Conserving migratory wildlife requires understanding how groups of individuals interact across seasons and landscapes. Telemetry reveals individual movements at large spatiotemporal scales; however, using movement data to define conservation units requires scaling up from individual movements to species- and community-level patterns. We developed a framework to define flyways and identify important sites from telemetry data and applied it to long-term, range-wide tracking data from three species (640 individuals) of sea ducks: namely, North American scoters (Melanitta spp). Our network of 88 nodes included both multispecies hotspots and areas uniquely important to individual species. We found limited spatial overlap between scoters wintering on the Atlantic and Pacific coasts of North America, with differing connectivity patterns between coasts. Finally, we identified four multispecies conservation units that did not correspond to traditional management flyways. From this approach, we show how individual movements can be used to quantify range-wide connectivity of migratory species and reveal gaps in conservation strategies.
Macroseismic observations and analysis connect our collective seismological past with the present and the present to the future by facilitating hazard estimates and communicating the effects of ground shaking to a wide variety of audiences across the ages. Invaluable ground shaking and building damage information is gained through standardized, systematic approaches for assigning intensities and, importantly, sharing and archiving those assignments in a reproducible form. The applications for these assignments are far reaching. Traditional macroseismic surveys provide vital constraints on critical aspects of earthquakes and their effects on society, whereas internet-based macroseismic datasets are extremely valuable for real-time earthquake situational awareness, and they contribute to later engineering loss and risk analyses. These important applications of macroseismic observations would be helped by revisiting traditional macroseismic surveys for modern environments, standardizing internet-based collection strategies, and ensuring compatibility between traditional and internet-based approaches of macroseismic data collection.
Even with best practices, we have identified several limitations with modern macroseismic data collection approaches, particularly from the U.S. Geological Survey's perspective. First, whereas crowdsourced, internet-based intensities such as “Did You Feel It?” are robust and definitive for lower intensities, they are poorly defined above intensity VII, where damage observations may require expert knowledge of each building’s structural system.
Second, in the United States, we use the Modified Mercalli Intensity (MMI) Scale, which is consistent with—yet inferior to—the more recently developed European Macroseismic Scale (EMS–98; Grünthal and others, 1998). Similarly, New Zealand uses the New Zealand MMI Scale (Dowrick and others, 2008), which lacks detail on how to assign intensities above MMI VIII. The EMS–98 fundamentally advanced the science of macroseismic intensity assignment by requiring quantitative assessments at each location through consistent application on statistical ranges of well-defined damage grades to building-specific vulnerability classes. Lastly, the United States and New Zealand no longer have professionals dedicated to conducting traditional macroseismic field surveys, so a strategy is needed for allowing postearthquake building inspectors and insurance loss assessors to contribute to intensity assignments.
The goals of our International Macroseismic Scale workshop were thus twofold. First, harmonize the MMI Scale with EMS–98 for the United States and New Zealand—which share several similar building types—by considering those structures and associated damage grades that are not well represented in the current EMS–98 building vulnerability class table. Second, begin to formalize the process of augmenting EMS–98 with new regional building classes and damage grades toward the development of a macroseismic scale that can be used globally, beyond the United States and New Zealand. Such an effort necessarily requires reviewing and expanding the original EMS–98 explanatory documents and consideration of any required revisions. We can build on the shoulders of giants in that a few of the original EMS–98 developers and experts participated in and were integral to our workshop. Their background and guidance were key in moving forward toward an international scale.
We agreed that additional building vulnerability classes, damage grades, and written and pictorial descriptions are necessary and ideally accompanied by a detailed paper trail for other nations to follow. If we can improve the macroseismic assignment process in both nations, we can also aim to refine the process of collecting postearthquake impact data, a boon to many engineering and financial concerns.
The benefits of a truly International Macroseismic Scale are considerable for both the engineering and seismology communities. A modern macroseismic scale requires more deliberate archival damage data collection, motivating more consistent and accessible postevent datasets that would have applications beyond the specific event. Applying field-collected building damage data toward macroseismic assignments would allow for increased coordination between engineering reconnaissance teams and local inspectors in collecting such data for official purposes. In addition, rapid and consistent intensity assignments globally would enable more accurate ShakeMaps—and thus improved earthquake engineering and geotechnical forensics, loss and risk estimates, and correlations between macroseismic intensity and ground motion parameters.
A brief summary of the Powell Center IMS workshop was published by Wald and others (2023) in the magazine Eos. This Open-File Report describes the workshop, its discussions, and its outcomes in detail. In summarizing the workshop, we have added important background material and reflections for proper context.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20231098","usgsCitation":"Wald, D.J., Goded, T., Hortacsu, A., and Loos, S.C., 2024, Developing and implementing an International Macroseismic Scale (IMS) for earthquake engineering, earthquake science, and rapid damage assessment: U.S. Geological Survey Open-File Report 2023–1098, 55 p., https://doi.org/10.3133/ofr20231098.","productDescription":"viii, 55 p.","onlineOnly":"Y","ipdsId":"IP-149203","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":424198,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1098/images"},{"id":424196,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1098/ofr20231098.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1098"},{"id":424195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1098/coverthb.jpg"},{"id":424207,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231098/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1098"},{"id":424199,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1098/ofr20231098.xml"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Hidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.
In this paper, we describe a general framework for modelling multi-state capture-recapture data via HMMs using both maximum likelihood and Bayesian methods. We then apply an HMM to invasive silver carp telemetry data from the Illinois River and compare the results estimated by both methods.
Our analysis demonstrates disadvantages of relying on a single approach and highlights insights obtained from implementing both methods together. While both methods often struggled to converge, our results show biologically informative priors for Bayesian methods and initial values for maximum likelihood methods can guide convergence toward realistic solutions. Incorporating prior knowledge of the system can successfully constrain estimation to biologically realistic movement and detection probabilities when dealing with sparse data.
Biologically unrealistic estimates may be a sign of poor model convergence. In contrast, consistent convergence behavior across approaches can increase the credibility of a model. Estimates of movement probabilities can strongly influence the predicted population dynamics of a system. Therefore, thoroughly assessing results from HMMs is important when evaluating potential management strategies, particularly for invasive species.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-023-00434-w","usgsCitation":"Labuzzetta, C.J., Coulter, A.A., and Erickson, R.A., 2024, Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River: Ecology Movement, v. 12, 2, 15 p., https://doi.org/10.1186/s40462-023-00434-w.","productDescription":"2, 15 p.","ipdsId":"IP-150787","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":440773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-023-00434-w","text":"Publisher Index Page"},{"id":426962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.0589176022094,\n 38.26712368948924\n ],\n [\n -87.45540197720929,\n 38.26712368948924\n ],\n [\n -87.45540197720929,\n 42.10926577184944\n ],\n [\n -91.0589176022094,\n 42.10926577184944\n ],\n [\n -91.0589176022094,\n 38.26712368948924\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Labuzzetta, Charles J. 0000-0002-6027-0120","orcid":"https://orcid.org/0000-0002-6027-0120","contributorId":332055,"corporation":false,"usgs":true,"family":"Labuzzetta","given":"Charles","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coulter, Alison A.","contributorId":90992,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false},{"id":26877,"text":"Southern Illinois University, Carbondale, IL","active":true,"usgs":false}],"preferred":false,"id":897154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897155,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251021,"text":"70251021 - 2024 - Widespread chemical dilution of streams continues as long-term effects of acidic deposition slowly reverse","interactions":[],"lastModifiedDate":"2024-01-18T12:44:48.545435","indexId":"70251021","displayToPublicDate":"2024-01-07T06:41:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Widespread chemical dilution of streams continues as long-term effects of acidic deposition slowly reverse","docAbstract":"Studies of recovery from acidic deposition have focused on reversal of acidification and its associated effects, but as recovery proceeds slowly, chemical dilution of surface waters is emerging as a key factor in the recovery process that has significant chemical and biological implications. This investigation uses long-term chemical records from 130 streams in the Adirondack region of New York, USA, to evaluate the role of ongoing decreases in conductance, an index of dilution, in the recovery of these streams. Stream chemistry data spanning up to 40 years (1980s–2022) showed that acid-neutralizing capacity has increased in 92% of randomly selected streams, but that harmful levels of acidification still occur in 37% of these streams. Conductance and Ca2+ concentrations decreased in 79% of streams, and SO42− concentrations in streams continued to show strong decreases but remained several times higher than concentrations in precipitation. These changes were ongoing through 2022 even though acidic deposition levels were approaching those estimated for pre-industrialization. Further dilution is continuing through ongoing decreases in stream SO42−. Nevertheless, Ca2+ continued to be leached from soils by SO42−, organic acids and NO3−, limiting the replenishment of available soil Ca2+, a prerequisite to stem further dilution of stream water.
The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.169881","usgsCitation":"Twomey, A., Nunez, K., Carr, J., Crooks, S., Friess, D., Glamore, W., Orr, M., Reef, R., Rogers, K., Waltham, N., and Lovelock, C.E., 2024, Planning hydrological restoration of coastal wetlands: Key model considerations and solutions: Science of the Total Environment, v. 915, 169881, 16 p., https://doi.org/10.1016/j.scitotenv.2024.169881.","productDescription":"169881, 16 p.","ipdsId":"IP-156710","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":440785,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.169881","text":"Publisher Index Page"},{"id":424276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"915","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twomey, Alice","contributorId":333063,"corporation":false,"usgs":false,"family":"Twomey","given":"Alice","email":"","affiliations":[{"id":13335,"text":"The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nunez, Karinna","contributorId":333064,"corporation":false,"usgs":false,"family":"Nunez","given":"Karinna","email":"","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":891827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crooks, Steve","contributorId":333065,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":891829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friess, Daniel A.","contributorId":35454,"corporation":false,"usgs":false,"family":"Friess","given":"Daniel A.","affiliations":[{"id":25407,"text":"Department of Geography, National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":891830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glamore, William","contributorId":333067,"corporation":false,"usgs":false,"family":"Glamore","given":"William","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":891831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Orr, Michelle","contributorId":197537,"corporation":false,"usgs":false,"family":"Orr","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":891832,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reef, Ruth","contributorId":298614,"corporation":false,"usgs":false,"family":"Reef","given":"Ruth","affiliations":[{"id":64623,"text":"Monash University, Australia","active":true,"usgs":false}],"preferred":false,"id":891833,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Kerrylee","contributorId":64151,"corporation":false,"usgs":false,"family":"Rogers","given":"Kerrylee","email":"","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":891834,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Waltham, Nathan","contributorId":333070,"corporation":false,"usgs":false,"family":"Waltham","given":"Nathan","email":"","affiliations":[{"id":40403,"text":"James Cook University","active":true,"usgs":false}],"preferred":false,"id":891835,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891836,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70250949,"text":"70250949 - 2024 - Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","interactions":[],"lastModifiedDate":"2024-01-13T15:15:09.210779","indexId":"70250949","displayToPublicDate":"2024-01-05T09:11:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","docAbstract":"Hawaiian honeycreepers, a group of endemic Hawaiian forest birds, are being threatened by avian malaria, a non-native disease that is driving honeycreepers populations to extinction. Avian malaria is caused by the parasite Plasmodium relictum, which is transmitted by the invasive mosquito Culex quinquefasciatus. Environmental and geographical factors play an important role in shaping mosquito-borne disease transmission dynamics through their influence on the distribution and abundance of mosquitoes. We assessed the effects of environmental (temperature, precipitation), geographic (site, elevation, distance to anthropogenic features), and trap type (CDC light trap, CDC gravid trap) factors on mosquito occurrence and abundance. Occurrence was analyzed using classification and regression tree models (CART) and generalized linear models (GLM); abundance (count data) was analyzed using generalized linear mixed models (GLMMs). Models predicted highest mosquito occurrence at mid-elevation sites and between July and November. Occurrence increased with temperature and precipitation up to 580 mm. For abundance, the best model was a zero-inflated negative-binomial model that indicated higher abundance of mosquitoes at mid-elevation sites and peak abundance between August and October. Estimation of occurrence and abundance as well as understanding the factors that influence them are key for mosquito control, which may reduce the risk of forest bird extinction.
Bird populations are declining globally. Wind and solar energy can reduce emissions of fossil fuels that drive anthropogenic climate change, yet renewable-energy production represents a potential threat to bird species. Surveys to assess potential effects at renewable-energy facilities are exclusively local, and the geographic extent encompassed by birds killed at these facilities is largely unknown, which creates challenges for minimizing and mitigating the population-level and cumulative effects of these fatalities. We performed geospatial analyses of stable hydrogen isotope data obtained from feathers of 871 individuals of 24 bird species found dead at solar- and wind-energy facilities in California (USA). Most species had individuals with a mix of origins, ranging from 23% to 98% nonlocal. Mean minimum distances to areas of likely origin for nonlocal individuals were as close as 97 to >1250 km, and these minimum distances were larger for species found at solar-energy facilities in deserts than at wind-energy facilities in grasslands (Cohen's d = 6.5). Fatalities were drawn from an estimated 30–100% of species’ desingated ranges, and this percentage was significantly smaller for species with large ranges found at wind facilities (Pearson's r = −0.67). Temporal patterns in the geographic origin of fatalities suggested that migratory movements and nonmigratory movements, such as dispersal and nomadism, influence exposure to fatality risk for these birds. Our results illustrate the power of using stable isotope data to assess the geographic extent of renewable-energy fatalities on birds. As the buildout of renewable-energy facilities continues, accurate assessment of the geographic footprint of wildlife fatalities can be used to inform compensatory mitigation for their population-level and cumulative effects.
Proterozoic rocks exposed in the southwestern U.S.A. represent one of the best examples of crustal growth by arc-related magmatism and accretionary orogenesis. Within the Southwest the 1.8–1.7 Ga Yavapai Province is widely regarded as a classic example of juvenile arc crust, however 1.8–2.5 Ga inherited zircon and Nd and Hf model ages have been recognized near Gunnison in central Colorado. These data have led to questions regarding the extent and nature of pre-1.8 Ga crustal material and the genesis of the Yavapai Province. We present evidence for a geochemically distinct, spatially restricted crustal block underlain by pre-1.8 Ga crust material (referred to here as the Gunnison block) in central to western Colorado within the Yavapai Province. The Gunnison block is characterized by 1.8–1.9 and 2.4–2.6 Ga inherited zircon, Pb isotopic systematics (μ = 9.8 ± 0.1, κ = 3.7 ± 0.1) elevated relative to 1.8 Ga depleted mantle values, 1.8–2.5 Ga Nd and Hf model ages, and a distinct pressure-temperature-time history. The geochemical data are consistent with mixing between juvenile 1.8 Ga and pre-1.8 Ga sources. The older crustal component is most similar to the isotopically enriched Mojave Province of eastern California and western Arizona, suggesting greater similarities between these provinces than previously recognized. Monazite and xenotime petrochronology indicate ca. 1.75–1.74, 1.72–1.69, 1.67, and 1.47–1.38 Ga tectono-metamorphic events. These data suggest that the Gunnison block accreted to other components of the Yavapai Province outboard of Laurentia at 1.75–1.74 Ga. The composite Yavapai Province was accreted to the margin of Laurentia during the 1.72–1.69 Ga Yavapai orogeny. Later overprinting is associated with the ∼1.68–1.60 Ga Mazatzal and ∼1.47–1.37 Ga Picuris orogenies. Identification of distinct crustal terranes within the Yavapai Province supports models involving multiple arcs and back-arcs that were progressively assembled prior to their accretion to Laurentia, perhaps akin to the present-day Banda Sea in Indonesia.
Accurate estimation and monitoring of wildlife population trends is foundational to evidence-based conservation. Here, we use hierarchical modelling to estimate population trends for six species of management interest (coyotes; red foxes, white-tailed deer, gray foxes; eastern wild turkey, and bobcats) while accounting for observation error from a long-term camera trap survey conducted across the State of New York. We were able to detect population level trends in occurrence and abundance and produce spatially explicit predictions for all six species using a combination of single-species occupancy models and Royle-Nichols models. Coyote (mean λ = 1.22, 95 % CI = 0.85–1.82) and red fox (mean λ = 1.17, 95 % CI = 0.95–1.46) populations were widely distributed with stable populations across the sampling period from 2014 to 2021. White-tailed deer populations were highly abundant and displayed an increasing population trend (mean λ = 1.85, 95 % CI = 1.54–2.10). Eastern wild turkey occupancy remained low across the state despite displaying a slight increase in occupancy over the sampling period (mean ψ = 0.16, 95 % CI = 0.07–0.25). Gray fox occupancy was also low (mean ψ = 0.22, 95 % CI = 0.12–0.29), consistent with growing concerns over the species across North America. Despite recent recoveries elsewhere, bobcat populations in New York State displayed very low occupancy (mean ψ = 0.07, 95 % CI = 0.02–0.12), highlighting the necessity of monitoring to inform conservation action. We provide empirically supported management implications for each species and demonstrate the efficacy of long-term camera trapping to provide robust evidence on population trends while accounting for imperfect detections, over scales meaningful to species management and conservation.
Paciorek and Wehner raise important questions around our use of the Mann-Kendall nonparametric trend test on smoothed data for analyzing long-term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p-values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends).
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School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona 85721","active":true,"usgs":false}],"preferred":false,"id":908529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dannenberg, Matthew P.","contributorId":239668,"corporation":false,"usgs":false,"family":"Dannenberg","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":47960,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ; Geographical and Sustainability Services, University of Iowa, Iowa City, IA","active":true,"usgs":false}],"preferred":false,"id":908530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yan, Dong","contributorId":207300,"corporation":false,"usgs":false,"family":"Yan","given":"Dong","email":"","affiliations":[{"id":37515,"text":"University of Arizona School of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":908532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":908533,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251052,"text":"70251052 - 2024 - Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","interactions":[],"lastModifiedDate":"2024-01-19T15:15:53.833192","indexId":"70251052","displayToPublicDate":"2024-01-04T09:15:25","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-002","title":"Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","docAbstract":"The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale.\n\nThe workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. \n\nConfidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM.\nLocal monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.","language":"English","publisher":"Chesapeake Bay Program STAC (Scientific and Technical Advisory Committee)","usgsCitation":"Berger, K., Filippino, K.C., Shenk, G.W., Goulet, N., Lookenbill, M., Moyer, D.L., Noe, G.E., Porter, A.J., Shallenberger, J., Thomas, B., and Yactayo, G., 2024, Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model: STAC Workshop Report 24-002, 35 p.","productDescription":"35 p.","ipdsId":"IP-160274","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":424622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424607,"rank":1,"type":{"id":15,"text":"Index 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Using data from two, 14-year demographic studies for a North American montane amphibian, Cascades frog (Rana cascadae), we quantified how aspects of current climate influenced annual survival of larvae and adult stages and modeled the stochastic population growth rate (λs) of each population for current (1980–2006) and future periods (2080s). Climate drivers of survival for the populations were similar for larvae (i.e., decreases in precipitation lead to pond drying and mortality), but diverged for terrestrial stages where decreases in winter length and summer precipitation had opposite effects. By the 2080s, we predict one population will be in sharp decline (λs = 0.90), while the other population will remain nearly stable (λs = 0.99) in the absence of other stressors, such as mortality due to disease. Our case study demonstrates a result counter to many climate envelope predictions in that stage-specific responses to local climate and hydrology result in a higher extinction risk for the more northern population.
National parks and other protected areas are important for preserving landscapes and biodiversity worldwide. An essential component of the mission of the United States (U.S.) National Park Service (NPS) requires understanding and maintaining accurate inventories of species on protected lands. We describe a new, national-scale synthesis of amphibian species occurrence in the NPS system. Many park units have a list of amphibian species observed within their borders compiled from various sources and available publicly through the NPSpecies platform. However, many of the observations in NPSpecies remain unverified and the lists are often outdated. We updated the amphibian dataset for each park unit by collating old and new park-level records and had them verified by regional experts. The new dataset contains occurrence records for 292 of the 424 NPS units and includes updated taxonomy, international and state conservation rankings, hyperlinks to a supporting reference for each record, specific notes, and related fields which can be used to better understand and manage amphibian biodiversity within a single park or group of parks.
Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (> 100 km2) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.
Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47–65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose–response to low (<30 μg/L) concentrations of Li.
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Science Center","active":true,"usgs":true}],"preferred":true,"id":891901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256598,"text":"70256598 - 2024 - Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","interactions":[],"lastModifiedDate":"2024-08-01T14:48:54.943118","indexId":"70256598","displayToPublicDate":"2024-01-02T09:45:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","docAbstract":"The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single‐station earthquake magnitudes using raw three‐component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (Mousavi and Beroza, 2020b), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P‐phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near‐real‐time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source‐station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic‐related hazards.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230171","usgsCitation":"Dybing, S., Yeck, W.L., Cole, H.M., and Melgar, D., 2024, Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale: Bulletin of the Seismological Society of America, v. 114, no. 3, p. 1523-1538, https://doi.org/10.1785/0120230171.","productDescription":"16 p.","startPage":"1523","endPage":"1538","ipdsId":"IP-158857","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":432030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, Sydney","contributorId":341314,"corporation":false,"usgs":false,"family":"Dybing","given":"Sydney","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":908223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Hank M. 0000-0003-1684-9116","orcid":"https://orcid.org/0000-0003-1684-9116","contributorId":335228,"corporation":false,"usgs":true,"family":"Cole","given":"Hank","email":"","middleInitial":"M.","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":908224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melgar, Diego","contributorId":341315,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908225,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250760,"text":"70250760 - 2024 - Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast","interactions":[],"lastModifiedDate":"2024-01-03T13:03:53.48821","indexId":"70250760","displayToPublicDate":"2024-01-02T07:01:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10942,"text":"PNAS Nexus","active":true,"publicationSubtype":{"id":10}},"title":"Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast","docAbstract":"Coastal communities are vulnerable to multihazards, which are exacerbated by land subsidence. On the US east coast, the high density of population and assets amplifies the region's exposure to coastal hazards. We utilized measurements of vertical land motion rates obtained from analysis of radar datasets to evaluate the subsidence-hazard exposure to population, assets, and infrastructure systems/facilities along the US east coast. Here, we show that 2,000 to 74,000 km2 land area, 1.2 to 14 million people, 476,000 to 6.3 million properties, and >50% of infrastructures in major cities such as New York, Baltimore, and Norfolk are exposed to subsidence rates between 1 and 2 mm per year. Additionally, our analysis indicates a notable trend: as subsidence rates increase, the extent of area exposed to these hazards correspondingly decreases. Our analysis has far-reaching implications for community and infrastructure resilience planning, emphasizing the need for a targeted approach in transitioning from reactive to proactive hazard mitigation strategies in the era of climate change.
","language":"English","publisher":"Proceedings of the National Academy of Sciences","doi":"10.1093/pnasnexus/pgad426","usgsCitation":"Ohenhen, L.O., Shirzaei, M., and Barnard, P.L., 2024, Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast: PNAS Nexus, v. 3, no. 1, pgad426, 14 p., https://doi.org/10.1093/pnasnexus/pgad426.","productDescription":"pgad426, 14 p.","ipdsId":"IP-144579","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440818,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/pnasnexus/pgad426","text":"Publisher Index Page"},{"id":424065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.68468831217973,\n 40.41367726741822\n ],\n [\n -69.60900471842967,\n 40.41367726741822\n ],\n [\n -69.60900471842967,\n 42.22869359582157\n ],\n [\n -74.68468831217973,\n 42.22869359582157\n ],\n [\n -74.68468831217973,\n 40.41367726741822\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Ohenhen, Leonard O.","contributorId":290168,"corporation":false,"usgs":false,"family":"Ohenhen","given":"Leonard","email":"","middleInitial":"O.","affiliations":[{"id":62367,"text":"Department of Earth Sciences, University of Delaware, Newark, DE, USA","active":true,"usgs":false}],"preferred":false,"id":891289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shirzaei, Manoochehr 0000-0003-0086-3722","orcid":"https://orcid.org/0000-0003-0086-3722","contributorId":245637,"corporation":false,"usgs":false,"family":"Shirzaei","given":"Manoochehr","email":"","affiliations":[{"id":49242,"text":"Dept. of Geosciences, Virginia Tech Univ.","active":true,"usgs":false}],"preferred":false,"id":891290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":891291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249904,"text":"70249904 - 2024 - Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series","interactions":[],"lastModifiedDate":"2024-04-01T17:30:03.617149","indexId":"70249904","displayToPublicDate":"2024-01-01T12:27:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series","docAbstract":"The U.S. Geological Survey (USGS) and NOAA’s National Integrated Drought Information System (NIDIS) conducted a series of four Listening Sessions in 2022 – each with a different application or topical focus – to seek input on priorities and needs related to predicting water availability changes under drought conditions at national and regional scales. This input was gathered to help inform the USGS Drought Program, regional and national drought efforts at NIDIS, and other national drought efforts. The series started with a February 2022 kick-off that introduced the series of Listening Sessions being held from March through September 2022. This kickoff also provided an overview of the USGS Drought Program’s work to characterize hydrological (e.g., streamflow and groundwater) drought, drought variability, drivers, and trends over the past century. Participants in these Listening Sessions included diverse stakeholder representation and perspectives.\n\nThe first of the four Listening Sessions focused on streamflow (March 3, 2022), and included a short introduction to the USGS national streamflow drought research, the properties of a national drought prediction system, as well as presentations by other agencies on different drought prediction and forecasting efforts. The second session focused on groundwater (May 5, 2022), and included presentations on groundwater drought, sustainable groundwater management, and improving our understanding of soil moisture, groundwater, and surface water drought. The third session focused on water use (July 14, 2022), and included a discussion of the different drought types, as well as an introduction to several key projects, including the USGS Upper Colorado River Basin Study, the Ogallala Data Directory project, and a multi-agency drought prediction partnership in Oklahoma. The fourth and final Listening Session focused on water availability prediction for ecosystems (September 8, 2022), and included presentations on the development of a national capacity for eco-hydrological and drought science, building climate resilience, and actionable ecodrought resources.","language":"English","publisher":"National Integrated Drought Information System","usgsCitation":"Skumanich, M., Smith, E., Lisonbee, J., and Hammond, J., 2024, Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series, 24 p.","productDescription":"24 p.","ipdsId":"IP-153596","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":422385,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.drought.gov/documents/drought-prediction-and-water-availability-report-2022-usgs-nidis-national-listening","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skumanich, Marina","contributorId":260137,"corporation":false,"usgs":false,"family":"Skumanich","given":"Marina","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":897766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Erik 0000-0001-8434-0798","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":221804,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lisonbee, Joel","contributorId":298624,"corporation":false,"usgs":false,"family":"Lisonbee","given":"Joel","email":"","affiliations":[{"id":64629,"text":"NOAA-NIDIS","active":true,"usgs":false}],"preferred":false,"id":897768,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887629,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259501,"text":"70259501 - 2024 - Snake River Fall Chinook Salmon research and monitoring","interactions":[],"lastModifiedDate":"2024-10-10T16:16:16.36293","indexId":"70259501","displayToPublicDate":"2024-01-01T10:59:34","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Snake River Fall Chinook Salmon research and monitoring","docAbstract":"In Chapter 1, we report on development and application of an integrated population model (IPM) for the natural-origin fall Chinook salmon population upstream of Lower Granite Dam. This year’s efforts represent the third update to the model. Initial efforts focused on generating juvenile and adult abundance estimates, with estimates of uncertainty, for informing the life-cycle model and estimating the effects of covariates on key demographic parameters. The goals of this year’s report are to 1) describe the modifications and advances made since the previous report, 2) to annually update and report the abundance estimates and other quantities used in the model, 3) to provide annual estimates of population parameters estimated by the IPM, and 4) to outline the next year’s tasks for advancing and/or applying the model.\n Since our last report on the life-cycle model, we have made a number of changes including: 1) incorporating jack abundance and age-structure data into the observation model, 2) changing smolt-to-adult survival (SAR) for subyearling and yearling to partial SARs that represent the joint probability surviving and entering the ocean at a given juvenile age, 3) combining age categories for rarely observed ages, 4) using scale data from unmarked fish to estimate age structure, and 5) generating composite life-cycle demographic parameters (cumulative capacity and productivity) from stage-specific parameters. We also generated juvenile abundance estimates, extended the model to include three additional brood years (1992– 2021), and ran the model to forecast returns to Lower Granite in 2022. \n For posterior medians of life stage-specific parameters, we estimated a mean productivity of 438 natural-origin juvenile recruits per female spawner, a capacity of 1.36 million juveniles, and a mean smolt-to-adult survival (SAR) of 1.2%. We detected strong density-dependent regulation, with juvenile recruits per spawner declining to about 50 juvenile recruits per female spawner at high spawner abundance. Across the entire life cycle, these stage-specific parameters resulted in a median cumulative intrinsic productivity of 1.93 adult female recruits per female spawner and a median equilibrium abundance of 2,851 female spawners (7,842 total spawners). Annual juvenile productivity varied from about 250–1,000 juveniles per spawner but displayed no temporal trends or patterns. For the three most recent brood years added to the model, recruits per spawner were higher than average but well within the range of uncertainty observed over the entire time series. In contrast to juvenile recruitment variability, SAR varied considerably among years and exhibited two periods of high survival (1996–2001 and 2007–2012) when SAR ranged from 2% to 6% and cumulative productivity ranged from 2 to 8 recruits per spawner. Partial SARs revealed that yearling outmigrants contributed substantially to the high SARs in the first high-survival period, but the second period was dominated by subyearlings. Yearlings contributed >30% to SAR in most years prior to 2007, and <30% since 2007.\n\nOur two-stage IPM provides a wealth of information about population dynamics affecting two key life-stage transitions (spawner to juvenile, and juvenile to spawner) centered on passage at Lower Granite Dam. By summarizing these stage-specific demographic parameters across the entire life cycle, this information will be useful for informing the recovery status of this threatened population. Whereas previous versions introduced hydrosystem and ocean covariates into the model, this phase of model development focused on solidifying the underlying model structure by introducing the concept of partial SARs and developing composite productivity and capacity as a function of underlying stage-specific parameters. Given this advancement, our next steps are to re-incorporate covariates into the model, specifically to understand how different factors affect partial SARs of subyearling and yearlings. Longer term model developments include:1) incorporating hatchery fish to explicitly estimate their survival as an alternative method for estimating natural-origin age composition, 2) expanding the model’s structure to include the three major spawning aggregates, 3) more explicitly modeling hydrosystem effects including transportation, and 4) using the model to assess retrospective and prospective management actions.\n\nIn 2022, the U.S. Geological Survey (USGS) focused adult salmon survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use used a boat-mounted underwater video camera to count 99 deepwater redds at 16 of the 29 sites surveyed. Redd depths averaged 4.4 m. In conjunction with the Idaho Power Company, we collected genetic samples from 318 live fall Chinook salmon (Oncorhynchus tshawytscha) and 19 carcasses at 40 unique geographic locations that spanned 91 river kilometers. Eighty fish were collected at three sites (High Range [rkm 332.3], Dug Bar [rkm 315.4], and Three Creek [rkm 384.0]), which accounted for 23% of all collected fish in 2022. Most (333 fish) post-spawned salmon were collected from early to mid-November just after the peak of spawning. A summary of 2021 PBT results produced by the Idaho Power Company can be found in Appendix A.2.\n\nBeach seining and PIT tagging of subyearling fall Chinook salmon was conducted in Snake and Salmon rivers to obtain information on population metrics and growth as well as to provide data for ongoing life-cycle modeling. In the Snake River, we collected 7,496 subyearlings, tagged 4,139, and recaptured 502 (12.1%). Using 8-mm tags in 45–49-mm fish allowed us to represent an additional 25% of the juvenile population through PIT tagging beyond just using standard 9- and 12-mm tags. In the Salmon River, we captured 206 natural subyearlings with the majority (52%) of fish being captured at two sites: rkm 20 and 26. We tagged 145 subyearlings and recaptured 9 fish. \n\nMany of the subyearlings we tagged in the Snake River were detected passing Lower Granite Dam, but only 4 fish tagged in the Salmon River were detected. In total we detected 484 (11.3%) tagged fish at Lower Granite Dam, and detection rates varied by tag size and passage route. More subyearlings were detected passing via the removable spill weir (RSW) earlier in the season while more fish were detected passing through the juvenile fish bypass system (JBS) earlier in the season while more fish were detected passing via the removable spill weir (RSW) later in the season. In general, fish tagged with 12-mm PIT tags had higher detection rates than fish tagged with smaller tags. Survival to Lower Granite Dam was low and ranged from 0.22 to 0.36. Season-wide, growth of subyearlings was higher in the lower reach than in the upper reach of the Snake River.","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Perry, R., Hance, D., Plumb, J., Tiffan, K.F., Bickford, B., Benson, S.L., Rhodes, T., Brink, S., and Alcorn, B., 2024, Snake River Fall Chinook Salmon research and monitoring, v, 110 p.","productDescription":"v, 110 p.","ipdsId":"IP-159991","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":462763,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org"},{"id":462792,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.73530070271516,\n 46.5793713644326\n ],\n [\n -117.73530070271516,\n 44.66424938201729\n ],\n [\n -115.87996433031464,\n 44.66424938201729\n ],\n [\n -115.87996433031464,\n 46.5793713644326\n ],\n [\n -117.73530070271516,\n 46.5793713644326\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915620,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220177,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plumb, John 0000-0003-4255-1612","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":223236,"corporation":false,"usgs":true,"family":"Plumb","given":"John","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bickford, Brad 0000-0003-3756-6588","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":220180,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915512,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benson, Scott Louis 0000-0003-0397-1200","orcid":"https://orcid.org/0000-0003-0397-1200","contributorId":303796,"corporation":false,"usgs":true,"family":"Benson","given":"Scott","email":"","middleInitial":"Louis","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915514,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rhodes, Tobyn 0000-0002-4023-4827","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":220181,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn","email":"","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915513,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brink, Steve","contributorId":222508,"corporation":false,"usgs":false,"family":"Brink","given":"Steve","email":"","affiliations":[],"preferred":false,"id":915516,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Alcorn, Brad","contributorId":345063,"corporation":false,"usgs":false,"family":"Alcorn","given":"Brad","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":915517,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257354,"text":"70257354 - 2024 - Recent applications of the USGS National Crustal Model for Seismic Hazard Studies","interactions":[],"lastModifiedDate":"2024-08-15T15:19:06.555598","indexId":"70257354","displayToPublicDate":"2024-01-01T10:06:04","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Recent applications of the USGS National Crustal Model for Seismic Hazard Studies","docAbstract":"The U.S. Geological Survey is developing the National Crustal Model (NCM) for seismic hazard studies to facilitate modeling site, path, and source components of seismic hazard across the conterminous United States. The NCM is composed of a 1km grid of geophysical profiles, extending from the Earth’s surface into the upper mantle. It is constructed from a threedimensional (3D) geologic framework and geophysical rules that use (1) a petrologic and mineral physics database; (2) a 3D temperature model; and (3) a calibrated rock type- and age-dependent porosity model. Parameters needed to estimate site response for existing ground motion models (GMMs), including the time-averaged velocity in the upper 30 meters (VS30), the depths to 1.0 and 2.5 km/s shear-wave velocity (Z1.0 and Z2.5), and sediment thickness, can be computed from the NCM. As GMMs continue to improve in the future, other metrics could also be extracted or derived from the NCM, such as fundamental period, site attenuation (ko), a fully frequency-dependent site response function, or 3D geophysical volumes for wavefield simulations. Application of the NCM may also benefit other aspects of seismic hazard analysis, including better accounting for path-dependent attenuation and geometric spreading, more accurate estimation of earthquake source properties such as hypocentral location and stress drop, and calculation of crustal strength profiles that inform estimates of the base of seismicity.
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Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":910091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":910090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":910086,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910088,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910089,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ahdi, Sean Kamran 0000-0003-0274-5180","orcid":"https://orcid.org/0000-0003-0274-5180","contributorId":332928,"corporation":false,"usgs":false,"family":"Ahdi","given":"Sean","email":"","middleInitial":"Kamran","affiliations":[],"preferred":false,"id":910087,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270799,"text":"70270799 - 2024 - Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries","interactions":[],"lastModifiedDate":"2025-08-28T14:31:44.183974","indexId":"70270799","displayToPublicDate":"2024-01-01T09:09:48","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":6053,"text":"Hawaii Cooperative Studies Unit Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCFRU-002","title":"Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries","docAbstract":"Noncommercial, shore-based fisheries provide economic, social, and cultural services to communities throughout the Hawaiian Islands. The State of Hawai‘i Department of Land and Natural Resources (DLNR), Division of Aquatic Resources (DAR) routinely conducts surveys to monitor noncommercial fisheries such that estimates of fishing effort and catch by gear type can be generated and used to implement more sustainable management practices. DAR executes both the Hawai‘i Marine Recreational Fishery Survey (HMRFS), a nationally standardized survey that focuses on intercepting fishers at access points (i.e., boat ramps) across the main Hawaiian Islands, and a set of roving creel surveys on O‘ahu, Maui Nui, and Kaua‘i that observe and intercept fishers at locations along the shoreline outside of those targeted by HMRFS. The latter set of creel surveys were designed to complement HMRFS by expanding its geographic coverage and thus providing a more representative picture of noncommercial fishing in Hawai‘i.
Sustainable management priorities set by DAR rely on the availability of statewide, fishery- dependent data. Thus, we collate information from island-based roving creel surveys into a cohesive Statewide Creel Survey Database. Further, we provide preliminary analyses and describe ways that surveys could be streamlined to improve future data collection, analysis, and utility. In so doing, we synthesize the most detailed information to-date about noncommercial shore-based fisheries of Hawai‘i. The unprecedented spatial and temporal coverage of DAR’s dataset reveals the value of their survey efforts over the last decade to address fishery management needs. Our primary objectives, results, and conclusions are summarized below:
1) Integrate DAR roving creel survey data from different islands into a single Statewide Creel Survey Dataset (Chapter II). We describe the collation of creel survey data from O‘ahu, Maui Nui, and Kaua‘i into a statewide dataset. We also offer ways in which these surveys could be streamlined to meet the needs of managers and decision makers. Briefly, these are to create a statewide strategic plan, standardize the execution of standard operating procedures, centralize the creel survey database and associated metadata, and consider using technology that improves the data pipeline, including transitioning from paper-based to electronic systems for data entry and processing.
2) Assess whether the new Statewide Creel Survey Dataset can provide inputs for length-based stock assessments (Chapter III). Only on Maui were interviews conducted with associated catch data. There was reasonably high taxonomic coverage (42 species from 186 interviews with 310 fishers), but low sample sizes for nearly all species precluded the development of length-based stock assessments. We provide summary statistics from the existing data and briefly discuss how technologies could be used to automate analysis of images of noncommercial catch.
3) Analyze the Statewide Creel Survey Dataset for spatial and temporal patterns in fishing effort (Chapter IV):
a. Visualizing noncommercial fishing pressure. We found that fishing effort (mean number of fishers observed per survey event at a site) on O'ahu was over three times greater than that recorded during similar surveys conducted on Maui or Kaua'i. We create maps that display the distribution of angling and spearfishing effort around each of the three islands.
b. Factors that predict fishing “hotspots” around Maui. Fishing effort on Maui was associated with areas with more wave power and less parking availability. There were half as many fishers in areas with parking lots than in areas with parking on the road shoulder only.
c. Changes in fishing effort during the COVID-19 pandemic. There was no change in fishing effort on O‘ahu during the first year of the pandemic, but there was a 20% decline in year 2 and a 33% decline in year 3, both in comparison to pre-pandemic levels. Pre-pandemic creel survey data were unavailable for Maui and Kaua‘i, but fishing effort on these islands also declined as the pandemic progressed at similar or greater rates than those observed on O‘ahu.
4) Quantify potential bias in survey methods by experimentally deriving fisher detection probabilities of shore-based and drone-based surveys (Chapter V):
a. Shore-based surveys. We conducted roving creel surveys for four months at three locations around Hilo Bay, designed to emulate and estimate the efficacy of DAR standard operating procedures. There was high agreement between paired observers in counting fishers, leading to near-perfect detection probabilities of both anglers (94%) and spearfishers (97%), but relatively low agreement and detection probabilities of other fishers (throw net, ‘opihi picking, etc.) (52%).
b. Drone-based surveys. We used an unmanned aerial vehicle (UAV; operated by DAR staff) to collect imagery of fishers along the Hilo Bay shoreline. We used still images and video clips (with known fishing activity) to build an online survey that was distributed to DAR and HCFRU personnel, asking them to count and categorize resource users as a snorkeler, spearfisher, angler, or other fisher. Only 40.0% of the responses correctly counted and categorized resource users in the image. Anglers were correctly identified and enumerated in 90.0% of the responses, but the correct response rates of the other three user categories ranged from 67.8% – 79.4%. Snorkelers and anglers tended to be undercounted while spearfishers and other fishers were overcounted.
5) Review the potential for incorporating emerging technologies that will improve, augment, and evolve creel survey data collection, especially for spearfishing (Chapter VI). Within the context of monitoring shore-based noncommercial fishing, we review the use of electronic data entry/processing systems with geospatial and image capabilities, field cameras, drones, smart buoys, citizen science apps, data mining social media, artificial intelligence and machine learning. We highlight several of the challenges and considerations when implementing these technologies into creel surveys and provide a synthesis of options that could be used to better estimate spearfishing.
The general conclusion of this assessment is that the DAR roving creel survey program is collecting valuable data that supplement the existing HMRFS efforts. However, there are a number of areas that could be improved to make these efforts a more effective tool for decision-making processes in resource management and conservation:
1) Establishment of clear statewide and island objectives for the Statewide Creel Survey Dataset. Currently, data collection efforts are focused towards addressing a very broad purpose – supplementing the HMRFS data collection efforts. However, the results of the preliminary analyses conducted as part of this project suggest that the data could be used to address other areas of need if these objectives were clearly defined. Further, the design of the creel survey would benefit from greater standardization of survey protocols between islands and an effort to define a) the acceptable margins of error associated with the estimates generated by these data and b) the minimum level of change that the surveys would need to detect to be useful to managers.
2) Centralization of data entry, data quality assessment, and data accessibility. Currently, each DAR office manages data entry, checks the data for errors, and is responsible for managing and storing the data. Instituting a centralized data entry system, particularly an online database that can receive survey data from tablets or smartphones running a standardized data collection application would improve efficiency, reduce data entry errors, and accelerate the availability of data to managers. A substantial amount of time and effort from the project described in this report was devoted to checking the dataset for errors. The development and application of data quality assurance protocols would ensure that the data are reliable and available in a timely fashion to support management decisions.
3) Address lingering questions regarding the efficacy of current survey protocols to capture and characterize the spearfishing component of the noncommercial fishery. The results presented in the report suggest that the current creel survey protocols do a good job detecting spearfishers when present but are not capturing sufficient data about their catch or total effort. There are also questions remaining as to whether the survey times and sites are sufficiently capturing the behavior of spearfishers in Hawai‘i. A more thorough assessment – whether through additional research, alteration of survey design, or review of data by representatives of the spearfishing community – would provide insight on how to use the Statewide Creel Survey Database to inform management of spearfishing.
4) Investigate the integration of technological advancements into the creel survey methods. As priorities and needs are developed and formalized, it would be valuable to consider how various technological advancements might enhance and streamline data collection or open new avenues of inquiry.
","language":"English","publisher":"University of Hawaii at Hilo","usgsCitation":"Raz, L., Grabowski, T.B., and Masse, R., 2024, Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries: Hawaii Cooperative Studies Unit Technical Report HCFRU-002, 97 p.","productDescription":"97 p.","ipdsId":"IP-161781","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":494692,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/43603"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -161.2893065228523,\n 22.923592862898644\n ],\n [\n -158.3533070196289,\n 20.112666118168917\n ],\n [\n -156.1174519354472,\n 18.13898162765402\n ],\n [\n -153.9729170144374,\n 18.083792239507275\n ],\n [\n -154.07629488187575,\n 19.923092631698466\n ],\n [\n -155.52426288320237,\n 22.28367051716448\n ],\n [\n -161.2893065228523,\n 22.923592862898644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Raz, Lillian Joy Tuttle 0000-0002-5009-8080","orcid":"https://orcid.org/0000-0002-5009-8080","contributorId":354940,"corporation":false,"usgs":true,"family":"Raz","given":"Lillian Joy Tuttle","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":947095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masse, Richard","contributorId":360482,"corporation":false,"usgs":false,"family":"Masse","given":"Richard","affiliations":[{"id":86013,"text":"University of Hawai‘i","active":true,"usgs":false}],"preferred":false,"id":947097,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257407,"text":"70257407 - 2024 - What can conservation culturomics tell us about factors driving public interest in aquatic endangered species","interactions":[],"lastModifiedDate":"2024-09-04T16:47:55.576903","indexId":"70257407","displayToPublicDate":"2024-01-01T08:57:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"What can conservation culturomics tell us about factors driving public interest in aquatic endangered species","docAbstract":"The field of “Conservation Culturomics” uses large datasets of freely available web-data to understand cultural patterns and public interests related to conservation topics. We used a popular culturomics tool based on search engine usage to investigate how the U.S. Endangered Species Act listing actions may influence public interest in imperiled freshwater taxa. Yet questions remain regarding the acceptable applications of these data leading us to also evaluate aspects of data quality such as repeatability of timeseries and spatial relative search volume (RSV) data for our search terms. We discovered that low search volume for many freshwater species restricted the number of species that could be analyzed and may signal low public awareness for these species. Low repeatability of timeseries relative search volume data suggests that greater scrutiny and quality control methods may be needed when analyzing these data. For species that had the highest data repeatability, there were positive associations of listing actions and relative search volume for threatened and endangered species. Anomalous peaks of search volume were sometimes but not always related to listing actions or online news stories. Spatial analysis indicated that search volumes were highest in states within species’ native ranges suggesting that people are most interested in species that occur near where they live.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110397","usgsCitation":"Moore, M.J., and Hyman, A.A., 2024, What can conservation culturomics tell us about factors driving public interest in aquatic endangered species: Biological Conservation, v. 289, 110397, 8 p., https://doi.org/10.1016/j.biocon.2023.110397.","productDescription":"110397, 8 p.","ipdsId":"IP-154898","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"289","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Michael J. 0000-0002-5495-7049","orcid":"https://orcid.org/0000-0002-5495-7049","contributorId":304258,"corporation":false,"usgs":true,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hyman, A. A","contributorId":342659,"corporation":false,"usgs":false,"family":"Hyman","given":"A.","email":"","middleInitial":"A","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":910264,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250999,"text":"70250999 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-02-02T14:59:59.031674","indexId":"70250999","displayToPublicDate":"2024-01-01T08:50:09","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin.
The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results.
Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE Fort Worth District, FEMA Region 6, NWS WGRFC","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 166 p.","productDescription":"166 p.","ipdsId":"IP-127610","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424561,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250996,"text":"70250996 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","interactions":[],"lastModifiedDate":"2024-02-02T14:47:45.280372","indexId":"70250996","displayToPublicDate":"2024-01-01T08:26:24","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","docAbstract":"Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgages and period of historical flow observations prior to the gage installation provides an informative means of estimating flood flow frequency. The U.S. Geological Survey contributed to the InFRM team’s efforts by performing the statistical analysis of the gaged record and authored this Appendix to the Lower Watershed Hydrology Assessment. Flood flow frequency is defined by values or quantiles of streamflow for selected annual exceedance probabilities (AEPs) (England and others, 2019). The annual peak streamflow data collected as part of the systematic operation of a streamgage provides the foundation for a detailed analysis of peak streamflow, but additional historical information pertaining to peak streamflows that predates the installation of a streamgage also can be used. An annual peak streamflow is defined as the maximum instantaneous streamflow for a streamgage for a given water year, and annual peak streamflow data for USGS streamgages can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2022). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of a multifaceted analysis, InFRM team members from the USGS analyzed annual peak streamflow records for the 45 USGS streamgages (gages) and 21 Lower Colorado River Authority (LCRA) streamgages (gages) in the lower Colorado River Basin listed in Table A.1 and Table A.8. The locations of USGS gages are also shown on Figure A.1, Figure A.2, and Figure A.3, and the locations of LCRA gages are shown in Section 1.4. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE-Fort Worth District, FEMA Region 6, NWS West Gulf River Forecast Center","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 246 p.","productDescription":"246 p.","ipdsId":"IP-133413","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424560,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}