Many mark–recapture models assume that releases and recaptures are discrete events, and researchers often aggregate continuous recapture data (e.g., passive integrated transponder [PIT] detections) into coarse temporal scales to satisfy this assumption. This temporal discretization could result in parameter biases by ignoring the individual heterogeneity in the time susceptible to mortality after recapture and the conditions experienced (e.g., temperature and predation risk) before and after recapture. Our objectives were to (1) estimate the amount of bias in survival and emigration rates due to different temporal discretization durations when recapture events occur continuously and (2) apply this semicontinuous model to estimate rates of early emigration and overwinter survival for Coho Salmon Oncorhynchus kisutch in a coastal California watershed.
We developed a semicontinuous time multistate mark–recapture model to separately estimated emigration and survival rates throughout the year. We used weekly time-varying occasions paired with discrete spatial states and conducted extensive simulation trials to explore potential model bias. We then applied the model to an existing 4-year dataset of Coho Salmon PIT tag detections.
Our simulations indicated that that the amount of bias in survival and movement rates decreased as the temporal discretization duration decreased. The confidence interval of the bias estimates included zero with a duration of 8 days, indicating that this duration was sufficiently short to model movement and survival. Results from our Coho Salmon analysis suggest that overwinter survival rate ranged from 0.72 to 0.83, which is higher than previous estimates for Coho Salmon in this region. We estimate that a substantial proportion of smaller juveniles (0.21–0.28 annually) move to downstream nonnatal rearing habitats before the spring smolt migration.
Our semicontinuous modeling approach can be implemented relatively easily and used to analyze continuous detection data to accurately estimate survival and movement rates. Our analysis of Coho Salmon PIT tag detections implies that previous low estimates of apparent overwinter survival of Coho Salmon were partially due to high movement rates to alternative rearing locations. This contrasts with conclusions from the previous research that suggested that overwinter survival was a major limiting factor for population recovery and implies that species recovery may be improved by considering multiple emigration patterns in the design of future research, monitoring, and restoration projects.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10471","usgsCitation":"Van Vleet, N.P., Ward, D., Som, N.A., Barton, D.C., Anderson, C., and Henderson, M., 2024, It's about time: A multistate semicontinuous time mark–recapture model to evaluate seasonal survival and movement rates of juvenile Coho Salmon in a small coastal watershed: Transactions of the American Fisheries Society, v. 153, no. 5, p. 541-558, https://doi.org/10.1002/tafs.10471.","productDescription":"18 p.","startPage":"541","endPage":"558","ipdsId":"IP-155645","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439222,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10471","text":"Publisher Index Page"},{"id":433668,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Freshwater Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.16553732037661,\n 40.822354187176614\n ],\n [\n -124.16553732037661,\n 40.688724809582\n ],\n [\n -123.9770464919041,\n 40.688724809582\n ],\n [\n -123.9770464919041,\n 40.822354187176614\n ],\n [\n -124.16553732037661,\n 40.822354187176614\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Vleet, Nicholas P.","contributorId":342870,"corporation":false,"usgs":false,"family":"Van Vleet","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":910459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, Darren","contributorId":342871,"corporation":false,"usgs":false,"family":"Ward","given":"Darren","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":910460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":910461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barton, Daniel C.","contributorId":88221,"corporation":false,"usgs":true,"family":"Barton","given":"Daniel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":910462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Colin","contributorId":342879,"corporation":false,"usgs":false,"family":"Anderson","given":"Colin","email":"","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":910463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":910464,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","interactions":[{"subject":{"id":70261584,"text":"70261584 - 2024 - Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"70261584","publicationYear":"2024","noYear":false,"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"predicate":"SUPERSEDED_BY","object":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"id":1}],"supersededBy":{"id":70266894,"text":"ofr20251007 - 2025 - Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","indexId":"ofr20251007","publicationYear":"2025","noYear":false,"title":"Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery"},"lastModifiedDate":"2025-05-20T13:24:14.978891","indexId":"70261584","displayToPublicDate":"2024-08-09T08:50:10","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery","docAbstract":"Two eelgrass (Zostera marina) maps of Izembek Lagoon, Alaska, were generated by first creating maps of spectrally unique classes from each of two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing the spectral classes with information about eelgrass conditions based on field data. Maps depicting various eelgrass metrics, such as percent cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons between the 2016 and 2020 Sentinel-2 maps of eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined between 2006 and 2020 were most prevalent in the central part Izembek Lagoon, while areas of possible biomass decline were more prevalent in the southern part between 2016 and 2020. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data provides capabilities for making comparisons over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2024.08.07.607047","usgsCitation":"Douglas, D.C., Fleming, M., Patil, V.P., and Ward, D.H., 2024, Mapping eelgrass cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery: BioRxiv, https://doi.org/10.1101/2024.08.07.607047.","productDescription":"35 p.","ipdsId":"IP-168423","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466967,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.08.07.607047","text":"External Repository"},{"id":465143,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -162.9274024959248,\n 55.158158818332055\n ],\n [\n -162.84978004715862,\n 55.18903118481694\n ],\n [\n -162.68483234353056,\n 55.32568974463476\n ],\n [\n -162.63354608309427,\n 55.35091614284903\n ],\n [\n -162.5670125555805,\n 55.33988157021267\n ],\n [\n -162.4880039916577,\n 55.3792766436201\n ],\n [\n -162.49632068259703,\n 55.470522299013965\n ],\n [\n -162.5891903980851,\n 55.45166118374158\n ],\n [\n -162.78047428968748,\n 55.384001418091316\n ],\n [\n -162.88304681127136,\n 55.3438228419308\n ],\n [\n -163.03274724817751,\n 55.22383284787324\n ],\n [\n -163.09789466053493,\n 55.170827297352844\n ],\n [\n -162.9274024959248,\n 55.158158818332055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":921108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleming, Michael D.","contributorId":332620,"corporation":false,"usgs":false,"family":"Fleming","given":"Michael D.","affiliations":[{"id":79518,"text":"Images Unlimited","active":true,"usgs":false}],"preferred":false,"id":921109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":921110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":921111,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259095,"text":"70259095 - 2024 - Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection","interactions":[],"lastModifiedDate":"2024-09-27T11:59:37.62189","indexId":"70259095","displayToPublicDate":"2024-08-09T06:54:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection","docAbstract":"Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.
Fish stranding has been studied in select rivers worldwide, often with the purpose of determining how to mitigate adverse effects of dam operations on highly valued salmon and trout populations. However, where a reduction in trout population size is desired by resource managers, as is the case downstream of the Glen Canyon Dam on the Colorado River, flow manipulations termed trout management flows (TMFs) may be used to optimize fish stranding and mortality. To inform the design and implementation of potential future TMFs, we reviewed relevant literature to identify key factors that influence fish stranding. We found that key factors were highly interdependent and site-specific, but general trends suggest that down-ramping (decreasing flow) at rapid rates in daytime during the late spring to summer emergence period would lead to stranding of age-0 rainbow trout in shallow shoreline habitat. A hypsometric analysis was then used to predict stranding risk for age-0 rainbow trout in Glen Canyon for a range of TMFs, which incorporated existing bathymetric data and flow and habitat suitability models. Our results indicate that a TMF with a steady high flow ranging from 12,000 to 16,000 cubic feet per second (ft3/s) combined with a minimum flow ranging from 3,000 to 5,000 ft3/s may effectively strand age-0 fish while also minimizing risk to water storage in Lake Powell and other resources. This strategy implemented under normal hydropeaking operations was predicted to lead to a substantive stranding risk when paired with low flows of 5,000 ft3/s, and especially 3,000 ft3/s. However, there remains uncertainty associated with elements of implementing an effective TMF downstream from Glen Canyon Dam. The main uncertainties include (1) the down-ramp rate that maximizes stranding of age-0 trout, (2) the duration of drawdown to maximize stranding mortality while minimizing impact to downstream resources, (3) duration of high flows required for age-0 fish to colonize newly created shoreline habitat (this is only for certain TMF hydrographs), (4) number of repetitions of TMF cycles to minimize compensatory survival response, and (5) recruitment threshold of both rainbow and brown trout populations to trigger TMF implementation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241033","collaboration":"Prepared in cooperation with Ecometric Research Inc.","usgsCitation":"Giardina, M., Korman, J., Yard, M.D., Wright, S., Kaplinski, M., and Bennett, G., 2024, A literature review and hypsometric analysis to support decisions on trout management flows on the Colorado River downstream from Glen Canyon Dam: U.S. Geological Survey Open-File Report 2024–1033, 50 p., https://doi.org/10.3133/ofr20241033.","productDescription":"Report: viii, 50 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-133316","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":432181,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241033/full"},{"id":432178,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1XEZO","text":"USGS Data Release","description":"Korman, J., Giardina, M.A., Yard, M.D., Wright, S.A., Kaplinski, M., and Bennett, G., 2024, Colorado River milage system and ancillary attribute data for connecting to hydrodynamic model output in Glen Canyon, AZ: U.S. Geological Survey data release, https://doi.org/10.5066/P9L1XEZO.","linkHelpText":"Colorado River milage system and ancillary attribute data for connecting to hydrodynamic model output in Glen Canyon, AZ"},{"id":432177,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1033/ofr20241033.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432179,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1033/ofr20241033.xml"},{"id":432180,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1033/images"},{"id":432176,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1033/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.636412112225,\n 36.82347668968964\n ],\n [\n -111.44233548505323,\n 36.82347668968964\n ],\n [\n -111.44233548505323,\n 36.96315377672772\n ],\n [\n -111.636412112225,\n 36.96315377672772\n ],\n [\n -111.636412112225,\n 36.82347668968964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
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2255 N. Gemini Drive
Flagstaff, AZ 86001
Hydrologic stress is increasing in Fremont cottonwood (Populus fremontii) forests across the southwestern United States because of increased temperature and streamflow diversion. The spatial variability of this stress is large yet poorly understood. Along the Yampa and Green Rivers in Colorado and Utah, vapour pressure deficit and flow diversions increase downstream. To investigate effects of this gradient on cottonwoods, we measured the percent live canopy and height of randomly selected trees at three sites: Deerlodge Park on the Yampa River (DLP), Island Park on the upper Green (ILP) and Canyonlands National Park on the lower Green (CAN). From these same trees, we took increment cores to understand differences in tree growth in each forest over time. We then related tree metrics to local water availability, streamflow and climatic data. Cottonwoods at CAN were shorter and had lower percent live canopy and growth rate than similarly aged trees upstream. CAN trees that grew higher above the water surface also tended to have lower tree growth, height and live canopy percentage. Furthermore, the correlation between tree growth and maximum vapour pressure deficit showed a much stronger negative shift since 1990 at CAN than at the other sites. All of these differences suggest higher hydrologic stress at CAN, which we attribute to the combined effects of peak flow declines from Flaming Gorge Reservoir, flow diversion and the higher and increasing vapour pressure deficit at CAN. Further research on the variability of hydrologic stress on cottonwoods could help managers anticipate and mitigate the effects of drought stress in these iconic forests.
Background
Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is urgently needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored.
Methods
We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ⇠ 85% of the individuals have been directly monitored.
Results
Through the protocol, we demonstrated how pre-network data permu tations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples.
Conclusions
The protocol allows researchers to analyse GPS-based radio telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under di↵erent conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-024-00494-6","usgsCitation":"Kaur, P., Ciuti, S., Ossi, F., Cagnacci, F., Morellet, N., Loison, A., Atmeh, K., McLoughlin, P., Reinking, A., Beck, J.L., Ortega, A.C., Kauffman, M., Boyce, M.S., Haigh, A., David, A., Griffin, L., Conteddu, K., Faull, J., and Salter-Townshend, M., 2024, A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data, v. 12, 55, 36 p., https://doi.org/10.1186/s40462-024-00494-6.","productDescription":"55, 36 p.","ipdsId":"IP-167767","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485356,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":487941,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00494-6","text":"Publisher Index Page"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaur, Prabhleen","contributorId":354311,"corporation":false,"usgs":false,"family":"Kaur","given":"Prabhleen","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":935387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ciuti, Simone","contributorId":348021,"corporation":false,"usgs":false,"family":"Ciuti","given":"Simone","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":935388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ossi, Federico","contributorId":342386,"corporation":false,"usgs":false,"family":"Ossi","given":"Federico","email":"","affiliations":[{"id":81867,"text":"Research and Innovation Centre, Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":935389,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cagnacci, Francesca","contributorId":342410,"corporation":false,"usgs":false,"family":"Cagnacci","given":"Francesca","affiliations":[{"id":81867,"text":"Research and Innovation Centre, Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":935390,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morellet, Nicolas","contributorId":342402,"corporation":false,"usgs":false,"family":"Morellet","given":"Nicolas","affiliations":[{"id":41661,"text":"Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":935391,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loison, Anne","contributorId":284699,"corporation":false,"usgs":false,"family":"Loison","given":"Anne","email":"","affiliations":[],"preferred":false,"id":935392,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Atmeh, Kamal","contributorId":348008,"corporation":false,"usgs":false,"family":"Atmeh","given":"Kamal","affiliations":[{"id":83278,"text":"Laboratoire Biometrie; 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In this study, we examined chemical and isotopic data to assess if thermogenic gas or saline water from oil producing formations have mixed with groundwater near the Elk Hills and North Coles Levee Oil Fields in the southwestern San Joaquin Valley. Major ion concentrations and stable isotope compositions were largely consistent with natural processes, including mixing of different recharge sources and water-rock interactions. Trace methane concentrations likely resulted from microbial rather than thermogenic sources. Trace concentrations of benzene and other dissolved hydrocarbons in three wells had uncertain sources that could occur naturally or be derived from oil and gas development activities or other anthropogenic sources. In the mid-1990s, two industrial supply wells had increasing Cl and B concentrations likely explained by mixing with up to 15 percent saline oil-field water injected for disposal in nearby injection disposal wells. Shallow groundwater along the western margin of Buena Vista Lake Bed had elevated Cl, B, and SO4 concentrations that could be explained by accumulation of salts during natural wetting and drying cycles or, alternatively, legacy surface disposal of saline oil-field water in upgradient ephemeral drainages. This study showed that groundwater had relatively little evidence of thermogenic gas or saline water from oil and gas sources in most parts of the study area. However, the evidence for groundwater mixing with injected disposal water, and possibly legacy surface disposal water, demonstrates produced water management practices as a potential risk factor for groundwater-quality degradation near oil and gas fields. Additional studies in the San Joaquin Valley and elsewhere could improve understanding of such risks by assessing the locations, volumes, and types of produced water disposal practices used during the life of oil fields.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pwat.0000258","usgsCitation":"Warden, J.G., Landon, M.K., Stephens, M.J., Davis, T., Gillespie, J.M., McMahon, P.B., Kulongoski, J.T., Hunt, A., Shimabukuro, D.H., Gannon, R., and Ball, L.B., 2024, Assessing potential effects of oil and gas development activities on groundwater quality near and overlying the Elk Hills and North Coles Levee Oil Fields, San Joaquin Valley, California: PLOS Water, v. 3, no. 8, e0000258, 37 p., https://doi.org/10.1371/journal.pwat.0000258.","productDescription":"e0000258, 37 p.","ipdsId":"IP-153863","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":439229,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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especially of coral reef ecosystems, can significantly reduce the exposure of coastal communities to natural hazards and, consequently, the risk of wave-driven flooding. Likewise, reef degradation can increase coastal flood risks to people and property. In this study, the valuation of coral reefs in the United States Virgin Islands (USVI), along the coasts of St. Croix, St. John, and St. Thomas, demonstrated the social and economic benefits provided by these natural defenses. Across the territory, more than 481 people and $31.2 million of infrastructure were estimated to receive protection from coral reefs per year (2010 U.S. dollars). In 2017, Hurricanes Irma and Maria significantly damaged coral reefs throughout the archipelago. By combining engineering, ecological, geospatial, social, and economic data and tools, this study provided a rigorous valuation of where potential coral reef restoration projects could help rebuild these damaged habitats and decrease the risks from coastal hazards faced by USVI’s reef-fronted communities. Multiple restoration scenarios were considered in the analysis, two of which are detailed in this report. These include (1) ‘Ecological’ restoration, where restoration creates a structure that is 0.25 m high and 25-m-wide reef, and (2) ‘Hybrid’ restoration, where restoration creates a structure that is 1.25 m high and 5 m wide. There are many ways that such structures could be developed. In the hydrodynamic analyses, there are no assumptions about how the restoration is developed. Many practitioners of both coral (and oyster reef) restoration consider that a reef height of 0.25 m might be delivered from planting corals alone and that 1.25 m might require a combination of artificial structures and coral planting. In a third scenario, the analysis investigated the reduction of protection benefits that would occur through the reduction of 1 meter of naturally occurring reef height due to reef degradation. The reduction of protection due to the loss of reefs can also be interpreted as the protection value of the existing reefs.
In all studied restoration scenarios, it was assumed that the planting of corals would enhance hydrodynamic roughness, effectively dissipating incident wave energy and reducing the potential for coastal flooding. A standardized approach was employed to strategically locate potential restoration projects along the entire linear extent of existing reefs bordering the USVI, and to identify where coral reef restoration could offer valuable benefits in flood reduction. Potential restoration projects were only located within the existing distribution of reefs across the region, even though numerous sites were positioned far offshore (2-3 km), and some were at relatively deep depths (up to 7 m). Risk-based valuation approaches were followed to delineate flood zones at a 10 m2 resolution along the entire region's reef-lined shorelines for all the potential coral reef restoration scenarios. These were subsequently compared to flood zones without coral reef restoration.
The potential reduction in coastal flood risk provided by coral reef restoration, and the protection value of existing reefs, were quantified utilizing the latest information available at the time of analysis from the U.S. Census Bureau, Federal Emergency Management Agency (FEMA), and Bureau of Economic Analysis for return-interval storm events. The change in Expected Annual Damages (EAD), a metric indicating the annual protection gained due to coral reef restoration, was calculated based on the damages associated with each storm probability. The findings suggest that the benefits of reef restoration are spatially variable within the USVI. In some areas, the analysis showed limited benefits from reef restoration, which may be attributed to the depth or offshore distances of proposed restoration sites. However, there were a number of key areas where reef restoration could have substantial benefits for flood risk reduction.
The annual flood risk reduction attributed to potential ‘ecological’ coral reef restoration in the USVI was 99 people and $6.1 million (2010 U.S. dollars). The Benefit-to-Cost Ratio (BCR) for this restoration approach was found to be larger than 1 (i.e., cost-effective) along 11% of the St. Croix coastline, 4.9% of the St. John coastline, and 8.7% of the St. Thomas coastline. This analysis offers stakeholders and decision-makers a spatially explicit and rigorous evaluation that illustrates how, where, and when potential coral reef restoration efforts in St. Croix, St. John, and St. Thomas could be instrumental to reducing coastal storm-induced flooding. Understanding areas where reef management, recovery, and restoration could effectively reduce climate hazard-related risks is crucial to protect and enhance the resilience of coastal communities in USVI.
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Celeste","contributorId":341906,"corporation":false,"usgs":false,"family":"Jarvis","given":"Celeste","email":"","affiliations":[{"id":33811,"text":"TNC","active":true,"usgs":false}],"preferred":false,"id":909101,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":909102,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70264829,"text":"70264829 - 2024 - Observations of flocs in an estuary and implications for computation of settling velocity","interactions":[],"lastModifiedDate":"2025-03-26T15:29:46.647918","indexId":"70264829","displayToPublicDate":"2024-08-04T08:01:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2321,"text":"Journal of Geophysical Research: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Observations of flocs in an estuary and implications for computation of settling velocity","docAbstract":"The settling velocity (ws) in estuarine environments can impact whether a region is eroding or accreting sediment on the bed, yet determining this rate can be an indirect process requiring a number of assumptions. Accurate determination of ws is especially needed for numerical models to reproduce observed sediment concentrations at the appropriate timescale. We collected information on suspended sediment flocculation at a channel site (13 m deep) and a shallows site (4 m deep) within South San Francisco Estuary, alongside timeseries of flow, wave statistics, turbulent shear, and bottle samples analyzed for both ws and particle size. Using the measurements of floc size and settling velocity, we performed a sensitivity analysis on the unknown parameters in the general explicit formula for settling velocity. The collected particle size distribution data show that multiple classes of flocs are present; these are characterized as flocculi, microflocs, and macroflocs. We show that ws of flocculi is closest to ws for the full distribution. The determined parameter values lead to near-bed mass-weighted settling velocities (standard deviation) of 1.18 (0.55) and 0.22 (0.15) mm/s at the channel and shallows sites, respectively. Modeling efforts can use this work to help select an appropriate sediment model and parameter values.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JC019402","usgsCitation":"Allen, R., Livsey, D., and McGill, S., 2024, Observations of flocs in an estuary and implications for computation of settling velocity: Journal of Geophysical Research: Oceans, v. 129, no. 8, e2022JC019402, 21 p., https://doi.org/10.1029/2022JC019402.","productDescription":"e2022JC019402, 21 p.","ipdsId":"IP-143577","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488664,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jc019402","text":"Publisher Index Page"},{"id":483876,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.73260724811783,\n 38.375373912625804\n ],\n [\n -123.73260724811783,\n 37.14063832700886\n ],\n [\n -121.47531521074498,\n 37.14063832700886\n ],\n [\n -121.47531521074498,\n 38.375373912625804\n ],\n [\n -123.73260724811783,\n 38.375373912625804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Rachel 0000-0002-0287-6466","orcid":"https://orcid.org/0000-0002-0287-6466","contributorId":216002,"corporation":false,"usgs":true,"family":"Allen","given":"Rachel","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Livsey, Daniel","contributorId":352687,"corporation":false,"usgs":false,"family":"Livsey","given":"Daniel","affiliations":[{"id":37600,"text":"Queensland University of Technology","active":true,"usgs":false}],"preferred":false,"id":932005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGill, Samantha C. 0000-0001-9320-8764","orcid":"https://orcid.org/0000-0001-9320-8764","contributorId":304095,"corporation":false,"usgs":true,"family":"McGill","given":"Samantha C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932006,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256696,"text":"sir20245055 - 2024 - Low-flow statistics for selected streams in New York, excluding Long Island","interactions":[],"lastModifiedDate":"2026-02-03T19:42:11.527606","indexId":"sir20245055","displayToPublicDate":"2024-08-02T15:12:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5055","displayTitle":"Low-Flow Statistics for Selected Streams in New York, Excluding Long Island","title":"Low-flow statistics for selected streams in New York, excluding Long Island","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, updated low-streamflow statistics for New York, excluding Long Island and including hydrologically connected watersheds in bordering States, for the first time since 1972. Historical daily streamflow data for active and inactive gages were considered for this study with periods of record as recent as March 31, 2022, adding 50 years of data to the last published low-streamflow statistics report for New York and including 119 new gages where low-streamflow statistics are calculated for the first time. Gages were evaluated across several criteria to identify gages that were not suitable for low-streamflow frequency analysis. In addition, gages were evaluated for the presence of alteration within the streamflow period of record based on previous studies and U.S. Geological Survey National Water Information System site metadata including peak flow codes.
A trend analysis was performed using the Wilcoxon rank-sum hypothesis test comparing data from the most recent 30 years of record to data from 30 years and earlier for each long-record gage (30 years or more of available data). Results from the trend analysis indicated that 45 unaltered and 32 altered long-record sites had a statistically significant trend for the annual minimum n-day time series; most gages showed increasing trends in the annual minimum n-day time series. Low-streamflow statistics were calculated using the most recent 30 years of record for gages with a statistically significant trend. Before and after 1972, the lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10 statistics respectively) increased significantly at 41 unaltered gages and decreased significantly at 3 unaltered gages where data were available.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245055","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Graziano, A.P., Woda, J.C., Glas, R.L., and Gazoorian, C.L., 2024, Low-flow statistics for selected streams in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2024–5055, 39 p., https://doi.org/10.3133/sir20245055.","productDescription":"Report: vi, 39 p.; Data Release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-157678","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":432052,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5055/images/"},{"id":499477,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117164.htm","linkFileType":{"id":5,"text":"html"}},{"id":432053,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":432048,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5055/coverthb.jpg"},{"id":432049,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5055/sir20245055.pdf","text":"Report","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5055 PDF"},{"id":432051,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5055/sir20245055.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5055 XML"},{"id":432050,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245055/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5055 HTML"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91286009788664,\n 41.01178774841347\n ],\n [\n -74.0266841070774,\n 40.720375728237144\n ],\n [\n -73.9615635090029,\n 40.70803672971712\n ],\n [\n -73.67394830242374,\n 40.966707520822155\n ],\n [\n -73.67401345493704,\n 41.01587061494237\n ],\n [\n -73.7610093516628,\n 41.09363100726691\n ],\n [\n -73.47905430116539,\n 41.21618146660194\n ],\n [\n -73.54958995108733,\n 41.28551897494029\n ],\n [\n -73.48572425776553,\n 42.050669593361164\n ],\n [\n -73.52340772727291,\n 42.08675087137979\n ],\n [\n -73.27944022486994,\n 42.71925225128092\n ],\n [\n -73.2459886442373,\n 43.56229834373957\n ],\n [\n -73.31078219020274,\n 43.62532933066436\n ],\n [\n -73.37060286487959,\n 43.61346437461091\n ],\n [\n -73.43066543683648,\n 43.58573443689042\n ],\n [\n -73.35806708414735,\n 43.79014308086275\n ],\n [\n -73.40009937812863,\n 44.00975633545363\n ],\n [\n -73.41060974678811,\n 44.08529772972881\n ],\n [\n -73.35805219069586,\n 44.318863718851276\n ],\n [\n -73.33701549362132,\n 44.52903276098101\n ],\n [\n -73.36854225932441,\n 44.626354722690195\n ],\n [\n -73.3054828249653,\n 44.73844638732069\n ],\n [\n -73.37904970354246,\n 44.83541786386914\n ],\n [\n -73.34751720937287,\n 45.0140137308498\n ],\n [\n -74.34598531174339,\n 44.99914556300604\n ],\n [\n -74.8399460822856,\n 45.02144131236122\n ],\n [\n -75.34442725785361,\n 44.80559688881786\n ],\n [\n -75.71227781323351,\n 44.58894197821161\n ],\n [\n -75.93298792150392,\n 44.363963821867316\n ],\n [\n -76.22726798212784,\n 44.23609096764133\n ],\n [\n -76.4374686507537,\n 44.085295715094446\n ],\n [\n -76.80532026610759,\n 43.62298965505482\n ],\n [\n -78.73916539102125,\n 43.63059733126207\n ],\n [\n -79.1910965886907,\n 43.44011660394341\n ],\n [\n -79.02292338480896,\n 43.20308949268261\n ],\n [\n -79.06495062651008,\n 43.0880701724453\n ],\n [\n -78.88515521912908,\n 42.85650132783874\n ],\n [\n -79.77268100220316,\n 42.50253000668161\n ],\n [\n -79.7434614428243,\n 42.00043762996769\n ],\n [\n -75.398839703039,\n 41.97678364767139\n ],\n [\n -75.09566664827426,\n 41.829506046160375\n ],\n [\n -74.91860171317268,\n 41.43129306375607\n ],\n [\n -73.91286009788664,\n 41.01178774841347\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The thematic issue entitled, “Current Advances in Coastal Wetland Elevation Dynamics,” draws on topics from two special sessions at the CERF 2021 conference plus additional recent research describing scientific insights gained from the Surface Elevation Table–Marker Horizon (SET–MH) method and its application across the globe to quantify and understand subsurface process influences on wetland elevation change and wetland responses to sea-level rise. The findings group articles within each of five thematic topics. (1) A 30-year retrospective on the scientific insights gained on surface and shallow subsurface process dynamics. (2) Investigations of the subsurface soil process influences on wetland elevation. (3) How the scientific community applies the SET–MH method to quantify and understand wetland responses to RSLR and other environmental drivers such as altered hydrology and sediment supply. (4) How SET–MH data are used in long-term monitoring networks at different geographic scales. (5) Pairing the SET-MH method with (a) survey techniques to increase lateral coverage of wetland elevation trends and (b) geodetic measurements to increase vertical coverage of vertical land motion.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-024-01411-1","usgsCitation":"Cahoon, D., and Guntenspergen, G.R., 2024, Current advances in coastal wetland elevation dynamics: Introduction to the special issue: Estuaries and Coasts, v. 47, p. 1703-1707, https://doi.org/10.1007/s12237-024-01411-1.","productDescription":"5 p.","startPage":"1703","endPage":"1707","ipdsId":"IP-167659","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-024-01411-1","text":"Publisher Index Page"},{"id":483451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationDate":"2024-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257086,"text":"70257086 - 2024 - Peri-Gondwanan sediment in the Arkoma Basin derived from the north: The detrital zircon record of a uniquely concentrated non-Laurentian source signal in the late Paleozoic","interactions":[],"lastModifiedDate":"2024-10-07T16:14:46.422867","indexId":"70257086","displayToPublicDate":"2024-08-02T06:46:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Peri-Gondwanan sediment in the Arkoma Basin derived from the north: The detrital zircon record of a uniquely concentrated non-Laurentian source signal in the late Paleozoic","docAbstract":"During the assembly of Pangea, peri-Gondwanan terranes collided with the eastern and southern margins of Laurentia and brought with them unique detrital zircon U-Pb signatures. Discriminating between individual peri-Gondwanan terranes in the detrital record is difficult due to their similar geologic histories. However, characterization of this provenance is critical for understanding late Paleozoic sediment routing during development of Pangea. Along southeastern Laurentia, in the Arkoma Basin (present-day Arkansas and eastern Oklahoma, southeastern United States), we identified Middle Pennsylvanian (Desmoinesian) strata that exhibit a concentrated peri-Gondwanan detrital zircon signature (e.g., ca. 800–550 Ma). Although several southern peri-Gondwanan terranes (e.g., Maya, Suwannee) are closer to the Arkoma Basin, geologic data, such as predominantly north-to-south paleocurrents and proximal-to-distal facies relationships in these Desmoinesian strata, support a northern source (e.g., Ganderia, Avalonia, Meguma). Further evidence of a northern source comes from detrital zircon source mapping, which reveals the persistence of this peri-Gondwanan signal in depocenters to the north of the basin after the signal had diminished in the Arkoma Basin. To this end, bottom-up detrital zircon source modeling, source mapping, regional stratigraphy, paleocurrent data, and sandstone petrography allow us to reconstruct the evolution of this Middle Pennsylvanian (Desmoinesian) sediment pathway in the context of intraplate and plate-margin tectonic activity. This reconstruction documents processes affecting Earth’s surface (e.g., tectonics, climate) during the assembly of Pangea and describes in detail part of a dynamic continental-scale drainage system.
Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included:
● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting,
● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels,
● Improve the use of networking technologies for monitoring and modeling efforts,
● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs,
● Develop food web models on the ecosystem fate and effects of toxins,
● Develop and improve species-specific models that link to physical-biological models.
Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts.
Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Harmful algal research & response: A national environmental science strategy (HARRNESS), 2024-2034","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Woods Hole Oceanographic Institution","doi":"10.1575/1912/69773","usgsCitation":"Bouma-Gregson, K., Doucette, G., Graham, J.L., Kudela, R., Stauffer, B., Anderson, C., Bratton, J.F., Holcomb, B.M., Hubbard, K., Norris, T., Stiles, T., Tango, P.J., Raymond, H., and Zubkousky, V., 2024, Observing systems, modeling, and forecasting, 25 p., https://doi.org/10.1575/1912/69773.","productDescription":"25 p.","startPage":"31","endPage":"55","ipdsId":"IP-146565","costCenters":[{"id":154,"text":"California Water Science 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jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kudela, Raphael","contributorId":196461,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","affiliations":[],"preferred":false,"id":922061,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stauffer, Beth","contributorId":347626,"corporation":false,"usgs":false,"family":"Stauffer","given":"Beth","affiliations":[],"preferred":false,"id":922072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Clarissa 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M.","contributorId":53700,"corporation":false,"usgs":true,"family":"Holcomb","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":922060,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hubbard, Kate","contributorId":347683,"corporation":false,"usgs":false,"family":"Hubbard","given":"Kate","affiliations":[],"preferred":false,"id":922062,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Norris, Tenaya","contributorId":347684,"corporation":false,"usgs":false,"family":"Norris","given":"Tenaya","affiliations":[],"preferred":false,"id":922063,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stiles, Tom","contributorId":347685,"corporation":false,"usgs":false,"family":"Stiles","given":"Tom","affiliations":[],"preferred":false,"id":922064,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tango, Peter J. pjtango@usgs.gov","contributorId":4088,"corporation":false,"usgs":true,"family":"Tango","given":"Peter","email":"pjtango@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922065,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Raymond, Heather","contributorId":291257,"corporation":false,"usgs":false,"family":"Raymond","given":"Heather","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":922067,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Zubkousky, Vanessa","contributorId":347686,"corporation":false,"usgs":false,"family":"Zubkousky","given":"Vanessa","affiliations":[],"preferred":false,"id":922066,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70258271,"text":"70258271 - 2024 - Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands","interactions":[],"lastModifiedDate":"2024-09-11T15:14:35.971177","indexId":"70258271","displayToPublicDate":"2024-08-01T10:13:51","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR—2024/176","title":"Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands","docAbstract":"We estimated both abundance and distribution of white-tailed deer (Odocoileus virginianus) on the Brandywine Valley unit of First State National Historical Park (FRST) and the Brandywine Creek State Park (BCSP) during 2020 and 2021 with two widely used field methods — a road-based count and a network of camera traps. We conducted 24 road-based counts, covering 260 km of roadway, and deployed up to 16 camera traps, processing over 82,000 images representing over 5,000 independent observations.
In both years, we identified bucks based on their body and antler characteristics, tracking their movements between baited camera trap locations. We tested seven estimators commonly reported in the literature, comparing the relative merits for managers of small, protected natural areas like FRST.
Deer densities estimated from conventional road-based distance sampling were approximately 10 deer/km2 lower than densities estimated from camera-trapping surveys. We attribute the bias in roadbased distance sampling to the difficulty of recording the precise effort expended to obtain the counts. Modifying the distance sampling method addressed many of the issues associated with the conventional approach. Despite little substantive differences in land cover types between the two methods, a clear spatial segregation of male and female deer at camera trap locations could bias roadbased counts if the sexes are not encountered in proportion to their abundances. There was a distinct gradient in deer distribution across the study area, with higher proportions of deer recorded in camera traps at FRST than BCSP, which harvests 20–60 deer annually during a regulated, hunting season.
The most reliable (i.e., low bias, acceptable precision) methods, Spatial Capture Recapture (SCR) and Density Surface Modeling (DSM), produced deer densities of approximately 50 deer/km2 in each year — a number which is consistent with previous estimates for New Castle County, Delaware, and our experience in similar, unhunted natural areas. Across both FRST and BCSP, these densities translated into area-wide (~1000 ha) population sizes between 650–1000 deer, with about one-half to two-thirds comprising the FRST population.
Density surface modeling of mapped locations of deer detected during surveys, combined with camera-trapping and a time-to-event data analysis might be the only practical means of reliably assessing white-tailed deer abundance in small (<2000 ha), protected natural areas like FRST. Most other approaches are either too time-consuming, require identification and tracking of individual deer, the use of bait, or require intervention by a subject-area expert.
","language":"English","publisher":"National Park Service","doi":"10.36967/2305428","usgsCitation":"Underwood, H.B., Hand, M.R., and Leopold, D.J., 2024, Abundance and distribution of white-tailed deer on First State National Historical Park and surrounding lands: Science Report NPS/SR—2024/176, x, 76 p., https://doi.org/10.36967/2305428.","productDescription":"x, 76 p.","ipdsId":"IP-154880","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":433697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Pennsylvania","otherGeospatial":"Brandywine Creek State Park, Brandywine Valley unit of First State National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.57893855864407,\n 39.84864721075371\n ],\n [\n -75.57893855864407,\n 39.79988968093724\n ],\n [\n -75.54431018453508,\n 39.79988968093724\n ],\n [\n -75.54431018453508,\n 39.84864721075371\n ],\n [\n -75.57893855864407,\n 39.84864721075371\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Underwood, H. Brian 0000-0002-2064-9128 hbunderw@usgs.gov","orcid":"https://orcid.org/0000-0002-2064-9128","contributorId":140185,"corporation":false,"usgs":true,"family":"Underwood","given":"H.","email":"hbunderw@usgs.gov","middleInitial":"Brian","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":912809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hand, Madison R.","contributorId":344097,"corporation":false,"usgs":false,"family":"Hand","given":"Madison","email":"","middleInitial":"R.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":912810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Donald J.","contributorId":220142,"corporation":false,"usgs":false,"family":"Leopold","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":912811,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257603,"text":"70257603 - 2024 - Joint pilot fish habitat framework","interactions":[],"lastModifiedDate":"2024-08-20T15:11:01.81526","indexId":"70257603","displayToPublicDate":"2024-08-01T10:04:45","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18345,"text":"NOAA Story Map","active":true,"publicationSubtype":{"id":1}},"title":"Joint pilot fish habitat framework","docAbstract":"This story map will take you through the process of exploring and testing methods necessary for a higher resolution, seamless fish habitat assessment across both inland and estuarine waters through the lens of our joint pilot assessment
Fish habitat assessments attempt to relate past, current, or future landscape conditions to the state of fish species occurrence, distribution, abundance, or community and habitat condition in streams, rivers, or estuaries. Previous fish habitat assessments, such as the National Fish Habitat Assessment, conducted separate and disconnected assessments for inland waters and estuaries. In this project, National Oceanic and Atmospheric Administration ( NOAA ) and U.S. Geological Survey ( USGS ) researchers created a seamless spatial framework to allow assessments that integrate influences on fish habitat from headwaters to the estuary. This effort began when the Chesapeake Bay Program Fish Habitat Action Team expressed interest in a Baywide fish habitat assessment spanning tidal salt, tidal fresh, warm non-tidal, and cold non-tidal waters. However, the complexity of the myriad of implementation details to consider when developing such an assessment necessitated the need for a tributary-specific pilot assessment. To conduct this pilot assessment, a NOAA/USGS joint partnership was formed with cooperation and support from the Chesapeake Bay Agreement and Chesapeake Bay Fish Habitat Action Team (FHAT).
Climate change has and will continue to sharpen climate-related risks to communities and natural resources in California and elsewhere, as the probabilities of more extreme weather, floods, and fires continue to increase. This poses a problem of novel situations for emergency management. Progress has been made in terms of formally incorporating climate projections, data, and research on expected changes in climate-driven hazards into long-term hazard mitigation and climate adaptation strategies at both state and national levels. However, there are fewer examples of how climate change considerations have, or could be, incorporated into shorter-term emergency preparedness and response strategies. This is an important gap to fill, as climate resilience depends not only on mitigation and prevention measures, but also on the ability of agencies to coordinate and effectively minimize impacts when prevention measures fall short.
The goal of this primer is to provide guidance on how to incorporate the best available information on climate variability and change into emergency management planning, with a focus on the development and use of extreme weather event scenarios for use in exercises. The first section is aimed toward a broad audience, including emergency management practitioners who use extreme weather event scenarios. It provides an overview of available data and tools that can inform scenario design as well as techniques for scenario design based on the hazard of interest, the audience and application, and the technical skills and resources required to develop, summarize, and/or visualize the data. This section concludes with an overview of approaches and lessons learned related to extreme event response planning and exercise design. Overall, this section highlights the advantages of developing quantitative scenarios based on spatial data, which allows visualizations and interactive data explorations that can provide greater specificity in discussions related to preparedness and response strategies. It further highlights the advantages of developing a core expert working group to guide planning, holding pre-exercise workshops to engage diverse communities outside of the emergency management sector, and engaging decisionmakers post-exercise to communicate key issues and outcomes as well as potential approaches for mitigating consequences that were identified by participants.
The second section is aimed toward the scientific community and data developers involved in the creation of extreme weather event scenarios. This section provides technical guidance and detailed descriptions of four types of data resources and five analytical approaches that can be used to create extreme weather event scenarios based on the design considerations highlighted in section one. The computational resources and expertise required varies substantially across the options presented and is a primary consideration. These requirements, in addition to considerations related to audience and application, may determine the novelty and detail of the event, the detail of weather forecast information that can be provided, and the spatial extent across which the event can reasonably be modeled. The importance of, and approaches for, delivering information in a form that is accessible to emergency management practitioners is also discussed.
","language":"English","publisher":"Desert Research Institute","usgsCitation":"Albano, C.M., McCarthy, M.I., Mcafee, S.A., Wein, A., and Dettinger, M., 2024, Incorporating climate data into emergency planning and exercises: A primer for emergency management practioners and data developers, x, 32 p.","productDescription":"x, 32 p.","ipdsId":"IP-164123","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":433003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":433002,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.dri.edu/project/arkstormsierrafront-2-0/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Albano, Christine M.","contributorId":169455,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":911158,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Maureen I.","contributorId":343457,"corporation":false,"usgs":false,"family":"McCarthy","given":"Maureen","email":"","middleInitial":"I.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":911159,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAfee, Stephanie Anne 0000-0002-9313-5857","orcid":"https://orcid.org/0000-0002-9313-5857","contributorId":343458,"corporation":false,"usgs":true,"family":"McAfee","given":"Stephanie","middleInitial":"Anne","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":911160,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":911161,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dettinger, Michael D.","contributorId":343459,"corporation":false,"usgs":false,"family":"Dettinger","given":"Michael D.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":911162,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257588,"text":"70257588 - 2024 - Diminishing productivity and hyperstable harvest in northern Wisconsin walleye fisheries","interactions":[],"lastModifiedDate":"2024-12-11T15:56:41.280711","indexId":"70257588","displayToPublicDate":"2024-08-01T09:22:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Diminishing productivity and hyperstable harvest in northern Wisconsin walleye fisheries","docAbstract":"Managing fisheries in a changing socio-ecological environment may require holistic approaches for identifying and adapting to novel ecosystem dynamics. Using 32 years of Ceded Territory of Wisconsin (CTWI) walleye (Sander vitreus) data, we estimated production (P), biomass (B), biomass turnover (P/B), yield (Y), and yield over production (Y/P) and tested for hyperstability in walleye yield. Most CTWI walleye populations showed low P, and B, and Y/P < 1. Yet, production overharvest (Y/P > 1) was prevalent among Wisconsin walleye recruitment-based management approaches (natural recruitment [NR], sustained only by stocking, combination). Production, B, and P/B have declined in NR populations, while Y and Y/P have remained constant. Walleye Y was hyperstable along a production gradient among all management approaches and fishery types (i.e., angling only, angling/tribal harvest combined). Diminishing productivity and hyperstable yield may be jointly contributing to observed walleye declines. We classified lakes into management groups of low, moderate, or high vulnerability to harvest based on Y/P and P/B dynamics and recommend that exploitation may need to decline to maintain or increase the adaptive capacity of CTWI walleye.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0372","usgsCitation":"Mrnak, J.T., Embke, H.S., Wilkinson, M.V., Shaw, S.L., Vander Zanden, M.J., and Sass, G., 2024, Diminishing productivity and hyperstable harvest in northern Wisconsin walleye fisheries: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 12, p. 1650-1665, https://doi.org/10.1139/cjfas-2023-0372.","productDescription":"16 p.","startPage":"1650","endPage":"1665","ipdsId":"IP-160358","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":489877,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2023-0372","text":"Publisher Index Page"},{"id":432934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.18049419077052,\n 45.948938075630764\n ],\n [\n -88.62936625659313,\n 45.99443402697818\n ],\n [\n -90.13495797737319,\n 46.33769085403185\n ],\n [\n -90.45290902399807,\n 46.58248141670697\n ],\n [\n -90.71475106239458,\n 46.659555080264994\n ],\n [\n -90.27523049794291,\n 47.05602167993885\n ],\n [\n -90.41550301851264,\n 47.11969380652522\n ],\n [\n -91.04205361039043,\n 46.96037076403644\n ],\n [\n -91.63119819678276,\n 46.73651903458966\n ],\n [\n -91.94914924340715,\n 46.68522192077327\n ],\n [\n -92.0800702626054,\n 46.755742883136094\n ],\n [\n -92.20163978043216,\n 46.69805077000643\n ],\n [\n -92.27645179140251,\n 46.68522192077327\n ],\n [\n -92.29515479414547,\n 46.0917995100892\n ],\n [\n -92.71597235585415,\n 45.89038836410472\n ],\n [\n -92.86559637779489,\n 45.668642940969534\n ],\n [\n -92.87494787916634,\n 45.58362485690526\n ],\n [\n -92.70662085448265,\n 45.524690647636874\n ],\n [\n -92.65051184625477,\n 45.43945434374024\n ],\n [\n -92.77208136408203,\n 45.17637992800505\n ],\n [\n -90.04144296366039,\n 44.48667212247656\n ],\n [\n -88.06827617431473,\n 45.3869371369814\n ],\n [\n -88.18049419077052,\n 45.948938075630764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"81","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mrnak, Joseph T.","contributorId":275764,"corporation":false,"usgs":false,"family":"Mrnak","given":"Joseph","email":"","middleInitial":"T.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":910969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":910970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkinson, Max V.","contributorId":343401,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Max","email":"","middleInitial":"V.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shaw, Steph L.","contributorId":343404,"corporation":false,"usgs":false,"family":"Shaw","given":"Steph","email":"","middleInitial":"L.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vander Zanden, M. Jake","contributorId":265448,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"M.","email":"","middleInitial":"Jake","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":910973,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sass, Greg G.","contributorId":244466,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":910974,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257411,"text":"70257411 - 2024 - Disentangling genetic diversity of Myotis septentrionalis: population structure, demographic history, and effective population size","interactions":[],"lastModifiedDate":"2024-08-30T16:27:16.23961","indexId":"70257411","displayToPublicDate":"2024-08-01T09:19:36","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Disentangling genetic diversity of Myotis septentrionalis: population structure, demographic history, and effective population size","docAbstract":"Myotis septentrionalis (Northern Long-eared Bat) has recently suffered a >90% decline in population size in North America due to white-nose syndrome (WNS). We assessed genetic diversity, population structure, current effective population size, and demographic history of M. septentrionalis distributed across the United States to determine baseline levels pre-WNS. We analyzed RADseq data from 81 individuals from Kentucky, Louisiana, Michigan, Minnesota, North Carolina, Oklahoma, and Wisconsin. Additionally, we examined population genetic structure using discriminant analysis of principal components, fastStructure, and STRUCTURE. We then estimated effective population size and demographic history using fastsimcoal2. Similar levels of genetic diversity were found across all samples. We found no population genetic structure in the varied analyses from these contemporary samples. The best model for demographic history estimated a rapid population expansion followed by a slower expansion approximately 340,000 years ago. The vagility of M. septentrionalis, along with male dispersal and random mating, may provide a buffer against serious bottleneck effects stemming from rapid population declines due to WNS. This research provides a baseline for tracking and monitoring the influence of WNS on genetic diversity such as potential reduced diversity or increased population structuring in the future.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyae056","usgsCitation":"Grimshaw, J.R., Donner, D., Perry, R., Ford, W., Silvis, A., Garcia, C.J., Stevens, R.D., and Ray, D.A., 2024, Disentangling genetic diversity of Myotis septentrionalis: population structure, demographic history, and effective population size: Journal of Mammalogy, v. 105, no. 4, p. 854-864, https://doi.org/10.1093/jmammal/gyae056.","productDescription":"11 p.","startPage":"854","endPage":"864","ipdsId":"IP-144514","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439235,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyae056","text":"Publisher Index Page"},{"id":433382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky, Louisiana, Michigan, Minnesota, North Carolina, Oklahoma, 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The Mayflower Section is a mine influenced Superfund Site in Colorado. This investigation utilized electromagnetic induction (EM or EMI), magnetic, and fiber optic distributed temperature system (FODTS) geophysical methods to measure the bulk earth electrical conductivity, magnetic susceptibility, and temperature of specific surveyed volumes of the earth near the Mayflower Section of the Animas River. These physical parameters are used to understand the groundwater – surface water interactions, which can guide decision makers in their assessment of mine-impacted surface water. This report details the results from characterization and monitoring technologies to provide high data density and continuous monitoring of bulk earth electrical conductivity and temperature in the Mayflower section of the Animas River to identify zones of groundwater – surface water interactions and potential metal loading from mine influenced water. The investigation separated right and left bank characterization for each method and indicates more groundwater is entering from the right bank than the left bank and these predominantly right bank discharges potentially contain metal-rich water compared to the left bank. Results also indicate mineral veins facilitate preferential groundwater discharge to the river due to possible jointing, fractures, and permeability differences sometimes occurring along veins relative to host rock. For example, Boulder Gulch is likely groundwater dominated and may be receive water impacted by Mayflower Impoundments #1 and #2. Additionally, the beaver ponds near Blair Gulch may influence groundwater discharge and Mayflower Impoundment #4 is possibly impacting groundwater and surface water and may be connected to the wetlands to the west of the impoundment. These data could be further analyzed for smaller spatial scale analysis within areas of interest. The identification of these locations along sections of the river likely impacted by mine influenced groundwater potentially entering the Animas River and can be used by site investigators, decision makers, and stakeholders in mitigation decisions and strategies.","language":"English","publisher":"U.S. Environmental Protection Agency","collaboration":"U.S. Environmental Protection Agency","usgsCitation":"Werkema, D., Terry, N., and Trottier, B., 2024, Geophysical characterization of mine influenced groundwater and surface water in the Mayflower section of the Animas River, Bonita Peak Mining District, Silverton Colorado, 38 p.","productDescription":"38 p.","ipdsId":"IP-159820","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":480783,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://assessments.epa.gov/risk/document/&deid%3D362424"},{"id":481134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Animas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.6563034660742,\n 37.80974882626913\n ],\n [\n -107.58602809458,\n 37.80974882626913\n ],\n [\n -107.58602809458,\n 37.85248770149306\n ],\n [\n -107.6563034660742,\n 37.85248770149306\n ],\n [\n -107.6563034660742,\n 37.80974882626913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Werkema, Dale","contributorId":294506,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":924478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":924479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trottier, Brett 0000-0002-6148-0875","orcid":"https://orcid.org/0000-0002-6148-0875","contributorId":304452,"corporation":false,"usgs":false,"family":"Trottier","given":"Brett","affiliations":[{"id":66072,"text":"Pittsburgh, PA","active":true,"usgs":false}],"preferred":false,"id":924480,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256402,"text":"ofr20241049 - 2024 - Methods for computing water-quality concentrations and loads at sites operated by the U.S. Geological Survey Kansas Water Science Center","interactions":[],"lastModifiedDate":"2024-08-01T13:51:09.369775","indexId":"ofr20241049","displayToPublicDate":"2024-08-01T07:14:10","publicationYear":"2024","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":"2024-1049","displayTitle":"Methods for Computing Water-Quality Concentrations and Loads at Sites Operated by the U.S. Geological Survey Kansas Water Science Center","title":"Methods for computing water-quality concentrations and loads at sites operated by the U.S. Geological Survey Kansas Water Science Center","docAbstract":"The U.S. Geological Survey (USGS) Kansas Water Science Center (KSWSC) has published time-series computations of water-quality concentrations and loads based on in situ sensor data since 1995. Water-quality constituent concentrations or densities are computed using regression models that relate in situ sensor values to laboratory analyses of periodically collected samples. These regression models currently (2024) follow no uniform published guidance and are individually documented through USGS reports. This report describes updated (2024) procedures designed to improve the consistency, quality, and timeliness of computed continuous water-quality data produced by the USGS KSWSC. Beginning in 2024, models developed by the USGS KSWSC that follow specific procedures and requirements related to sample collection, model fit, and model documentation outlined in this report are planned to be published and stored in the USGS National Real-Time Water Quality Data for the Nation Data Service. This report also describes USGS KSWSC procedures for evaluating and publishing time-series water-quality computations after initial model development and documentation. 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241049","usgsCitation":"Stone, M.L., Lee, C.J., Rasmussen, T.J., Williams, T.J., Kramer, A.R., and Klager, B.J., 2024, Methods for computing water-quality concentrations and loads at sites operated by the U.S. Geological Survey Kansas Water Science Center: U.S. Geological Survey Open-File Report 2024–1049, 10 p., https://doi.org/10.3133/ofr20241049.","productDescription":"Report: iii, 10 p.; 2 Appendixes","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-160483","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":431715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1049/coverthb.jpg"},{"id":431716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1049/ofr20241049.pdf","text":"Report","size":"628 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1049"},{"id":431717,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1049/ofr20241049.XML"},{"id":431718,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1049/images/"},{"id":431720,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241049/full"},{"id":431719,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2024/1049/downloads/","text":"Appendixes 1 and 2"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
AimSpatiotemporal variation in resource availability is a strong driver of animal distributions. In the northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high-elevation songbirds.
Location
Northeastern United States.
Methods
We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in the tree mast. We also assessed population responses using systematic auditory surveys analysed with an open-population binomial mixture model. Further, we used modelled red squirrel abundance in nest-survival models to evaluate whether their abundance is correlated with the daily nest survival of three songbird species.
Results
The tree mast provided a critical resource pulse that resulted in a two-fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663–1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443–1545 m asl) after a large mast event. The daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels.
Main Conclusions
Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.
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2024 - Identifying transportation data and system needs for a Federal lands transportation data platform","interactions":[],"lastModifiedDate":"2024-08-01T13:46:37.3606","indexId":"ofr20241038","displayToPublicDate":"2024-07-31T13:10:00","publicationYear":"2024","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":"2024-1038","displayTitle":"Identifying Transportation Data and System Needs for a Federal Lands Transportation Data Platform","title":"Identifying transportation data and system needs for a Federal lands transportation data platform","docAbstract":"Modern transportation and land-use planning efforts include information from many sources to address topics such as safety, efficiency, commercial, and social needs. This wide breadth of topics provides opportunities for collaboration and development of common tools for diverse users. In many cases, different information systems provide the spatial data and geographic content necessary for transportation and land-use planners to consider multiple lines of evidence. The Federal Highway Administration Office of Federal Lands Highway (FLH) and Federal Land Management Agency partners use detailed spatial and quantitative data to inform transportation decisions. However, logistic challenges to data sharing exist because data are often managed by separate agencies; data-exchange frameworks and interagency data agreements are insufficient; and consistency from aggregated data requires maintenance, coordination, and supporting infrastructure.
The FLH and U.S. Geological Survey collaboratively examined (1) use and availability of spatial data for transportation planning and (2) a possible mechanism to use more shared and consistent data in a common planning environment. The goals of this collaborative effort were to describe data needs from the perspective of planners and to identify opportunities for shared data resources. Results presented here focus on two workshops and a subsequent investigation of data and tools available from partner agencies. The objectives of this report are to (1) describe information used in transportation planning with geographic data; (2) identify spatially explicit data that inform transportation plans and could be shared among all partners; and (3) describe current platforms, planning and administrative opportunities, and potential barriers to developing an integrated planning tool.
Key information and data needs were identified in three major classes: system, user, and influential factors. System data are parts of the transportation network and information about the condition of individual segments and the network. User data provide details about the function of the system and insights into potential needs; for example, user trips between source and destinations inform road and network demands that can lead to congestion and safety issues (in the future, user data might also include scenarios and projections based on land-use plans). Influential data represent social and environmental factors that influence transit demands and network conditions. These factors could be popular locations or seasonal events that influence demand and congestion; wildlife habitat or migration intersections that affect safety and management priorities; or geologic features that influence hazards, maintenance, and safety. Responses described here provide specific information for web-tool design and give a framework for interagency communication and cooperation to address specific information needs for integrated planning. Existing web-mapping and web-services, and the data that inform them, are also described. Commonly, these data are created and published by one agency, and the core users are outside of that agency; for example, threatened species distributions are published by the U.S. Fish and Wildlife Service for consideration by planners in advance of National Environmental Policy Act (42 U.S.C. 4321 et seq.) evaluation.
This report is provided to inform FLH leaders and Federal Land Management Agency partners by articulating user needs and requirements for integrated planning tool(s). Programmers creating a secure web-based data-sharing platform (with data-viewing, -analysis and -download functions) can use the information presented here to organize data and user interfaces. This integrated perspective can help FLH and Federal Land Management Agency partners develop transportation networks that better serve the needs of people in local communities and across States and the Nation.
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U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This paper presents a summary of previously published work (Celebi 2023) related to the new Self-Anchored Suspension (SAS) bridge that went into service within the last decade as a replacement for the older truss bridge spanning between Yerba Buena Island and Oakland, California, within the San Francisco Bay Area. During the October 19, 1989 M6.9 Loma Prieta earthquake, which occurred ~100 km south of the Bay Bridge, a section of the upper deck of the truss bridge fell onto the lower deck – thus closing this important lifeline between San Francisco and Oakland. The SAS is unique, self-anchored, and suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the decks. The SAS bridge is extensively instrumented by the California Geological Survey’s Strong Motion Instrumentation Program (CSMIP). There are approximately 85 channels of accelerometers in the seismic monitoring system that recorded the October 14, 2019 Mw4.6 Pleasant Hill earthquake. The data allow a complex but identifiable coupled response of the deck, tower, and cable system. Both acceleration and displacement time-history data are used to extract significant frequencies using system identification methods, including spectral analyses. Results are compared to those from finite-element-model (FEM) analyses carried out during the design and analysis process of the bridge in 2002 (Nader et al. 2002). There are differences between FEM analyses results and those from the low amplitude shaking caused by a seismic event. An apparent frequency (period) of the SAS bridge is assessed (approximately 5.2 seconds). In a plot of deck length versus period, there is an almost linear relationship with periods of other regular suspension bridges, such as the Golden Gate Bridge and the Carquinez Bridge, both in the San Francisco Bay.