The loss of tidal wetlands in the San Francisco Bay estuary have led to declines in native fish presence. Restoration of tidal wetlands in this area has intensified, with a primary goal of increasing the number of native fishes. We compared the presence of longfin smelt in naturally accreted and beneficial dredge reuse wetlands as a measure of successful restoration. We used environmental DNA (eDNA) analyses as our metric for fish presence and fish community composition, employing two different water sampling methods for comparison (standard and high-volume). Longfin smelt were present in multiple sites, but at numbers too low for accurate comparisons across sites. Community composition varied based on the water sampling method, but the presence/absence of longfin smelt was consistent across sampling methods. As this represents a pilot study, further refinement of methodology is necessary, but the use of high-volume water sampling methods is promising.
Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.
Explosive volcanic eruptions produce hazardous atmospheric plumes composed of tephra particles, hot gas and entrained air. Such eruptions are generally driven by magmatic fragmentation or steam expansion. However, an eruption mechanism outside this phreatic–magmatic spectrum was suggested by a sequence of 12 explosive eruptions in May 2018 at Kīlauea, Hawaii, that occurred during the early stages of caldera collapse and produced atmospheric plumes reaching 8 km above the vent. Here we use seismic inversions for reservoir pressure as a source condition for three-dimensional simulations of transient multiphase eruptive plume ascent through a conduit and stratified atmosphere. We compare the simulations with conduit ascent times inferred from seismic and infrasound data, and with plume heights from radar data. We find that the plumes are consistent with eruptions caused by a stomp-rocket mechanism involving the abrupt subsidence of reservoir roof rock that increased pressure in the underlying magma reservoir. In our model, the reservoir was overlain by a pocket of accumulated high-temperature magmatic gas and lithic debris, which were driven through a conduit approximately 600 m long to erupt particles at rates of around 3,000 m3 s−1. Our results reveal a distinct collapse-driven type of eruption and provide a framework for integrating diverse geophysical and atmospheric data with simulations to gain a better understanding of unsteady explosive eruptions.
Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer (Odocoileus virginianus) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110756","usgsCitation":"Bondo, K.J., Rosenberry, C., Stainbrook, D., and Walter, W., 2024, Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types: Ecological Modeling, v. 493, 110756, 16 p., https://doi.org/10.1016/j.ecolmodel.2024.110756.","productDescription":"110756, 16 p.","ipdsId":"IP-163658","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bondo, Kristin J.","contributorId":343150,"corporation":false,"usgs":false,"family":"Bondo","given":"Kristin","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":910646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Christopher S.","contributorId":343151,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stainbrook, David","contributorId":343152,"corporation":false,"usgs":false,"family":"Stainbrook","given":"David","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910649,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261997,"text":"70261997 - 2024 - Development and evaluation of public-supply community water service area boundaries for the conterminous United States","interactions":[],"lastModifiedDate":"2025-01-08T15:23:27.857215","indexId":"70261997","displayToPublicDate":"2024-05-26T09:18:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of public-supply community water service area boundaries for the conterminous United States","docAbstract":"The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.
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Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":922612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922613,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254929,"text":"70254929 - 2024 - Prion seeding activity in plant tissues detected by RT-QuIC","interactions":[],"lastModifiedDate":"2024-06-11T13:53:25.440832","indexId":"70254929","displayToPublicDate":"2024-05-26T08:48:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9113,"text":"Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Prion seeding activity in plant tissues detected by RT-QuIC","docAbstract":"Prion diseases such as scrapie, bovine spongiform encephalopathy (BSE), and chronic wasting disease (CWD) affect domesticated and wild herbivorous mammals. Animals afflicted with CWD, the transmissible spongiform encephalopathy of cervids (deer, elk, and moose), shed prions into the environment, where they may persist and remain infectious for years. These environmental prions may remain in soil, be transported in surface waters, or assimilated into plants. Environmental sampling is an emerging area of TSE research and can provide more information about prion fate and transport once shed by infected animals. In this study, we have developed the first published method for the extraction and detection of prions in plant tissue using the real-time quaking-induced conversion (RT-QuIC) assay. Incubation with a zwitterionic surfactant followed by precipitation with sodium phosphotungstate concentrates the prions within samples and allows for sensitive detection of prion seeding activity. Using this protocol, we demonstrate that prions can be detected within plant tissues and on plant surfaces using the RT-QuIC assay.
","language":"English","publisher":"MDPI","doi":"10.3390/pathogens13060452","usgsCitation":"Burgener, K., Lichtenberg, S.S., Walsh, D.P., Inzalaco, H., Lomax, A., and Pedersen, J., 2024, Prion seeding activity in plant tissues detected by RT-QuIC: Pathogens, v. 13, no. 6, 452, 12 p., https://doi.org/10.3390/pathogens13060452.","productDescription":"452, 12 p.","ipdsId":"IP-163951","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/pathogens13060452","text":"Publisher Index Page"},{"id":429868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Burgener, Kate","contributorId":338037,"corporation":false,"usgs":false,"family":"Burgener","given":"Kate","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lichtenberg, Stuart Siegfried","contributorId":338040,"corporation":false,"usgs":false,"family":"Lichtenberg","given":"Stuart","email":"","middleInitial":"Siegfried","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":902916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":902917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Inzalaco, Heather","contributorId":338043,"corporation":false,"usgs":false,"family":"Inzalaco","given":"Heather","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lomax, Aaron","contributorId":338045,"corporation":false,"usgs":false,"family":"Lomax","given":"Aaron","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pedersen, Joel","contributorId":338048,"corporation":false,"usgs":false,"family":"Pedersen","given":"Joel","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255784,"text":"70255784 - 2024 - Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","interactions":[],"lastModifiedDate":"2024-07-09T14:40:37.188664","indexId":"70255784","displayToPublicDate":"2024-05-26T06:43:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9121,"text":"Frontiers Earth Science Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","title":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","docAbstract":"The threat posed by emerging infectious diseases is a major concern for global public health, animal health and food security, and the role of birds in transmission is increasingly under scrutiny. Each year, millions of mass-reared game-farm birds are released into the wild, presenting a unique and a poorly understood risk to wild and susceptible bird populations, and to human health. In particular, the shedding of enteric pathogens through excrement into bodies of water at shared migratory stop-over sites, and breeding and wintering grounds, could facilitate multi-species long-distance pathogen dispersal and infection of high numbers of naive endemic birds annually. The Mallard (Anas platyrhynchos) is the most abundant of all duck species, migratory across much of its range, and an important game species for pen-rearing and release. Major recent population declines along the US Atlantic coast has been attributed to game-farm and wild mallard interbreeding and the introduction maladaptive traits into wild populations. However, pathogen transmission and zoonosis among game-farms Mallard may also impact these populations, as well as wildlife and human health. Here, we screened 16 game-farm Mallard from Wisconsin, United States, for enteric viral pathogens using metatranscriptomic data. Four families of viral pathogens were identified – Picobirnaviridae (Genogroup I), Caliciviridae (Duck Nacovirus), Picornaviridae (Duck Aalivirus) and Sedoreoviridae (Duck Rotavirus G). To our knowledge, this is the first report of Aalivirus in the Americas, and the first report of Calicivirus outside domestic chicken and turkey flocks in the United States. Our findings highlight the risk of viral pathogen spillover from peri-domestically reared game birds to naive wild bird populations.
Predator non-consumptive effects (NCE) can alter prey foraging time and habitat use, potentially reducing fitness. Prey can mitigate NCEs by increasing vigilance, chewing-vigilance synchronization, and spatiotemporal avoidance of predators. We quantified the relationship between Mexican wolf (Canis lupus baileyi) predation risk and elk (Cervus canadensis) behavior. We conducted behavioral observations on adult female elk and developed predation risk indices using GPS collar data from Mexican wolves, locations of elk killed by wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking. Metrics that quantified both spatial and temporal predation risk were the most predictive. Vigilance was positively associated with increased predation risk. The effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was lower, the probability of foraging increased while resting decreased in high-risk areas. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. Probability of multitasking depended on canopy openness and an interaction between maternal period and predation risk; multitasking decreased prior to parturition and increased post parturition in high-risk areas. Openness was inversely related to multitasking. These results suggest adult female elk are altering the type of vigilance used depending on resource availability/quality, current energetic needs, and predation risk. Our results highlight potentially important, but often-excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator–prey dynamics.
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-024-00620-2","usgsCitation":"Van Ee, J.J., Hagen, C., Pavlacky, D.C., Haukos, D.A., . Lawrence, A., Tanner, A.M., Grisham, B.A., Fricke, K., Liza G. Rossi, Beauprez, G., Kuklinski, K.E., Martin, R., Koslovsky, M.D., Rintz, T.B., and Hooten, M., 2024, Melded integrated population models: Journal of Agricultural, Biological and Environmental Statistics, v. 5, 31 p., https://doi.org/10.1007/s13253-024-00620-2.","productDescription":"31 p.","ipdsId":"IP-157570","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.41733481268557,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 38.10618949808156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Ee, Justin J.","contributorId":341159,"corporation":false,"usgs":false,"family":"Van Ee","given":"Justin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagen, Christian A.","contributorId":341160,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[],"preferred":false,"id":908024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavlacky, David C. Jr.","contributorId":341161,"corporation":false,"usgs":false,"family":"Pavlacky","given":"David","suffix":"Jr.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":908025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":". Lawrence, Andrew J","contributorId":341162,"corporation":false,"usgs":false,"family":". Lawrence","given":"Andrew J","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":908027,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tanner, Ashley M","contributorId":341163,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":908028,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grisham, Blake A.","contributorId":341164,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake","email":"","middleInitial":"A.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908029,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fricke, Kent A.","contributorId":341165,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent A.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":908030,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Liza G. Rossi","contributorId":341166,"corporation":false,"usgs":false,"family":"Liza G. Rossi","affiliations":[{"id":13724,"text":"Texas A&M University-Kingsville","active":true,"usgs":false}],"preferred":false,"id":908031,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Beauprez, Grant M.","contributorId":341167,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant M.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908032,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kuklinski, Kurt E.","contributorId":341168,"corporation":false,"usgs":false,"family":"Kuklinski","given":"Kurt","email":"","middleInitial":"E.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":908033,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Martin, Russell","contributorId":341169,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":908034,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Koslovsky, Matthew D.","contributorId":341170,"corporation":false,"usgs":false,"family":"Koslovsky","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":908035,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rintz, Troy B.","contributorId":341171,"corporation":false,"usgs":false,"family":"Rintz","given":"Troy","email":"","middleInitial":"B.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":908036,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hooten, Mevin B.","contributorId":341172,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin B.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":908037,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70254403,"text":"sir20235141 - 2024 - Effects of drought and cloud-water interception on groundwater recharge and wildfire hazard for recent and future climate conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi","interactions":[],"lastModifiedDate":"2026-01-30T19:52:42.185216","indexId":"sir20235141","displayToPublicDate":"2024-05-24T09:56:40","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5141","displayTitle":"Effects of Drought and Cloud-Water Interception on Groundwater Recharge and Wildfire Hazard for Recent and Future Climate Conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi","title":"Effects of drought and cloud-water interception on groundwater recharge and wildfire hazard for recent and future climate conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi","docAbstract":"The Water-budget Accounting for Tropical Regions Model (WATRMod) code was used for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi to estimate the spatial distribution of groundwater recharge, soil moisture, evapotranspiration, and climatic water deficit for a set of water-budget scenarios. The scenarios included historical and future drought conditions, and a land-cover condition where shrubland and forest within the cloud zone were converted to grassland. For the historical drought condition, island-wide mean annual recharge estimates range from a decrease of 30 percent (239 million gallons per day [Mgal/d]) for Kauaʻi to a decrease of 39 percent (2,706 Mgal/d) for the Island of Hawaiʻi, relative to the reference condition consisting of 1978–2007 rainfall and 2020 land cover. For the future drought condition, estimates of island-wide mean annual recharge range from a decrease of 40 percent (477 Mgal/d) on Maui to a decrease of 51 percent (116 Mgal/day) on Molokaʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition produces estimated land-cover-related decreases in island-wide mean annual recharge (in addition to the drought-related decreases) of 11–12 Mgal/d on Oʻahu, 119–135 Mgal/day on Maui, and 689–849 Mgal/d on the Island of Hawaiʻi. The spatial distributions of increases in conditions indicative of moisture stress and potential wildfire hazard were quantified using the relative frequency of soil moisture less than a selected threshold value (monthly mean soil moisture less than 0.074, expressed as a fraction of available water capacity), evapotranspiration less than a selected threshold value (monthly evapotranspiration less than 0.96 inches), and climatic water deficit greater than a selected threshold value (monthly climatic water deficit greater than 0.77, expressed as fraction of potential evapotranspiration). For the historical drought condition, the greatest increases in the relative frequency for the moisture-stress indicators occur across parts of east and southwest Kauaʻi; central, east, and west Oʻahu; central Molokaʻi; central Maui and low- to mid-altitude parts of West Maui volcano; and the northwestern and southern parts of the Island of Hawaiʻi. For the future drought condition, the greatest increases in the relative frequency of the moisture-stress indicators occur across parts of west Kauaʻi; central and west Oʻahu and Molokaʻi; a band of mid-altitude area on the southern slope of West Maui volcano and across the southwestern slope of Haleakalā; and mid-altitude areas of the northwestern and southern parts of the Island of Hawaiʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition results in land-cover-related increases in the relative frequency of moisture-stress indicators around Kaʻala in the Waiʻanae Range and the southeastern part of the Koʻolau Range on Oʻahu, the southern part of West Maui volcano and the southwestern slope of Haleakalā on Maui, and the upland regions of the western and southern parts of the Island of Hawaiʻi.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235141","collaboration":"Prepared in cooperation with the Pacific Islands Climate Adaptation Science Center","usgsCitation":"Mair, A., Oki, D.S., Kāne, H.L., Johnson, A.G., and Rotzoll, K., 2024, Effects of drought and cloud-water interception on groundwater recharge and wildfire hazard for recent and future climate conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2023–5141, 98 p., https://doi.org/10.3133/sir20235141","productDescription":"Report: viii, 98 p.; 2 Data Releases","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-139810","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":429177,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235130","text":"Scientific Investigations Report 2023-5130","linkHelpText":"- Estimated Groundwater Recharge for Mid-Century and End-of-Century Climate Projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i"},{"id":429174,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGHWS4","text":"USGS Data Release","description":"Mair, A., 2024, Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9HGHWS4.","linkHelpText":"Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions"},{"id":429173,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDP1C6","text":"USGS Data Release","description":"Mair, A., 2024, Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9DDP1C6.","linkHelpText":"Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions"},{"id":429175,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5141/sir20235141.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499403,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117007.htm","text":"Island of Hawaii","linkFileType":{"id":5,"text":"html"}},{"id":499402,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117006.htm","text":"Islands of Kauai, Oahu, Molokai, and 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.
","language":"English","publisher":"Copernicus","doi":"10.5194/os-20-661-2024","usgsCitation":"Yates, K., Fehr, Z., Johnson, S.A., and Zawada, D., 2024, Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA: Ocean Science, v. 20, no. 3, p. 661-688, https://doi.org/10.5194/os-20-661-2024.","productDescription":"28 p.","startPage":"661","endPage":"688","ipdsId":"IP-157460","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/os-20-661-2024","text":"Publisher Index Page"},{"id":429348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Looe Key Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.84282237828644,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.603571498742696\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":217808,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fehr, Zachery","contributorId":336991,"corporation":false,"usgs":false,"family":"Fehr","given":"Zachery","affiliations":[{"id":64427,"text":"Cherokee Nation System Solutions","active":true,"usgs":false}],"preferred":false,"id":901671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901673,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254427,"text":"sir20235130 - 2024 - Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","interactions":[],"lastModifiedDate":"2026-01-30T19:28:16.899695","indexId":"sir20235130","displayToPublicDate":"2024-05-24T09:48:23","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5130","displayTitle":"Estimated Groundwater Recharge for Mid-Century and End-of-Century Climate Projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","title":"Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","docAbstract":"Demand for freshwater in the State of Hawaiʻi is expected to increase by roughly 13 percent from 2020 to 2035. Groundwater availability in Hawaiʻi is affected by a number of factors, including land cover, rainfall, runoff, evapotranspiration, and climate change. To evaluate the availability of fresh groundwater under projected future-climate conditions, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of groundwater recharge for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, Maui, and the Island of Hawaiʻi for recent climate conditions and three future-climate scenarios. Climate conditions from 1978 to 2007 were used as the reference period for recent climate conditions on each island. The three future-climate scenarios were developed using available high-resolution downscaled climate projections that include (1) a mid-century scenario using projected rainfall conditions for the Representative Concentration Pathway (RCP) scenario during 2041–71 with a total radiative forcing of 8.5 watts per square meter by the year 2100 (RCP8.5 2041–71 scenario), (2) a dry-climate scenario using projected rainfall conditions for the RCP8.5 scenario during 2071–99, and (3) a wet-climate scenario using projected rainfall conditions for the “Special Report on Emissions Scenarios” A1B scenario during 2080–99 for Maui, the RCP4.5 scenario during 2080–99 for Kauaʻi, Lānaʻi, and the Island of Hawaiʻi, and the RCP8.5 scenario during 2080–99 for Oʻahu and Molokaʻi. An additional drought scenario was added for Lānaʻi to assess the effect of extreme drought conditions during 2008–12 on groundwater recharge. All scenarios used 2020 land cover.
Mean annual groundwater recharge is estimated to decrease between 5 and 55 percent on all six islands in this study for the mid-century and dry-climate scenarios relative to the reference-period recharge. Recharge is estimated to increase for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, and Maui between 2 and 43 percent and decrease for the Island of Hawaiʻi by about 4 percent for the wet-climate scenario. Comparing the mid-century and dry-climate scenarios, all 110 aquifer systems (management areas defined by the State of Hawaiʻi Commission on Water Resource Management) from all six islands show similar direction in drying (104 aquifer systems) or wetting (6 aquifer systems) changes for recharge. However, among the three future scenarios, only 35 of 110 aquifer systems show similar direction in drying (30 aquifer systems) or wetting (5 aquifer systems) changes for recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235130","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management and the Pacific Islands Climate Adaptation Science Center and in collaboration with Pūlama Lāna‘i","usgsCitation":"Kāne, H.L., Mair, A., Johnson, A.G., Rotzoll, K., Mifflin, J., and Oki, D.S., 2024, Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2023–5130, 133 p., https://doi.org/10.3133/sir20235130.","productDescription":"Report: viii, 133 p.; Data Release","numberOfPages":"133","onlineOnly":"Y","ipdsId":"IP-136143","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499393,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117004.htm","linkFileType":{"id":5,"text":"html"}},{"id":429217,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235141","text":"Scientific Investigations Report 2023-5141","linkHelpText":"- Effects of Drought and Cloud-Water Interception on Groundwater Recharge and Wildfire Risk for Recent and Future Climate Conditions, Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi"},{"id":429216,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5130/sir20235130.pdf","text":"Report","size":"33 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P972KMSL","text":"USGS Data Release","description":"Kāne, H.L., Mair, A., and Mifflin, J., 2024, Mean annual water-budget components for Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i, for a set of recent and future-climate conditions, 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Shriver, L.C., 2024, Spring 2024 edition: RAMPS Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-166294","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":430572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430571,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cms.usgs.gov/centers/southwest-biological-science-center/news/ramps-newsletter-spring-2024","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Laura Cecilia 0009-0008-5567-0868","orcid":"https://orcid.org/0009-0008-5567-0868","contributorId":334175,"corporation":false,"usgs":true,"family":"Shriver","given":"Laura","email":"","middleInitial":"Cecilia","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":905032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254662,"text":"70254662 - 2024 - Best practices for genetic and genomic data archiving","interactions":[],"lastModifiedDate":"2024-07-15T15:11:44.799023","indexId":"70254662","displayToPublicDate":"2024-05-24T09:33:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17794,"text":"Nature, Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Best practices for genetic and genomic data archiving","docAbstract":"Genetic and genomic data are collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, data are typically used only by the generating authors. The reuse of genetic and genomic datasets remains uncommon because it is difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible and FAIR (findable, accessible, interoperable and reusable) compliant, they could be reused for many additional purposes. We discuss the main challenges with existing genetic and genomic data archives, and suggest best practices for archiving genetic and genomic data. Recognizing that this is a longstanding issue due to little formal data management training within the fields of ecology and evolution, we highlight steps that research institutions and publishers could take to improve data archiving.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-024-02423-7","usgsCitation":"Leigh, D.M., Vandergast, A.G., Hunter, M., Crandall, E.D., Funk, W., Garroway, C., Hoban, S.M., Oyler-McCance, S.J., Rellstab, C., Segelbacher, G., Schmidt, C., Vazquez-Dominguez, E., and Paz-Vinas, I., 2024, Best practices for genetic and genomic data archiving: Nature, Ecology and Evolution, v. 8, p. 1224-1232, https://doi.org/10.1038/s41559-024-02423-7.","productDescription":"9 p.","startPage":"1224","endPage":"1232","ipdsId":"IP-157740","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467004,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-04593895","text":"External Repository"},{"id":429571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Leigh, Deborah M.","contributorId":291307,"corporation":false,"usgs":false,"family":"Leigh","given":"Deborah","email":"","middleInitial":"M.","affiliations":[{"id":62679,"text":"WSL Swiss Federal Research Institute","active":true,"usgs":false}],"preferred":false,"id":902175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":902176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":902177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crandall, Eric D. 0000-0001-8580-3651","orcid":"https://orcid.org/0000-0001-8580-3651","contributorId":337181,"corporation":false,"usgs":false,"family":"Crandall","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":902178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":902179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garroway, Colin J","contributorId":302145,"corporation":false,"usgs":false,"family":"Garroway","given":"Colin J","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":902180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":902181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":902182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rellstab, Christian 0000-0002-0221-5975","orcid":"https://orcid.org/0000-0002-0221-5975","contributorId":337184,"corporation":false,"usgs":false,"family":"Rellstab","given":"Christian","email":"","affiliations":[{"id":80990,"text":"Swiss Federal Research Institute, Switzerland","active":true,"usgs":false}],"preferred":false,"id":902183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":902184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schmidt, Chloe 0000-0003-2572-4200","orcid":"https://orcid.org/0000-0003-2572-4200","contributorId":337185,"corporation":false,"usgs":false,"family":"Schmidt","given":"Chloe","email":"","affiliations":[{"id":13699,"text":"German Centre for Integrative Biodiversity Research, Germany","active":true,"usgs":false}],"preferred":false,"id":902185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vazquez-Dominguez, Ella 0000-0001-6131-2014","orcid":"https://orcid.org/0000-0001-6131-2014","contributorId":337186,"corporation":false,"usgs":false,"family":"Vazquez-Dominguez","given":"Ella","email":"","affiliations":[{"id":80993,"text":"Universidad Nacional Autónoma de México, México","active":true,"usgs":false}],"preferred":false,"id":902186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":902187,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254539,"text":"70254539 - 2024 - Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","interactions":[],"lastModifiedDate":"2024-05-31T14:34:42.026925","indexId":"70254539","displayToPublicDate":"2024-05-24T09:27:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","docAbstract":"Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging.
We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment.
Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance.
We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales.
Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10980-024-01901-4","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D.S., Liu, R., Dumandan, P.K., Hojatimalekshah, A., Delparte, D.M., and Caughlin, T., 2024, Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands: Landscape Ecology, v. 39, 108, 16 p., https://doi.org/10.1007/s10980-024-01901-4.","productDescription":"108, 16 p.","ipdsId":"IP-157067","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":439502,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-01901-4","text":"Publisher Index Page"},{"id":429403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.01648249125195,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 44.15423652632305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":901795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":210334,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":901796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Rongsong","contributorId":43480,"corporation":false,"usgs":false,"family":"Liu","given":"Rongsong","email":"","affiliations":[],"preferred":false,"id":901797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dumandan, Patricia Kaye T. 0000-0003-3360-1534","orcid":"https://orcid.org/0000-0003-3360-1534","contributorId":257070,"corporation":false,"usgs":false,"family":"Dumandan","given":"Patricia","email":"","middleInitial":"Kaye T.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901798,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hojatimalekshah, Ahmad","contributorId":337022,"corporation":false,"usgs":false,"family":"Hojatimalekshah","given":"Ahmad","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901799,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delparte, Donna M. 0000-0002-9107-5117","orcid":"https://orcid.org/0000-0002-9107-5117","contributorId":317762,"corporation":false,"usgs":false,"family":"Delparte","given":"Donna","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":901800,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901801,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70257294,"text":"70257294 - 2024 - Reframing wildlife disease management problems with decision analysis","interactions":[],"lastModifiedDate":"2024-08-15T12:10:34.601257","indexId":"70257294","displayToPublicDate":"2024-05-24T07:09:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Reframing wildlife disease management problems with decision analysis","docAbstract":"Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources.
The 26 January 1700 CE Cascadia subduction zone earthquake ruptured much of the plate boundary and generated a tsunami that deposited sand in coastal marshes from northern California to Vancouver Island. Although the depositional record of tsunami inundation is extensive in some of these marshes, few sites have been investigated in enough detail to map the inland extent of sand deposition and depict variability in tsunami deposit thickness and grain size. We collected 129 cores in marshes of the Salmon River estuary in Oregon and reanalyzed 114 core logs from a 1987–88 study that mapped the inland extent of circa 1700 CE sandy tsunami deposits. The ca. 1700 CE tsunami deposit in the Salmon River estuary is easily recognized in cores ≤1 m deep in which a buried marsh peat is overlain by a well sorted sand bed with a sharp lower contact that thins and fines inland. We use tsunami deposit data and models of sandy tsunami sediment transport (using Delft3D-FLOW) to test 15 rupture models that could represent a ca. 1700 CE earthquake. At least 12–16 m of slip offshore of the Salmon River, which results in 0.8–1.0 m of coastal coseismic subsidence, is required to match the ca. 1700 CE sand deposit's inland extent, which is consistent with models of heterogeneous megathrust slip in ca. 1700 CE. Our methods of detailed tsunami deposit mapping, combined with sediment transport modeling, can be used to test models of megathrust ruptures and their tsunamis to potentially improve earthquake and tsunami hazard assessments.
Population in western Sarpy County, Nebraska, has steadily increased over the last several decades and has led to increased groundwater use for domestic purposes. To meet the increase in demand, the Papio-Missouri River Natural Resources District is seeking to use all available sources of groundwater in western Sarpy County. Additionally, elevated groundwater nitrate plus nitrite as nitrogen concentrations were detected, indicating the need to better understand the groundwater quality of the area. Although the general geology of the area is understood, the area does not have detailed information on the extent of the various aquifers, particularly the Dakota aquifer. To characterize these aquifers, the Papio-Missouri River Natural Resources District invested in airborne electromagnetic surveys of the area to better understand the subsurface geology. Although these surveys improved understanding of the groundwater systems in the area, the Papio-Missouri River Natural Resources District wanted to integrate the subsurface information with available water-quality and groundwater-level data.
In response, the U.S. Geological Survey, in cooperation with the Papio-Missouri River Natural Resources District, the Nebraska Natural Resources Commission, and the Nebraska Department of Natural Resources, assembled geologic, hydrogeologic and nitrate plus nitrite as nitrogen information for the selected area into a three-dimensional visualization computer software package called GeoScene3D. The completed GeoScene3D project was assembled to provide a visualization of the groundwater systems and associated water-quality results in Sarpy County and to provide the Papio-Missouri River Natural Resources District managers with information that can be used to make more informed groundwater resource-planning decisions in the future. This report details the development of a three-dimensional model created within GeoScene3D to visualize the subsurface, particularly the Dakota Sandstone in western Sarpy County.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The global decline of pollinators, particularly insects, underscores the importance of enhanced monitoring of their populations and habitats. However, monitoring some pollinator habitat is challenging due to widespread species distributions and shifts in habitat requirements through seasons and life stages. The monarch butterfly (Danaus plexippus), a migratory insect pollinator that breeds widely throughout North America, presents a unique case study for testing a sampling framework to overcome these challenges. Monarchs exhibit discrete resource needs across life stages (e.g., larval requirement for milkweed, adult requirement for floral nectar), utilizing many land use types across their extensive geographic range during breeding and migration seasons. The Integrated Monarch Monitoring Program (IMMP) uses a standardized protocol with a generalized random tessellation stratified (GRTS) sampling design to gather spatially balanced and ecologically representative information on monarch habitats within the United States. The IMMP is applicable to various land use types and habitats used by breeding monarchs and may be extended to sites outside of the GRTS design to collect data on non-random sites of interest, such as legacy or conservation sites. Additionally, the IMMP’s modular design and publicly available training allows for broad participation, including involvement from community scientists. Here, we summarize habitat metrics (milkweed and floral resources) across 1,233 sites covering much of the monarch’s breeding range. We examine variation in milkweed density and floral resource availability on probabilistic (random) and non-probabilistic (convenience) samples and among land use types (site types). Additionally, we assess resource availability within core geographies for monarch breeding and migration, specifically within the U.S. Fish and Wildlife Service’s Monarch Conservation Units (western, northern, and southern United States). Milkweed density, floral frequency, and floral richness were higher on non-random sites and in the North region. Among site types, milkweed density was highest on Rights-of-Way and Unclassified Grassland, while floral frequency was lowest on Rights-of-Way. The IMMP represents the first field-based habitat monitoring program of this scale for monarchs, yielding a robust dataset on monarchs and their habitats across their breeding range and offering a framework for surveying the habitat of insect species with diverse habitat requirements or widespread distributions.
The U.S. Geological Survey (USGS) Northern Rocky Mountain Science Center is based in Bozeman, Montana, and has field offices in Glacier National Park, Mont.; Missoula, Mont.; and Knoxville, Tennessee. Our scientists respond to the natural resource management needs of Federal, Tribal, and State partners—directly engaging in the coproduction and application of integrated, interdisciplinary science—and perform place-based research throughout the northern Rocky Mountains, including Yellowstone and Glacier National Parks and the northern Great Plains. However, the scope and implications of our research extend across the Nation. Our research themes are as follows: (1) climate change and drought, (2) species at risk, (3) habitat in changing landscapes, and (4) invasive species and wildlife disease. This Fact Sheet highlights examples of dynamic partnerships and key advances in our themes in fiscal year 2023 (October 2022–September 2023).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20243012","programNote":"Species Management Research Program, Land Management Research Program, Biological Threats and Invasive Species Research Program, Land Change Science Program, Climate Adaptation Science Centers, and Environmental Health Program","usgsCitation":"Wojtowicz, T., 2024, U.S. Geological Survey Northern Rocky Mountain Science Center science highlights for fiscal year 2023: U.S. Geological Survey Fact Sheet 2024–3012, 4 p., https://doi.org/10.3133/fs20243012.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-159158","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":429149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3012/fs20243012.pdf","text":"Report","size":"1.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3012"},{"id":429148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3012/coverthb.jpg"},{"id":429207,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3012/fs20243012.xml","linkHelpText":"https://pubs.usgs.gov/fs/2024/3013/images"},{"id":429209,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3012/images"},{"id":429384,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243012/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3012"}],"contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Early detection of biological threats, such as invasive species, increases the likelihood that control efforts will be successful and cost-effective. Environmental deoxyribonucleic acid (eDNA) sampling is an established method for the efficient and sensitive early detection of new biological threats. The Rapid eDNA Assessment and Deployment Initiative & Network (READI-Net) is a project designed with partners to provide a full suite of tools to maximize the power of eDNA sampling for detecting invasive species. The READI-Net suite of tools will include the availability of autonomous eDNA samplers, multispecies molecular DNA detection tools, strategic sample design, standardized and repeatable lab analysis, and a communication framework to deliver eDNA detection results to inform invasive species science, policy, and management. The READI-Net project is part of a national strategy to implement eDNA sampling for the early detection of and rapid response to biological threats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243013","programNote":"Biological Threats and Invasive Species Research Program","usgsCitation":"McKeon, L., and Wojtowicz, T., 2024, READI-Net—Providing tools for the early detection and management of aquatic invasive species: U.S. Geological Survey Fact Sheet 2024–3013, 2 p., https://doi.org/10.3133/fs20243013.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-159164","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":429178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3013/coverthb.jpg"},{"id":429179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3013/fs20243013.pdf","text":"Report","size":"960 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3013"},{"id":429210,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3013/images"},{"id":429211,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3013/fs20243013.xml"},{"id":429383,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3013"}],"contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Mauna Loa, the largest active volcano on Earth, has erupted 34 times since written descriptions became available in A.D. 1832. The most recent eruption of Mauna Loa occurred on November 27, 2022, after a 38 year hiatus; it lasted for 12 days. Some eruptions began with only brief seismic unrest, whereas others followed several months to a year of increased seismicity. Once underway, Mauna Loa’s eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. For example, lava flows that erupted from the Southwest Rift Zone in 1950 advanced at an average rate of 9.3 kilometers per hour (5.8 miles per hour); all three lobes reached the ocean within ~24 hours. Near the eruptive vents, the flows likely traveled even faster. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa has great volcanic-hazard potential for the Island of Hawai‘i. Volcanic hazards on Mauna Loa can be anticipated, and risk substantially mitigated, by documenting its past activity to refine our knowledge of the hazards, and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.
The map of the north and west flanks of Mauna Loa shows the distribution and relation of volcanic and surficial sedimentary deposits. It incorporates previously reported work published as generalized small-scale maps and a more detailed map.
Within the mapped area, lava has flowed from three different source regions: the Northeast Rift Zone (22 percent), the summit (64 percent), and radial vents (14 percent). All three have different points of origin which, in turn, affect the flow characteristics and periodicity of activity.
The map area includes the uppermost part of the NERZ and extends from the highest elevation––13,040 feet at the south end of the Kokoolau quadrangle, just below the summit caldera––to the sea northwest and west of the summit. Lava that erupts from the north and west flanks typically flows to the west, northwest, or north, depending on the vent location. Both morphologic lava flow types—‘a‘ā and pāhoehoe—are present. Pāhoehoe units tend to spread out or widen in low-slope regions, such as in the saddle regions between Mauna Loa and Mauna Kea or between Mauna Loa and Hualālai. In comparison, ʻaʻā flows generally produce narrower flow lobes that have higher relief.
This map is the fifth in a series of five maps that will cover Mauna Loa volcano.
NOTE: Map sheet 1 contains lines and type with overprint. This feature may be turned on or off in the Adobe Acrobat page display preferences.
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Algal blooms in water, soils, dusts, and the environment have captured national attention because of concerns associated with exposure to algal toxins for humans and animals. Algal blooms naturally occur in all surface-water types and are important primary producers for aquatic ecosystems. However, excessive algae growth can be associated with many harmful effects ranging from aesthetic to toxicity concerns, so this excessive growth is commonly called a harmful algal bloom (HAB).
Ecological imbalances that can lead to excessive algal growth, such as increased nutrient availability to waterbodies from natural and anthropogenic sources, are well documented in scientific literature. On the other hand, fundamental scientific understandings of environmental causes and controls leading to algal toxin production, environmental exposures, and adverse health outcomes for humans and animals could benefit from more attention by U.S. Geological Survey (USGS) scientists. Understanding when, why, and how the toxin is produced by individual algal cells or communities and why the toxin is released to the surrounding waterbody requires fundamental research to determine a toxin’s role, whether it provides competitive advantage or if other potential reasons exist for toxin production and release, such as secretions from otherwise benign biological processes. This research will require groundbreaking scientific discovery about underlying biologic and abiotic (non-living) processes commonly complicated by local variation in land use, microbial species composition, and ecosystem structure of the surrounding watershed.
Although underlying processes by which HABs form may be similar from one waterbody to another, individual waterbodies may be controlled by local factors for HAB development and toxin production that are unique to the watershed. Consequently, many fundamental science gaps exist that prevent informed mitigation and prevention of toxic HAB events. There are also gaps in understanding local conditions that control algal growth unique to specific watersheds. Addressing these science gaps is needed to inform evidence-based decisions that protect human and animal health and that reduce recreational and socioeconomic losses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1520","usgsCitation":"Christensen, V.G., Crawford, C.J., Dusek, R.J., Focazio, M.J., Fogarty, L.R., Graham, J.L., Journey, C.A., Lee, M.E., Larson, J.H., Stackpoole, S.M., Mazzei, V., Pindilli, E.J., Rattner, B.A., Slonecker, T., McSwain, K.B., Reilly, T.J., and Lopez, A.E., 2024, Interdisciplinary science approach for harmful algal blooms (HABs) and algal toxins—A strategic science vision for the U.S. Geological Survey: U.S. Geological Survey Circular 1520, 39 p., https://doi.org/10.3133/cir1520.","productDescription":"Report: vi; 39 p.; Appendix","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-144573","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":429145,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.XML","text":"XML","description":"CIR 1520"},{"id":429144,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1520","linkHelpText":"–Interdisciplinary Science Approach for Harmful Algal Blooms (HABs) and Algal Toxins—A Strategic Science Vision for the U.S. Geological Survey"},{"id":429143,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1520/images"},{"id":429142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1520/coverthb.jpg"},{"id":429203,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/circ/1520/cir1520_app1.pdf","text":"Appendix 1","size":"332 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":429146,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1520/full"}],"contact":"Director, Water Resources Mission Area
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Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range.
","language":"English","publisher":"Springer","doi":"10.1007/s00521-024-09897-3","usgsCitation":"Gonzalez-Afanador, I., Chen, C., Morales-Torres, G., Miehls, S.M., Shi, H., Tan, X., and Sepulveda, N., 2024, Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems: Neural Computing and Applications, v. 36, p. 16195-16212, https://doi.org/10.1007/s00521-024-09897-3.","productDescription":"18 p.","startPage":"16195","endPage":"16212","ipdsId":"IP-164744","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","noUsgsAuthors":false,"publicationDate":"2024-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez-Afanador, Ian","contributorId":354863,"corporation":false,"usgs":false,"family":"Gonzalez-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Claudia","contributorId":354864,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morales-Torres, Gerardo","contributorId":354865,"corporation":false,"usgs":false,"family":"Morales-Torres","given":"Gerardo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Hongyang 0000-0003-4135-3673","orcid":"https://orcid.org/0000-0003-4135-3673","contributorId":214760,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":936591,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936592,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}