing methods implemented by the USGS for the NSHM and used by the CEUS-SSC for siting nuclear facilities based on probabilistic seismic hazard estimates from multiple source zones in the CEUS and for multiple sites.
","language":"English","publisher":"Nuclear Regulatory Commission","usgsCitation":"Anooshehpoor, R., Weaver, T., Ake, J., Munson, C., Moschetti, M.P., Shelly, D.R., and Powers, P.M., 2023, Magnitude conversion and earthquake recurrence rate models for the central and eastern United States: Research Information Letter 2023-03, 81 p.","productDescription":"81 p.","ipdsId":"IP-148166","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":415346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415324,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://adamswebsearch2.nrc.gov/webSearch2/main.jsp?AccessionNumber=ML23073A370","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","otherGeospatial":"central and eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115,\n 50\n ],\n [\n -115,\n 25\n ],\n [\n -65,\n 25\n ],\n [\n -65,\n 50\n ],\n [\n -115,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anooshehpoor, Rasool","contributorId":303980,"corporation":false,"usgs":false,"family":"Anooshehpoor","given":"Rasool","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weaver, Thomas","contributorId":303981,"corporation":false,"usgs":false,"family":"Weaver","given":"Thomas","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ake, Jon","contributorId":303982,"corporation":false,"usgs":false,"family":"Ake","given":"Jon","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Cliff","contributorId":303983,"corporation":false,"usgs":false,"family":"Munson","given":"Cliff","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868794,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868795,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868796,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255002,"text":"70255002 - 2023 - Using decision analysis to determine the feasibility of a conservation translocation","interactions":[],"lastModifiedDate":"2024-06-11T15:23:15.270351","indexId":"70255002","displayToPublicDate":"2023-03-31T10:19:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14243,"text":"Decision Analysis","active":true,"publicationSubtype":{"id":10}},"title":"Using decision analysis to determine the feasibility of a conservation translocation","docAbstract":"Conservation translocations, intentional movements of species to protect against extinction, have become widespread in recent decades and are projected to increase further as biodiversity loss continues worldwide. The literature abounds with analyses to inform translocations and assess whether they are successful, but the fundamental question of whether they should be initiated at all is rarely addressed formally. We used decision analysis to assess northern leopard frog reintroduction in northern Idaho, with success defined as a population that persists for at least 50 years. The Idaho Department of Fish and Game was the decision maker (i.e., the agency that will use this assessment to inform their decisions). Stakeholders from government, indigenous groups, academia, land management agencies, and conservation organizations also participated. We built an age-structured population model to predict how management alternatives would affect probability of success. In the model, we explicitly represented epistemic uncertainty around a success criterion (probability of persistence) characterized by aleatory uncertainty. For the leading alternative, the mean probability of persistence was 40%. The distribution of the modelling results was bimodal, with most parameter combinations resulting in either very low (<5%) or relatively high (>95%) probabilities of success. Along with other considerations, including cost, the Idaho Department of Fish and Game will use this assessment to inform a decision regarding reintroduction of northern leopard frogs. Conservation translocations may benefit greatly from more widespread use of decision analysis to counter the complexity and uncertainty inherent in these decisions.
","language":"English","publisher":"Informs","doi":"10.1287/deca.2023.0472","usgsCitation":"Keating, L., Randall, L., Stanton, R., McCormack, C., Lucid, M., Seaborn, T., Converse, S.J., Canessa, S., and Moehrenschlager, A., 2023, Using decision analysis to determine the feasibility of a conservation translocation: Decision Analysis, v. 20, no. 4, p. 295-310, https://doi.org/10.1287/deca.2023.0472.","productDescription":"16 p.","startPage":"295","endPage":"310","ipdsId":"IP-142737","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443992,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2434/1040192","text":"External Repository"},{"id":429880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keating, Laura","contributorId":338249,"corporation":false,"usgs":false,"family":"Keating","given":"Laura","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903054,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Randall, Lea","contributorId":338250,"corporation":false,"usgs":false,"family":"Randall","given":"Lea","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Rebecca","contributorId":338251,"corporation":false,"usgs":false,"family":"Stanton","given":"Rebecca","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903056,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormack, Casey","contributorId":338252,"corporation":false,"usgs":false,"family":"McCormack","given":"Casey","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903057,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lucid, Michael","contributorId":338253,"corporation":false,"usgs":false,"family":"Lucid","given":"Michael","email":"","affiliations":[{"id":81108,"text":"Selkirk Wildlife Science, LLC","active":true,"usgs":false}],"preferred":false,"id":903058,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Seaborn, Travis","contributorId":338254,"corporation":false,"usgs":false,"family":"Seaborn","given":"Travis","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903059,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903060,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Canessa, Stefano","contributorId":149295,"corporation":false,"usgs":false,"family":"Canessa","given":"Stefano","email":"","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":903133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moehrenschlager, Axel","contributorId":338100,"corporation":false,"usgs":false,"family":"Moehrenschlager","given":"Axel","affiliations":[{"id":56586,"text":"czs","active":true,"usgs":false}],"preferred":false,"id":903134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263433,"text":"70263433 - 2023 - The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","interactions":[],"lastModifiedDate":"2025-02-11T15:25:05.142851","indexId":"70263433","displayToPublicDate":"2023-03-31T09:20:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2467,"text":"Journal of Structural Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","docAbstract":"Seismic behavior and performance of the new Self- Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System is studied using response data recorded during the October 14, 2019, \uD835\uDC40\uD835\uDC644.6 Pleasant Hill earthquake. The new bridge went into service within the last decade as a replacement for the older truss bridge that spanned between Yerba Buena Island and East Bay. During the October 19, 1989, M6.9 Loma Prieta earthquake, which occurred ∼100 km away from the Bay Bridge, a section of the upper deck of the old truss bridge fell onto the lower deck—thus closing this important lifeline between San Francisco and East Bay. The new SAS Bridge (as well as the rest of the Bay Bridge) is instrumented by the California Strong Motion Instrumentation Program (CSMIP). The unique SAS Bridge is suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the eastbound (E) and westbound (W) decks. At both the west and east ends of the SAS, there is a hinge system that connects the W and E decks to the skyways leading to highways. For the west side, the SAS is led to a tunnel at Yerba Buena Island. The response data analyses highlight the complex and yet identifiable coupled response of the deck, tower, and cable system. Using system identification methods including spectral analyses of both acceleration and displacement time history data, the fundamental frequencies (periods) and critical damping percentages are extracted for the main components (tower, deck, and cables) of the bridge where the sensors are deployed. Frequencies (periods) are then compared with the values computed during the design and analysis process of the bridge. The analyses in this paper showed that there is strong evidence of a beating effect attributed to low critical damping percentages and coupled modes. A possible correlation of fundamental periods of such suspension bridges with their span lengths is discussed. The beating effect and period versus span length can be significant topics for further research.
","language":"English","publisher":"American Society of Civil Engineering","doi":"10.1061/JSENDH.STENG-11725","usgsCitation":"Celebi, M., 2023, The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake: Journal of Structural Engineering, v. 149, no. 6, 05023003, 12 p., https://doi.org/10.1061/JSENDH.STENG-11725.","productDescription":"05023003, 12 p.","ipdsId":"IP-138272","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jsendh.steng-11725","text":"Publisher Index Page"},{"id":481928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Bridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.30992911723865,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.83469490117358\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"149","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":926975,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263877,"text":"70263877 - 2023 - Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","interactions":[],"lastModifiedDate":"2025-02-27T14:48:12.355469","indexId":"70263877","displayToPublicDate":"2023-03-31T08:41:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","docAbstract":"Understanding the timing and drivers of migration can be beneficial for improving response efforts aimed at reducing invasive species densities. Efforts by management agencies to remove grass carp (Ctenopharyngodon idella), an invasive species to the Laurentian Great Lakes, have been ongoing in Lake Erie tributaries since 2018. To bolster efforts, deployment of a non-physical barrier has been proposed downstream of a known grass carp spawning location near Brady’s Island (BI) in the Sandusky River, OH, USA to limit recruitment. However, knowledge of grass carp migratory timing, the environmental variables that cue carp migration, and the potential effects the barrier might impose on native fish [e.g., walleye (Sander vitreus)] movements would help inform barrier deployment and scheduling. We used detection data from grass carp (n = 29) and walleye (n = 84) tagged with acoustic transmitters to address four objectives: (1) quantify interannual variation (years = 2015–2021) of grass carp migration timing to BI; (2) evaluate timing of different grass carp movement modalities (residents and migrants); (3) assess overlap in migration timing with native walleye, and (4) evaluate environmental cues of grass carp migration to BI. Median grass carp arrival at BI occurred within a three-week period (148–165 Julian days), suggesting that deploying a barrier immediately prior to this time frame may be effective for deterring grass carp spawning. Temperature, photoperiod, and discharge influenced grass carp migration timing given that most arrival events occurred at daylengths > 14.5 h, temperatures exceeding 18 °C, and low discharge events (< 3,000 cubic feet second−1 [CFS]). Minimal interannual variability in migration timing existed for grass carp and walleye over a six-year period. However, the median departure time of walleye was more than 45 days before the median arrival time of grass carp, suggesting a spawning barrier may minimally affect walleye spawning. No differences in arrival timing at BI were observed between grass carp migratory contingents, indicating that if a barrier were deployed in the spring, it would likely affect all grass carp spatial contingents. This work highlights management implications of barrier control efforts of aquatic invasive species and provides insight into the environmental cues that grass carp use for upstream migration.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-023-03049-9","usgsCitation":"Bopp, J., Brenden, T.O., Faust, M., Vandergoot, C., Kraus, R., Roberts, J., and Nathan, L., 2023, Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy: Biological Invasions, v. 25, p. 2439-2459, https://doi.org/10.1007/s10530-023-03049-9.","productDescription":"21 p.","startPage":"2439","endPage":"2459","ipdsId":"IP-140268","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":482553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.5,\n 41.6\n ],\n [\n -83.5,\n 41.6\n ],\n [\n -83.5,\n 41.3\n ],\n [\n -82.5,\n 41.3\n ],\n [\n -82.5,\n 41.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin","contributorId":340933,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":348473,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher","contributorId":351529,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","affiliations":[{"id":84005,"text":"Michigan State University/GLATOS","active":true,"usgs":false}],"preferred":false,"id":928801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":928802,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nathan, Lucas","contributorId":351530,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":928804,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242013,"text":"70242013 - 2023 - The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration","interactions":[],"lastModifiedDate":"2023-06-08T14:48:49.884131","indexId":"70242013","displayToPublicDate":"2023-03-31T07:08:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration","docAbstract":"Understanding of the Everglades’ ecological vulnerabilities and restoration needs has advanced over the past decade but has not been applied in an integrated manner. To address this need, we developed the Everglades Vulnerability Analysis (EVA), a decision support tool that uses modular Bayesian networks to predict the ecological outcomes of a subset of the ecosystem’s health indicators. This tool takes advantage of the extensive modeling work already done in the Everglades and synthesizes information across indicators of ecosystem health to forecast long-term, landscape-scale changes. In addition, the tool can predict indicator vulnerability through comparison to user-defined ideal system states that can vary in the level of certainty of outcomes. An integrated understanding of the Everglades system is essential for evaluation of trade-offs at local, regional, and system-wide scales. Through EVA, Everglades restoration decision makers can provide effective guidance during restoration planning and implementation processes to mitigate unintended consequences that could result in further damage to the Everglades system.
Highly pathogenic (HP) avian influenza viruses (AIVs) of the clade 2.3.4.4 goose/Guangdong/1996 H5 lineage continue to be a problem in poultry and wild birds in much of the world. The recent incursion of a H5N1 clade 2.3.4.4b HP AIV from this lineage into North America has resulted in widespread outbreaks in poultry and consistent detections of the virus across diverse families of birds and occasionally mammals. To characterize the pathobiology of this virus in mallards (Anas platyrhynchos), which are a primary reservoir of AIV, a challenge study was conducted with 2 week-old birds. The 50% bird infectious dose was determined to be <2 log10 50% egg infectious doses (EID50) and all exposed ducks, including ducks co-housed with inoculated ducks, were infected. Infection appeared to be subclinical for 58.8% (20/34) of the ducks, 1 duck was lethargic, about 20% developed neurological signs and were euthanized, and 18% developed corneal opacity. The mallards shed virus by both the oral and cloacal routes within 24-48hr post-infection. Oral shedding substantially decreased by 6-7 days post-infection, but 65% of the ducks continued to shed virus cloacally through 14 days post-exposure (DPE) for the direct inoculate and 13DPE for contact exposed ducks. Based on the high transmissibility, high virus shed titers, and mild-to-moderate disease, mallards could serve as efficient reservoirs to amplify and disseminate recent North American clade 2.3.4.4b viruses.
Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.
We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.
Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.15163","usgsCitation":"Aunins, A.W., Mueller, S.J., Fike, J., and Cornman, R.S., 2023, Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling: PeerJ, v. 11, e15163, 34 p., https://doi.org/10.7717/peerj.15163.","productDescription":"e15163, 34 p.","ipdsId":"IP-146615","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":444004,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.15163","text":"Publisher Index Page"},{"id":435391,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNZNVH","text":"USGS data release","linkHelpText":"Metabarcode sequencing of aquatic environmental DNA from the Potomac River Watershed, 2015-2020"},{"id":415048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Sara J.","contributorId":303889,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":868370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242847,"text":"70242847 - 2023 - Ediacaran-Ordovician magmatism and REE mineralization in the Wet Mountains, Colorado, USA: Implications for failed continental rifting","interactions":[],"lastModifiedDate":"2023-04-20T12:03:48.647713","indexId":"70242847","displayToPublicDate":"2023-03-30T06:56:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Ediacaran-Ordovician magmatism and REE mineralization in the Wet Mountains, Colorado, USA: Implications for failed continental rifting","docAbstract":"Structures associated with Ediacaran-Ordovician alkaline magmatism and the timing of rare earth element (REE) mineralization in the Wet Mountains, CO, were analyzed using field, geophysical, and U-Th-Pb isotope methods to interpret their tectonic setting in the context of previously proposed rift models. The Wet Mountains are known for thorium and REE mineralization associated with failed rift-related, Ediacaran-Ordovician alkaline intrusions and veins. Structural field data indicate that alkaline dikes and mineralized veins are controlled by a system of northwest-striking, high-angle faults and tension fractures formed in a 040°-directed extensional regime. Magnetic and surface expressions of Democrat Creek and McClure Mountain complexes show tectonic elongation toward ∼045°, consistent with NE-directed extension. Magnetic data also suggest the existence of a fourth, previously unrecognized mafic-ultramafic complex of inferred Cambrian age with a similar elongated orientation. Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) 208Pb/232Th analysis of low-uranium zircon from carbonatite dikes and in situ 206Pb/238U LA-ICP-MS analysis of monazite in mineralized dikes yielded 465 ± 18 Ma and 489 ± 33 Ma ages, respectively. These ages are consistent with the expected age based on slightly older, cross-cut syenite dikes and the hypothesized Ordovician end to failed rift-related magmatism. The Ediacaran-Ordovician age of alkaline magmatic rocks and the associated northeast-directed extension direction are similar to those of the along-strike, Ediacaran-Cambrian Southern Oklahoma Aulacogen. Therefore, the failed rift system in the Wet Mountains is interpreted to be a northwestern continuation of the Southern Oklahoma Aulacogen with carbonatite magmatism and thorium/REE mineralization representing late intrusive phases.
An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers.
The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers.
We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management.
Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management.
Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01623-z","usgsCitation":"Fusco, E.J., Beaury, E.M., Bradley, B., Cox, M., Jarnevich, C.S., Mahood, A.L., Nagy, R.C., Nietupski, T., and Halofsky, J.E., 2023, The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States: Landscape Ecology, v. 38, p. 3825-3843, https://doi.org/10.1007/s10980-023-01623-z.","productDescription":"19 p.","startPage":"3825","endPage":"3843","ipdsId":"IP-145662","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":415769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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L.","contributorId":304165,"corporation":false,"usgs":false,"family":"Mahood","given":"Adam","email":"","middleInitial":"L.","affiliations":[{"id":65988,"text":"Earth Lab, University of Colorado Boulder and U.S Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":869519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nagy, R. Chelsea","contributorId":298272,"corporation":false,"usgs":false,"family":"Nagy","given":"R.","email":"","middleInitial":"Chelsea","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":869520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nietupski, Ty","contributorId":304166,"corporation":false,"usgs":false,"family":"Nietupski","given":"Ty","email":"","affiliations":[{"id":65989,"text":"ORISE Fellow hosted at the U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":869521,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Halofsky, Jessica E.","contributorId":146628,"corporation":false,"usgs":false,"family":"Halofsky","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":869522,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241872,"text":"ofr20221115 - 2023 - Geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California","interactions":[],"lastModifiedDate":"2026-02-10T21:15:46.852915","indexId":"ofr20221115","displayToPublicDate":"2023-03-29T10:44:23","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1115","displayTitle":"Geochronologic and Geochemical Data from Metasedimentary and Associated Rocks in the Lane Mountain Area, San Bernardino County, California","title":"Geochronologic and geochemical data from metasedimentary and associated rocks in the Lane Mountain area, San Bernardino County, California","docAbstract":"Eugeoclinal metasedimentary and metavolcanic rocks in the Lane Mountain area, California, are considered part of the El Paso terrane, which is commonly thought to have been displaced several hundred kilometers (km) southeastward from its place of origin during late Paleozoic truncation of the North American continental margin. Uranium-lead dating of detrital zircons from this area was undertaken to limit the depositional ages of these nearly non-fossiliferous metamorphic rocks. Analysis of detrital zircons from 17 metasedimentary rock samples yielded a composite age distribution that ranges from Archean to Jurassic and has significant peaks at ~2,800 2,400 mega-annum (Ma), 2,100–1,600 Ma, and ~300–200 Ma. The Proterozoic and Archean ages indicate derivation from continental sources in ancestral North America, whereas the late Paleozoic and Mesozoic ages are interpreted as derived from a magmatic arc that began to develop along the continental margin in Permian to Triassic time.
The 17 detrital zircon samples are from quartzitic and conglomeratic rocks of the Carbide, Williams Well, and Noble Well formations, which were informally named by T.H. McCulloh in 1960. The zircon data indicate that the oldest rocks in the Carbide formation are quartzites likely correlative with the Ordovician Eureka Quartzite of the Cordilleran miogeocline. These rocks lie structurally above the rest of the Carbide formation, different units of which yielded zircons that indicate maximum depositional ages ranging from middle Paleozoic to Late Triassic. Zircons from the Williams Well and Noble Well formations indicate maximum depositional ages of late Paleozoic and Early Jurassic, respectively. The Noble Well formation is interpreted to correlate with the lithologically similar, Early Jurassic, Fairview Valley Formation of the Black and Quartzite Mountain areas some 60 km to the southwest.
The above interpretations depend on the presumption that the detrital zircons in these samples did not undergo extreme, postdepositional lead loss, which would result in misleadingly young ages. Although such lead loss is considered unlikely for these samples, further work could test the validity of this interpretation.
Zircons from six additional samples were also analyzed: (1) a quartzite from which all the zircons are interpreted to have formed by Late Jurassic metamorphism; (2) three samples interpreted as albitized igneous rocks of Middle Permian age; and (3) two samples interpreted as fine-grained monzonite to diorite of Late Jurassic age. Both sets of igneous rocks were initially thought to be metasedimentary but were reinterpreted as igneous largely on the basis of the zircon data.
Based on the interpretations presented here, this study demonstrates that the depositional, magmatic, and deformational history of the El Paso terrane was longer and more complex than previously thought and will require reevaluation of existing tectonic models involving this terrane.
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