Plants and animals undergo certain recurring life-cycle events, such as migrations between summer and winter habitats or the annual blooming of plants. Known as phenology, the timing of these events is very sensitive to changes in climate (and changes in one species’ phenology can impact entire food webs and ecosystems). Shifts in phenology have been described as a “fingerprint” of the temporal and spatial responses of wildlife to climate change impacts. Thus, phenology provides one of the strongest indicators of the adaptive capacity of organisms (or the ability of organisms to cope with future environmental conditions).
In this study, researchers are exploring how the timing and occurrence of a number of highly migratory marine animals is changing due to a series of climatic and ecological shifts. First, using existing long-term historical data series, they will determine the direction and magnitude of how migration, abundance, or other phenological factors have changed for marine mammals, sea turtles, and fishes that migrate into the Gulf of Maine on a seasonal basis. Because marine animals are inherently difficult to detect, the team will apply dynamic occupancy models to evaluate seasonal migration patterns and habitat use across multiple habitats in the Gulf of Maine region. The project team will also synthesize regional information on a key, ecologically-important prey fish, sandlance, whose timing and abundance is a strong predictor of the occurrence and behavior of predator species targeted in this study as well as a range of other regional fish and wildlife of conservation and management concern. Results from this component of the project will identify coastal fish and wildlife species that are relatively more or less able to adapt and thus potentially vulnerable to climate change; determine the likely primary drivers of those changes; and identify data gaps and future monitoring needs. Ultimately, this information will be available and useful for regional coastal management and adaptation decisions that will allow managers to effectively plan for the future.
In a second component of the project, researchers will focus specifically on changes in migration patterns of the endangered North Atlantic right whale. While shifts in the distribution and time of recurring life events are adaptive responses that may help species cope with climate impacts, they can also lead to changes in how species interact with humans. The North Atlantic right whale is one of the most endangered whale species on the planet. In the North Atlantic Ocean, ship strikes and entanglements with commercial fishing gear represent fatal threats to right whales. Recent reports suggest that North Atlantic right whale migration patterns have changed. Many researchers posit that shifts in migration are responsible for recent increases in the overlap between right whales and human activities, especially fishing. To help understand how changes in right whale movements and behaviors may overlap with ship traffic, and thus put the animals at risk of encountering vessels, we will combine right whale habitat models with ship traffic maps. The end result will be a set of maps identifying risk levels.
Mercury (Hg) is a naturally occurring element that bonds with organic matter and, when converted to methylmercury, is a potent neurotoxicant. Here we estimate potential future releases of Hg from thawing permafrost for low and high greenhouse gas emissions scenarios using a mechanistic model. By 2200, the high emissions scenario shows annual permafrost Hg emissions to the atmosphere comparable to current global anthropogenic emissions. By 2100, simulated Hg concentrations in the Yukon River increase by 14% for the low emissions scenario, but double for the high emissions scenario. Fish Hg concentrations do not exceed United States Environmental Protection Agency guidelines for the low emissions scenario by 2300, but for the high emissions scenario, fish in the Yukon River exceed EPA guidelines by 2050. Our results indicate minimal impacts to Hg concentrations in water and fish for the low emissions scenario and high impacts for the high emissions scenario.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-18398-5","usgsCitation":"Schaefer, K., Elshorbany, Y., Jafarov, E., Schuster, P.F., Striegl, R.G., Wickland, K.P., and Sunderland, E.M., 2020, Potential impacts of mercury released from thawing permafrost: Nature-Communications, v. 11, 4650, 6 p., https://doi.org/10.1038/s41467-020-18398-5.","productDescription":"4650, 6 p.","ipdsId":"IP-115389","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-18398-5","text":"Publisher Index Page"},{"id":378509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.564453125,\n 60.19615576604439\n ],\n [\n -158.466796875,\n 61.52269494598361\n ],\n [\n -153.544921875,\n 63.89873081524394\n ],\n [\n -142.294921875,\n 61.56457388515458\n ],\n [\n -139.658203125,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 60.1524422143808\n ],\n [\n -136.7578125,\n 61.52269494598361\n ],\n [\n -136.0986328125,\n 64.47279382008166\n ],\n [\n -145.3271484375,\n 67.90861918215302\n ],\n [\n -147.744140625,\n 67.97463396204759\n ],\n [\n -153.72070312499997,\n 67.45808150845772\n ],\n [\n -156.88476562499997,\n 66.7745857647255\n ],\n [\n -160.400390625,\n 64.92354174306496\n ],\n [\n -160.83984375,\n 63.64625919492172\n ],\n [\n -163.16894531249997,\n 62.85514553774182\n ],\n [\n -163.916015625,\n 63.37183226679281\n ],\n [\n -165.6298828125,\n 62.28836509824845\n ],\n [\n -163.564453125,\n 60.19615576604439\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Kevin 0000-0002-5444-9917","orcid":"https://orcid.org/0000-0002-5444-9917","contributorId":202096,"corporation":false,"usgs":false,"family":"Schaefer","given":"Kevin","email":"","affiliations":[{"id":36340,"text":"National Snow and National Snow and Ice Data Center, Cooperative Institute for Research, Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":799032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elshorbany, Yasin","contributorId":240870,"corporation":false,"usgs":false,"family":"Elshorbany","given":"Yasin","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":799033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jafarov, Elchin","contributorId":195182,"corporation":false,"usgs":false,"family":"Jafarov","given":"Elchin","affiliations":[],"preferred":false,"id":799034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schuster, Paul F. 0000-0002-8314-1372 pschuste@usgs.gov","orcid":"https://orcid.org/0000-0002-8314-1372","contributorId":1360,"corporation":false,"usgs":true,"family":"Schuster","given":"Paul","email":"pschuste@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":799036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799037,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":65376,"corporation":false,"usgs":true,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799090,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215541,"text":"70215541 - 2020 - Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","interactions":[],"lastModifiedDate":"2020-10-22T14:41:08.677038","indexId":"70215541","displayToPublicDate":"2020-09-16T09:36:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","docAbstract":"Intramolecular isotope distributions, including isotope clumping and position specific fractionation, can provide proxies for the formation temperature and formation and destruction pathways of molecules. In this study, we explore the position-specific hydrogen isotope distribution in propane. We analyzed propane samples from 10 different petroleum systems with high-resolution molecular mass spectrometry. Our results show that the hydrogen isotope fractionation between central and terminal positions of natural propanes ranges from −102‰ to +205‰, a much larger range than that expected for thermodynamic equilibrium at their source and reservoir temperatures (36–63‰). Based on these findings, we propose that the hydrogen isotope structure of catagenic propane is largely controlled by irreversible processes, expressing kinetic isotope effects (KIEs). Kinetic control on hydrogen isotope composition of the products of thermal cracking is supported by a hydrous pyrolysis experiment using the Woodford Shale as substrate, in which we observed isotopic disequilibrium in the early stage of pyrolysis. We make a more general prediction of KIE signatures associated with kerogen cracking by simulating this chemistry in a kinetic Monte Carlo model for different types of kerogens. In contrast, unconventional shale fluids or hot conventional reservoirs contain propane with an isotopic structure close to equilibrium, presumably reflecting internal and/or heterogeneous exchange during high temperature storage (ca. 100–150 °C). In relatively cold (<100 °C) conventional gas accumulations, propane can discharge from its source to a colder reservoir, rapidly enough to preserve disequilibrium signatures even if the source rock thermal maturity is high. These findings imply that long times at elevated temperatures are required to equilibrate the hydrogen isotopic structure of propane in natural gas host rocks and reservoirs. We further defined the kinetics of propane equilibration through hydrogen isotope exchange experiments under hydrous conditions; these experiments show that hydrogen in propane is exchangeable over laboratory timescales when exposed to clay minerals such as kaolinite. This implies rather rapid transfer of propane from sources to cold reservoirs in some of the conventional petroleum systems. Propane is also susceptible to microbial degradation in both oxic and anoxic environments. Biodegradation of propane in the Hadrian and Diana Hoover oil fields (Gulf of Mexico) results in strong increases in central-terminal hydrogen isotope fractionation. This reflects preferential attack on the central position, consistent with previous studies.
The ecosystems along the border between the United States and Mexico are at increasing risk to wildfire due to interactions among climate, land-use, and fuel loads. A wide range of fuel treatments have been implemented to mitigate wildfire and its threats to valued resources, yet we have little information about treatment effectiveness. To fill critical knowledge gaps, we reviewed wildfire risk and fuel treatment studies that were conducted near the US-Mexico border and published in the peer-reviewed literature between 1986 and 2019. The number of studies has grown during this time in warm desert to forest ecosystems on primarily federal lands. The most common study topics included fire effects on native species, the role of invasive species and woody encroachment on wildfire risk, historical fire regimes, and remote sensing and modeling to study wildfire risk across the landscape. A majority of fuel treatment studies focused on prescribed burns, and fuel treatments collectively had mixed effects on mitigating future wildfire risk and threats to ecosystems depending on vegetation and fire characteristics. The diversity of ecosystems and land ownership along the US-Mexico border present unique challenges for understanding and managing wildfire risk, and also create opportunities for collaboration and cross-site studies to promote knowledge across broad environmental gradients.
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The coastal Pacific Northwest USA hosts thousands of deep-seated landslides. Historic landslides have primarily been triggered by rainfall, but the region is also prone to large earthquakes on the 1100-km-long Cascadia Subduction Zone megathrust. Little is known about the number of landslides triggered by these earthquakes because the last magnitude 9 rupture occurred in 1700 CE. Here, we map 9938 deep-seated bedrock landslides in the Oregon Coast Range and use surface roughness dating to estimate that past earthquakes triggered fewer than half of the landslides in the past 1000 years. We find landslide frequency increases with mean annual precipitation but not with modeled peak ground acceleration or proximity to the megathrust. Our results agree with findings about other recent subduction zone earthquakes where relatively few deep-seated landslides were mapped and suggest that despite proximity to the megathrust, most deep-seated landslides in the Oregon Coast Range were triggered by rainfall.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.aba6790","usgsCitation":"LaHusen, S., Duvall, A.R., Booth, A.M., Grant, A.R., Mishkin, B.A., Montgomery, D., Struble, W., Roering, J., and Wartman, J., 2020, Rainfall triggers more deep-seated landslides than Cascadia earthquakes in the Oregon Coast Range, USA: Science Advances, v. 6, no. 38, eaba6790, 11 p., https://doi.org/10.1126/sciadv.aba6790.","productDescription":"eaba6790, 11 p.","ipdsId":"IP-114882","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aba6790","text":"Publisher Index Page"},{"id":378896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 42.05745022024682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean R 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":241904,"corporation":false,"usgs":false,"family":"LaHusen","given":"Sean R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duvall, Alison R 0000-0002-7760-7236","orcid":"https://orcid.org/0000-0002-7760-7236","contributorId":241905,"corporation":false,"usgs":false,"family":"Duvall","given":"Alison","email":"","middleInitial":"R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Adam M. 0000-0002-7339-0594","orcid":"https://orcid.org/0000-0002-7339-0594","contributorId":241907,"corporation":false,"usgs":false,"family":"Booth","given":"Adam","email":"","middleInitial":"M.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":800162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":800163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishkin, Benjamin A","contributorId":241909,"corporation":false,"usgs":false,"family":"Mishkin","given":"Benjamin","email":"","middleInitial":"A","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montgomery, David R.","contributorId":241911,"corporation":false,"usgs":false,"family":"Montgomery","given":"David R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":800166,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":800167,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wartman, Joseph 0000-0001-7659-7198","orcid":"https://orcid.org/0000-0001-7659-7198","contributorId":241918,"corporation":false,"usgs":false,"family":"Wartman","given":"Joseph","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800168,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216424,"text":"70216424 - 2020 - Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","interactions":[],"lastModifiedDate":"2020-11-17T13:53:10.508922","indexId":"70216424","displayToPublicDate":"2020-09-16T07:46:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","docAbstract":"Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.
Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to account for the effects of streamflow variability on travel time and compare our estimates to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15 min interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60–90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because of heterogeneous surficial geology, hydro-climatology, and vegetation and for applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.
Rapid ʻŌhiʻa Death (ROD) is a deadly disease that is threatening the native Hawaiian keystone tree species, ʻōhiʻa lehua (Metrosideros polymorpha Gaudich). Ambrosia beetles (Curculionidae: Scolytinae) and their frass are hypothesized to play a major role in the spread of ROD, although their ecological niches and frass production within trees and across the landscape are not well understood. We characterized the beetle communities and associated frass production from bolts (tree stem sections) representative of entire individual ʻōhiʻa trees from multiple locations across Hawaiʻi Island by rearing beetles and testing their frass for viable ROD-causing fungi. Additionally, we estimated frass production for three beetle species by weighing their frass over time. We found that Xyleborinus saxesenii (Ratzburg), Xyleborus affinis Eichhoff, Xyleborus ferrugineus (Fabricius), Xyleborus perforans (Wollaston), and Xyleborus simillimus Perkins were commonly found on ROD-infected ʻōhiʻa and each produced frass containing viable Ceratocystis propagules. The Hawaiʻi Island endemic beetle and the only native ambrosia beetle associated with ʻōhiʻa, X. simillimus, was limited to high elevations and appeared to utilize similar tree heights or niche dimensions as the invasive X. ferrugineus. Viable Ceratocystis propagules expelled in frass were found throughout entire tree bole sections as high as 13 m. Additionally, we found that X. ferrugineus produced over 4× more frass than X. simillimus. Our results indicate the ambrosia beetle community and their frass play an important role in the ROD pathosystem. This information may help with the development and implementation of management strategies to control the spread of the disease.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvaa108","usgsCitation":"Roy, K., Jaenecke, K., and Peck, R., 2020, Ambrosia beetle (Coleoptera: Curculionidae) communities and frass production in ʻŌhiʻa (Myrtales: Myrtaceae) infected with Ceratocystis (Microascales: Ceratocystidaceae) fungi responsible for Rapid ʻŌhiʻa Death: Environmental Entomology, v. 49, no. 6, p. 1345-1354, https://doi.org/10.1093/ee/nvaa108.","productDescription":"10 p.","startPage":"1345","endPage":"1354","ipdsId":"IP-119817","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":455315,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvaa108","text":"Publisher Index Page"},{"id":436789,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJKOO6","text":"USGS data release","linkHelpText":"Hawai'i Island Rapid 'Ohi'a Death Ambrosia Beetle Communities and Frass 2018-2019"},{"id":381607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hawai'i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.07177734375,\n 18.8335153964335\n ],\n [\n -154.75341796875,\n 18.8335153964335\n ],\n [\n -154.75341796875,\n 20.46818922264095\n ],\n [\n -156.07177734375,\n 20.46818922264095\n ],\n [\n -156.07177734375,\n 18.8335153964335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Roy, Kylle 0000-0002-7993-9031","orcid":"https://orcid.org/0000-0002-7993-9031","contributorId":213271,"corporation":false,"usgs":true,"family":"Roy","given":"Kylle","email":"","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":807196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaenecke, Kelly 0000-0002-7124-4788","orcid":"https://orcid.org/0000-0002-7124-4788","contributorId":211063,"corporation":false,"usgs":false,"family":"Jaenecke","given":"Kelly","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":807197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":807198,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213246,"text":"ofr20201107 - 2020 - Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California","interactions":[],"lastModifiedDate":"2020-09-17T14:06:01.343734","indexId":"ofr20201107","displayToPublicDate":"2020-09-16T06:43:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1107","displayTitle":"Distribution and Abundance of Aquila chrysaetos (Golden Eagles) in the East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan Area, California","title":"Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California","docAbstract":"The East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan (HCP/NCCP) Preserve System was designed to protect and enhance ecological diversity and function in eastern Contra Costa County, California. Aquila chrysaetos (golden eagle) is a special-status species expected to benefit from biological goals of the HCP/NCCP. As part of a broader study, we estimated site-occupancy, abundance, and reproduction of golden eagles in the HCP/NCCP inventory area in 2019. We completed 99 surveys and recorded a total of 50 detections of territorial pairs of eagles at 20 (67 percent) of 30 sites (13.9-square-kilometer [km2] plots). Detection probability of territorial pairs was highest in January and February (≥0.75) and lowest in mid-June to late July (<0.50). After correcting for imperfect detection, the expected probability of site-occupancy was 0.69 (standard error [SE] = 0.09), and mean expected abundance was 0.76 pairs per site (SE = 0.16), or 27.4 pairs per 500 km2. We found evidence of successful nesting (≥1 young fledged) for 3 (14 percent) of 22 pairs of eagles monitored in 2019. Our study design and baseline results should be useful for future monitoring and conservation of golden eagles in the HCP/NCCP area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201107","collaboration":"Prepared in cooperation with East Contra Costa County Habitat Conservancy Science and Research Grant Program, East Bay Regional Parks District, Save Mount Diablo’s Mary Bowerman Science and Research Grant Program, and NextEra Energy","usgsCitation":"Wiens, J.D., Kolar, P.S., and Bell, D.A., 2020, Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California: U.S. Geological Survey Open-File Report 2020-1107, 11 p., https://doi.org/10.3133/ofr20201107.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-119617","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":378434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1107/coverthb.jpg"},{"id":378435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1107/ofr20201107.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1107"}],"country":"United States","state":"California","county":"Contra Costa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75048828124999,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 37.37015718405753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Tsunami risk management requires strategies that can address multiple sources with different recurrence intervals, wave-arrival times, and inundation extents. Probabilistic tsunami hazard analysis (PTHA) provides a structured way to integrate multiple sources, including the uncertainties due to the natural variability and limited knowledge of sources. PTHA-based products relate to specific average return periods (ARP) and while there has been considerable attention paid to ARP choice for building codes, guidance on ARP choice to support evacuation planning and related land use is lacking. We use the State of California (USA) coastal communities as a case study to explore the use of geospatial analysis and pedestrian-evacuation modeling for comparing the societal implications of tsunamis based on evacuation areas that reflect inundation from 475-year, 975-year, and 2475-year ARPs. Results demonstrate that changes in PTHA ARP had a substantial effect on the number of tax-lot parcels in PTHA evacuation areas, but not on the primary land use of these parcels or which communities had the largest number of exposed parcels. Composite PTHA maps provided high-level insights on hazard exposure and identified dominant sources; however, disaggregated PTHA outputs that reflect single source parameters (e.g., wave-arrival time) were necessary to quantify evacuation potential from local and distant tsunamis. Framing changes in ARP assumption based on changes in the number, land-use type, and potential evacuation challenges of parcels in evacuation areas can provide valuable insight on the real-world implications of which ARP to use in land use or evacuation planning.
Spatially distributed populations often rely on large-scale processes for long-term population stability. These processes are driven by individuals moving across the landscape through long-distance dispersal movements. However, as landscapes are continually altered by anthropogenic development, increased fragmentation and avoidance behavior can affect landscape permeability and limit dispersal. Lesser prairie-chickens (Tympanuchus pallidicinctus) are a species of concern that have lost significant portions (>90%) of their historic distribution in the Southern Great Plains of the United States and are currently being impacted by continued anthropogenic development. Using GPS telemetry locations of 346 lesser prairie-chickens across their entire geographic distribution, we identified 184 different long-distance movements that drive population connectivity. We used empirical cumulative distribution functions to create a selection–avoidance–neutral curve and estimated the spatial scale of response to anthropogenic features (i.e., towers and windmills, large transmission and smaller distribution powerlines, oil wells, roads, and fences) during these movements. In addition, we tested for behavioral differences between movement types (e.g., exploratory loops vs. long-distance movements between home ranges) and for regional differences in response among study areas. We found that during long-distance movements, lesser prairie-chickens generally avoided all anthropogenic feature types we tested despite some variation in the reported response distance among study areas. However, they avoided the tallest features (i.e., towers and windmills and transmission powerlines) at much greater distances in comparison with the shorter features in our analysis. Our results show that long-distance movements are likely affected by responses to functional landscape fragmentation through increased development of anthropogenic features in important connectivity zones. As our estimated response distances during long-distance movements varied in comparison with previously reported response distances during other behavioral states (e.g., breeding or nesting), using long-distance or dispersal specific movement data may be more appropriate when asking questions related to connectivity across the landscape.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3202","usgsCitation":"Peterson, J.M., Earl, J.E., Fuhlendorf, S.D., Elmore, D., Haukos, D.A., Tanner, A.M., and Carleton, S., 2020, Estimating response distances of lesser prairie-chickens to anthropogenic features during long-distance movements: Ecosphere, v. 11, no. 9, e03202, 15 p., https://doi.org/10.1002/ecs2.3202.","productDescription":"e03202, 15 p.","ipdsId":"IP-101823","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3202","text":"Publisher Index Page"},{"id":388350,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.94140625,\n 32.32427558887655\n ],\n [\n -98.349609375,\n 32.32427558887655\n ],\n [\n -98.349609375,\n 40.245991504199026\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -104.94140625,\n 32.32427558887655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Jacob M.","contributorId":264585,"corporation":false,"usgs":false,"family":"Peterson","given":"Jacob","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earl, Julia E.","contributorId":264586,"corporation":false,"usgs":false,"family":"Earl","given":"Julia","email":"","middleInitial":"E.","affiliations":[{"id":54510,"text":"ltu","active":true,"usgs":false}],"preferred":false,"id":821704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuhlendorf, Samuel D.","contributorId":264587,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elmore, Dwayne","contributorId":264588,"corporation":false,"usgs":false,"family":"Elmore","given":"Dwayne","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":821702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tanner, Ashley M.","contributorId":264589,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carleton, Scott A.","contributorId":264590,"corporation":false,"usgs":false,"family":"Carleton","given":"Scott A.","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":821708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215144,"text":"70215144 - 2020 - Modeling the spatial dynamics of marsh ponds in New England salt marshes","interactions":[],"lastModifiedDate":"2020-10-08T12:34:21.986773","indexId":"70215144","displayToPublicDate":"2020-09-15T07:24:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the spatial dynamics of marsh ponds in New England salt marshes","docAbstract":"Ponds are common features on salt marshes, yet it is unclear how they affect large-scale marsh evolution. We developed a spatially explicit model that combines cellular automata for pond formation, expansion, and drainage, and partial differential equations for elevation dynamics. We use the mesotidal Barnstable marsh (MA, USA) as a case study, for which we measured pond expansion rate by remote sensing analysis over a 41-year time span. We estimated pond formation rate by comparing observed and modeled pond size distribution, and predicted pond deepening by comparing modeled and measured pond depth. The Barnstable marsh is currently in the pond recovery regime, i.e.,every pond revegetates and recovers the necessary elevation to support plant growth after re-connecting to the channel network. This pond dynamic creates an equivalent (i.e.,spatially and temporally averaged over the whole marsh) 0.5–2 mm/yr elevation loss that needs to be supplemented by excess vertical accretion. We explore how the pond regime would change with decreased sediment supply and increased relative sea-level rise (RSLR) rate, focusing on the case in which the vegetated marsh keeps pace with RSLR. When the RSLR rate remains below the minimum unvegetated deposition rate, the pond dynamics is nearly unaltered and ponds always occupy ~10% of the marsh area. However, when RSLR rate exceeds this threshold, the ponds in the marsh interior – which receive the least amount of suspended sediment – do not recover after drainage. These ponds transition to mudflats and permanently occupy up to 30% of the marsh area depending on RSLR rate. For marshes with a small tidal range, such as the microtidal Sage Lot Pond marsh on the opposite side of the peninsula from Barnstable marsh, high RSLR rates could bring every portion of the marsh into the pond runaway regime, with the whole marsh eventually converting into mudflats. In this regime, the existing marsh would disappear within centuries to millennia depending on the RSLR rate. Because of the spatial and temporal components of marsh evolution, a single RSLR threshold value applied across the entire marsh landscape provides a limited description of the marsh vulnerability to RSLR.
We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41597-020-00643-7","usgsCitation":"Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., and Grace, K., 2020, Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set: Scientific Data, v. 7, 303, 14 p., https://doi.org/10.1038/s41597-020-00643-7.","productDescription":"303, 14 p.","ipdsId":"IP-118171","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455325,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-020-00643-7","text":"Publisher Index Page"},{"id":421817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Verdin, Andrew","contributorId":145812,"corporation":false,"usgs":false,"family":"Verdin","given":"Andrew","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":885585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":885587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":885588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tuholske, Cascade","contributorId":330685,"corporation":false,"usgs":false,"family":"Tuholske","given":"Cascade","email":"","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":885589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grace, Kathryn","contributorId":145815,"corporation":false,"usgs":false,"family":"Grace","given":"Kathryn","email":"","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":885590,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215576,"text":"70215576 - 2020 - A manipulative thermal challenge protocol for adult salmonids in remote field settings","interactions":[],"lastModifiedDate":"2020-10-23T13:00:33.632469","indexId":"70215576","displayToPublicDate":"2020-09-14T07:54:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"A manipulative thermal challenge protocol for adult salmonids in remote field settings","docAbstract":"Manipulative experiments provide stronger evidence for identifying cause-and-effect relationships than correlative studies, but protocols for implementing temperature manipulations are lacking for large species in remote settings. We developed an experimental protocol for holding adult Chinook salmon (Oncorhynchus tshawytscha) and exposing them to elevated temperature treatments. The goal of the experimental protocol was to validate heat stress biomarkers by increasing river water temperature from ambient (~14°C) to a treatment temperature of 18°C or 21°C and then maintain the treatment temperature over 4 hours within a range of ±1.0°C. Our protocol resulted in a mean rate of temperature rise of 3.71°C h-1 (SD = 1.31) to treatment temperatures and mean holding temperatures of 18.0°C (SD = 0.2) and 21.0°C (SD = 0.2) in the low- and high-heat treatments, respectively. Our work demonstrated that manipulative experiments with large, mobile study species can be successfully developed in remote locations to examine thermal stress.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coaa074","usgsCitation":"Donnelly, D., von Biela, V.R., McCormick, S.D., Laske, S.M., Carey, M.P., Waters-Dynes, S.C., Bowen, L., Brown, R., Larson, S., and Zimmerman, C.E., 2020, A manipulative thermal challenge protocol for adult salmonids in remote field settings: Conservation Physiology, v. 1, no. 8, coaa074, 11 p., https://doi.org/10.1093/conphys/coaa074.","productDescription":"coaa074, 11 p.","ipdsId":"IP-111875","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa074","text":"Publisher Index Page"},{"id":379683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, Daniel S. 0000-0001-9456-885X","orcid":"https://orcid.org/0000-0001-9456-885X","contributorId":243180,"corporation":false,"usgs":false,"family":"Donnelly","given":"Daniel S.","affiliations":[{"id":48651,"text":"Formally USGS Alaska Science Center","active":true,"usgs":false}],"preferred":false,"id":802824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology 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Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":802827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":802828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown, Randy J","contributorId":243248,"corporation":false,"usgs":false,"family":"Brown","given":"Randy J","affiliations":[{"id":48666,"text":"USFWS, Fairbanks, Alaska","active":true,"usgs":false}],"preferred":false,"id":802831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Larson, Sean","contributorId":243250,"corporation":false,"usgs":false,"family":"Larson","given":"Sean","email":"","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":802832,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":802833,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70213199,"text":"70213199 - 2020 - Land-use change and future water demand in California’s central coast","interactions":[],"lastModifiedDate":"2020-09-15T12:17:33.92982","indexId":"70213199","displayToPublicDate":"2020-09-14T07:07:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Land-use change and future water demand in California’s central coast","docAbstract":"Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historical land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001–2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~222.7 Mm3 by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm3 by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping to better inform land and resource management decisions.
","language":"English","publisher":"MDPI","doi":"10.3390/land9090322","usgsCitation":"Wilson, T., Van Schmidt, N.D., and Langridge, R., 2020, Land-use change and future water demand in California’s central coast: Land, v. 9, no. 322, p. 322-343, https://doi.org/10.3390/land9090322.","productDescription":"21 p.","startPage":"322","endPage":"343","ipdsId":"IP-112033","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land9090322","text":"Publisher Index Page"},{"id":378385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Cruz, San Benito, Monterey, San Luis Obispo, & Santa Barbara","otherGeospatial":"central California coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.838623046875,\n 35.585851593232356\n ],\n [\n -121.06109619140625,\n 35.505400093441324\n ],\n [\n -120.89904785156251,\n 35.22094130403182\n ],\n [\n -120.66558837890626,\n 34.89043681762452\n ],\n [\n -120.63812255859375,\n 34.76417891445512\n ],\n [\n -120.58319091796874,\n 34.646766246519114\n ],\n [\n -120.22613525390624,\n 34.80252766591687\n ],\n [\n -120.0640869140625,\n 34.872411827691025\n ],\n [\n -120.838623046875,\n 35.585851593232356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"322","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":798599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":240648,"corporation":false,"usgs":false,"family":"Van Schmidt","given":"Nathan","middleInitial":"D.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langridge, Ruth 0000-0001-5320-8882","orcid":"https://orcid.org/0000-0001-5320-8882","contributorId":240649,"corporation":false,"usgs":false,"family":"Langridge","given":"Ruth","email":"","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212635,"text":"70212635 - 2020 - Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","interactions":[],"lastModifiedDate":"2023-11-08T16:13:16.263836","indexId":"70212635","displayToPublicDate":"2020-09-13T13:38:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","docAbstract":"High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was calculated from a correlation with SC. Explanatory variables for BRT models included well location and construction, surficial variables (e.g., soils and land use), and variables extracted from a groundwater‐flow model, including simulated groundwater ages. BRT model fits (r2) were 0.74 (SC and Cl) and 0.62 (TDS). BRT models provided spatially continuous salinity predictions across surficial and deeper aquifers where discrete water‐quality samples were missing. Uncertainty was smaller where salinity was lower, and models tended to underpredict in areas of highest salinity. Despite this, BRT models were able to capture areas of documented high salinity that exceed the TDS secondary maximum contaminant level for drinking water of 500 mg/L. Variables that served as surrogates for position along groundwater flowpaths were the most important predictors, indicating that much of the control on dissolved solids is related to rock‐water interaction as residence time increases. BRT models additionally support hypotheses of both surficial and deep sources of salinity.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12879","usgsCitation":"Knierim, K.J., Kingsbury, J.A., Haugh, C., and Ransom, K.M., 2020, Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States: Journal of American Water Resources Association, v. 56, no. 6, https://doi.org/10.1111/1752-1688.12879.","productDescription":"20 p.","startPage":"1029","ipdsId":"IP-111775","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":455333,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12879","text":"Publisher Index Page"},{"id":436791,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBFR1T","text":"USGS data release","linkHelpText":"Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment"},{"id":382516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.3408203125,\n 36.98500309285596\n ],\n [\n -90.52734374999999,\n 36.73888412439431\n ],\n [\n -92.3291015625,\n 34.66935854524543\n ],\n [\n -93.779296875,\n 32.32427558887655\n ],\n [\n -92.548828125,\n 31.240985378021307\n ],\n [\n -90.52734374999999,\n 32.509761735919426\n ],\n [\n -88.857421875,\n 32.10118973232094\n ],\n [\n -87.2314453125,\n 30.789036751261136\n ],\n [\n -86.923828125,\n 31.690781806136822\n ],\n [\n -87.275390625,\n 32.879587173066305\n ],\n [\n -88.9453125,\n 33.87041555094183\n ],\n [\n -89.2529296875,\n 35.17380831799959\n ],\n [\n -88.6376953125,\n 36.59788913307022\n ],\n [\n -88.76953125,\n 36.914764288955936\n ],\n [\n -89.3408203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"6","edition":"1010","noUsgsAuthors":false,"publicationDate":"2020-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haugh, Connor J. 0000-0002-5204-8271","orcid":"https://orcid.org/0000-0002-5204-8271","contributorId":219945,"corporation":false,"usgs":true,"family":"Haugh","given":"Connor J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797185,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214081,"text":"70214081 - 2020 - The roles of storminess and sea level rise in decadal barrier island evolution","interactions":[],"lastModifiedDate":"2020-09-22T15:15:12.947787","indexId":"70214081","displayToPublicDate":"2020-09-13T10:09:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The roles of storminess and sea level rise in decadal barrier island evolution","docAbstract":"Models of alongshore sediment transport during quiescent conditions, storm‐driven barrier island morphology, and poststorm dune recovery are integrated to assess decadal barrier island evolution under scenarios of increased sea levels and variability in storminess (intensity and frequency). Model results indicate barrier island response regimes of keeping pace, narrowing, flattening, deflation (narrowing and flattening), and aggradation. Under lower storminess scenarios, more areas of the island experienced narrowing due to collision. Under higher storminess scenarios, more areas experienced flattening due to overwash and inundation. Both increased sea levels and increased storminess resulted in breaching when the majority of the island was not keeping pace and deflation was the dominant regime due to increased overtopping. Under the highest storminess scenario, the island was unable to recover elevation after storms and drowned in just 10 years.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089370","usgsCitation":"Passeri, D., Dalyander, P., Long, J.W., Mickey, R.C., Jenkins, R., Thompson, D.M., Plant, N.G., Godsey, E., and Gonzalez, V., 2020, The roles of storminess and sea level rise in decadal barrier island evolution: Geophysical Research Letters, v. 47, no. 18, e2020GL089370, 8 p., https://doi.org/10.1029/2020GL089370.","productDescription":"e2020GL089370, 8 p.","ipdsId":"IP-121601","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":378665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35273742675781,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.207454473209072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"18","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":799390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":799391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Godsey, Elizabeth","contributorId":177095,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","affiliations":[],"preferred":false,"id":799396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gonzalez, Victor","contributorId":173702,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Victor","affiliations":[],"preferred":false,"id":799397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70218454,"text":"70218454 - 2020 - Hydro-climatic drought in the Delaware River Basin","interactions":[],"lastModifiedDate":"2021-02-26T13:54:09.110536","indexId":"70218454","displayToPublicDate":"2020-09-13T07:50:23","publicationYear":"2020","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":"Hydro-climatic drought in the Delaware River Basin","docAbstract":"The Delaware River Basin (DRB) supplies water to approximately 15 million people and is essential to agriculture and industry. In this study, a monthly water balance model is used to compute monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [R]) for the DRB for the 1901 through 2015 period. Water‐year R is used to identify drought periods in the basin and seven drought periods were identified. All but one of the drought periods occurred before about 1970; after this date, precipitation increased in the DRB and droughts were infrequent. The seven droughts were largely driven by precipitation deficits, rather than by unusually warm temperatures. For six of the seven droughts, the precipitation deficits were associated with atmospheric pressure patterns that resulted in northerly wind anomalies (i.e., conditions that deviate from the long‐term mean) over the basin that indicate an anomalous flow of dry air from the North American continent into the DRB. An examination of drought events estimated from a tree ring–based reconstruction of the Palmer Drought Severity Index for the 490 through 2005 time period indicates that although there were some DRB droughts that were longer and more severe during previous centuries, the DRB droughts during 1901 through 2015 were comparable in duration and severity to most drought events during previous centuries.
The Geomagnetism Program of the U.S. Geological Survey (USGS) monitors geomagnetic field variation through operation of a network of observatories across the United States and its territories, and it pursues scientific research needed to estimate and assess geomagnetic and geoelectric hazards. Over the next five years (2020–2024 inclusive) and in support of national and agency priorities, Geomagnetism Program research scientists plan to pursue an integrated set of research projects broadly encompassing empirical estimation and mapping of geomagnetic disturbance, modeling of solid-Earth conductivity structure and surface impedance, and mapping of magnetic-storm-induced geoelectric fields. Analyses are empirically based, relying on measured time series as well as statistical and numerical modeling of geomagnetic-monitoring data from ground-based observatories and surface-impedance tensors acquired during magnetotelluric surveys. The plan describes augmentation and development of the Geomagnetism Program's existing research portfolio, assuming present funding levels and staffing numbers. Because the projects are interdependent, they cannot be straightforwardly prioritized. They will all be pursued as resources and time permit; additional funding and staffing would enable the projects to be broadened and more rapidly completed. Where appropriate and subject to budgetary constraints and staffing numbers, research on specific projects might be accelerated or even judiciously expanded—some opportunities for expansion are discussed in this plan. Results will provide realistic illumination of the nature of the ground-level expression of space-weather disturbance, a subject of particular importance for projects focused on evaluating the vulnerability of electric-power-grid systems. This plan does not cover Geomagnetism Program operations, which are primarily concerned with the operation of magnetic observatories and, now, magnetotelluric surveys, although the context of such observatories and surveys is discussed. The research element of the program provides guidance for the expansion of program operations and research projects. In addition to the research projects summarized here, program scientists continue to provide leadership to the national and international geomagnetic, magnetotelluric, and space-weather communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1469","issn":"2330-5703","usgsCitation":"Love, J.J., Kelbert, A., Murphy, B.S., Rigler, E.J., and Lewis, K.A., 2020, Geomagnetism Program research plan, 2020–2024: U.S. Geological Survey Circular 1469, 19 p., https://doi.org/10.3133/cir1469.","productDescription":"viii, 19 p.","onlineOnly":"Y","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":378321,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1469/coverthb.jpg"},{"id":378322,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1469/circ1469.pdf","text":"Report","size":"14.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1469"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225
The ability to effectively manage game species for specific conservation objectives is often limited by the scientific understanding of their distribution and abundance. This is especially true in Hawai‘i where introduced game mammals are poorly studied and have low value relative to native species in other states. We modeled the habitat suitability and ecological associations of European mouflon sheep (“mouflon”; Ovis musimon) and axis deer (Axis axis) on the island of Lāna‘i using intensive aerial survey and environmental data that included climate, vegetation, and topographic variables. We conducted diagnostic tests on a suite of primarily categorical predictors and determined most were highly correlated. We therefore developed a suite of other spatial predictor layers with continuous variables. We tested several modeling approaches but settled on generalized linear models (GLM) and random GLMs because they could account for group size of animals and were based on curvilinear responses of each species to environmental variability. Both mammal species were habitat generalists showing little affinity to particular plant species or communities. Continuous predictors associated with plant productivity such as mean annual precipitation, normalized difference vegetation index (NDVI), and cloud cover were important explanatory factors in a GLM of axis deer and a random GLM of mouflon habitat suitability. The presence of axis deer was also an important explanatory predictor for mouflon distribution, but deer were not influenced by mouflon distribution, indicating asymmetrical competition. Consequently, mouflon were restricted to lower elevation arid and very dry slopes, whereas axis deer were more broadly distributed throughout other upland environments of the island, but avoided steep terrain. Findings indicate that removal of a substantial portion of the more abundant axis deer population may lead to an increase in abundance and distribution of mouflon without containment. Resulting spatial models of game mammal habitat suitability will be employed to inform land use prioritization analyses and to help resolve long-standing conflicts between native species conservation and sustained-yield hunting.
","language":"English","publisher":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i","usgsCitation":"Hess, S.C., Fortini, L., Leopold, C., Muise, J., and Sprague, J., 2020, Habitat suitability and ecological associations of two non-native ungulate species on the Hawaiian island of Lanai: Hawaii Cooperative Studies Unit Technical Report Series 91, iv, 30 p.","productDescription":"iv, 30 p.","ipdsId":"IP-113538","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":378620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378602,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5383"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Lāna‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.79962158203125,\n 20.824159066298787\n ],\n [\n -156.8909454345703,\n 20.917189979347988\n ],\n [\n -156.9891357421875,\n 20.931941310423674\n ],\n [\n -157.060546875,\n 20.913982976117605\n ],\n [\n -157.06329345703125,\n 20.88383379386135\n ],\n [\n -157.0323944091797,\n 20.85624519604873\n ],\n [\n -157.0001220703125,\n 20.834427371957577\n ],\n [\n -156.9891357421875,\n 20.812606385754087\n ],\n [\n -156.99462890624997,\n 20.78564668820214\n ],\n [\n -156.9843292236328,\n 20.756113874762082\n ],\n [\n -156.9609832763672,\n 20.72400644605942\n ],\n [\n -156.88201904296875,\n 20.73877670943921\n ],\n [\n -156.8305206298828,\n 20.75868217465891\n ],\n [\n -156.80374145507812,\n 20.804904106750566\n ],\n [\n -156.79962158203125,\n 20.821591880501483\n ],\n [\n -156.79962158203125,\n 20.824159066298787\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hess, Steve C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":150366,"corporation":false,"usgs":true,"family":"Hess","given":"Steve","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":799277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":799278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Christina 0000-0003-0499-3196","orcid":"https://orcid.org/0000-0003-0499-3196","contributorId":178961,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","affiliations":[],"preferred":false,"id":799279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muise, Jacob","contributorId":240997,"corporation":false,"usgs":false,"family":"Muise","given":"Jacob","email":"","affiliations":[{"id":48185,"text":"KIA Hawaii","active":true,"usgs":false}],"preferred":false,"id":799280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprague, Jonathan","contributorId":240998,"corporation":false,"usgs":false,"family":"Sprague","given":"Jonathan","email":"","affiliations":[{"id":48186,"text":"Pulama Lana‘i","active":true,"usgs":false}],"preferred":false,"id":799281,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70214963,"text":"70214963 - 2020 - Testing a new passive acoustic recording unit to monitor wolves","interactions":[],"lastModifiedDate":"2020-10-05T11:53:58.708804","indexId":"70214963","displayToPublicDate":"2020-09-11T09:48:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Testing a new passive acoustic recording unit to monitor wolves","docAbstract":"As part of a broader trial of noninvasive methods to research wild wolves (Canis lupus) in Minnesota, USA, we explored whether wolves could be remotely monitored using a new, inexpensive, remotely deployable, noninvasive, passive acoustic recording device, the AudioMoth. We tested the efficacy of AudioMoths in detecting wolf howls and factors influencing detection by placing them at set distances from a captive wolf pack and compared those recordings with real‐time, on‐site howling data between 22 May and 17 June 2019. We identified 1,531 vocalizations grouped into 428 vocal events (236 solo howl series and 192 chorus howls). The on‐site AudioMoth correctly recorded 100% of chorus and solo howls that were also documented in real‐time. The remote array detected 49.5% of chorus and 11.9% of solo howls (≥1 unit detected the event). The closest remote AudioMoth (0.54 km, 0.33 mi) detected 37% of choruses and 8.9% of solo howls. Chorus howls (9.4%) were detected at the farthest unit (3.2 km, 2.0 mi). Favorable wind (carrying source howls to the remote units) and calm (no wind) conditions increased detectability and detection distance of chorus howls. Temperature was inversely related to detection. Given the detection distances we observed, AudioMoths are probably useful in studying specific sites during periods when wolves move less frequently (e.g., during late spring and summer at homesites or potentially during winter at kill sites of very large prey). AudioMoths would also be useful in a passive sampling array (e.g., occupancy studies), especially when used in concert with other methods such as camera‐trapping. Additional research should be conducted in areas with different environmental variables (e.g., wind, temperature, habitat, topography) to determine performance under varying conditions and also when fitted with a parabolic dish.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.1117","usgsCitation":"Barber-Meyer, S., Palacios, V., Marti‐Domken, B., and Schmidt, L., 2020, Testing a new passive acoustic recording unit to monitor wolves: Wildlife Society Bulletin, v. 44, no. 3, p. 590-598, https://doi.org/10.1002/wsb.1117.","productDescription":"9 p.","startPage":"590","endPage":"598","ipdsId":"IP-115563","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":379016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.10937499999999,\n 46.74362499884437\n ],\n [\n -91.42822265625,\n 47.12995075666307\n ],\n [\n -90.76904296874999,\n 47.60616304386874\n ],\n [\n -89.7802734375,\n 47.85740289465826\n ],\n [\n -89.593505859375,\n 48.019324184801185\n ],\n [\n -89.8077392578125,\n 48.019324184801185\n ],\n [\n -90.02197265625,\n 48.111099041065366\n ],\n [\n -90.24169921875,\n 48.10743118848039\n ],\n [\n -90.4779052734375,\n 48.14043243818811\n ],\n [\n -90.71411132812499,\n 48.103763074117765\n ],\n [\n -90.867919921875,\n 48.25028349849022\n ],\n [\n -91.14257812499999,\n 48.17341248658084\n ],\n [\n -91.4227294921875,\n 48.04503763958813\n ],\n [\n -91.82373046875,\n 48.25394114463431\n ],\n [\n -92.10937499999999,\n 48.381793961204984\n ],\n [\n -92.28515625,\n 48.3416461723746\n ],\n [\n -92.28515625,\n 48.272225451004324\n ],\n [\n -92.373046875,\n 48.25028349849022\n ],\n [\n -92.5048828125,\n 48.45835188280866\n ],\n [\n -92.68615722656249,\n 48.44377831058802\n ],\n [\n -92.6092529296875,\n 48.53843177405044\n ],\n [\n -92.955322265625,\n 48.61475368407372\n ],\n [\n -93.4002685546875,\n 48.636538782610465\n ],\n [\n -93.71337890625,\n 48.48748647988415\n ],\n [\n -93.36181640625,\n 48.38544219115483\n ],\n [\n -93.18603515624999,\n 48.09275716032736\n ],\n [\n -92.79052734375,\n 47.66538735632654\n ],\n [\n -92.2412109375,\n 47.15984001304432\n ],\n [\n -92.10937499999999,\n 46.74362499884437\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":217941,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":800443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palacios, Vicente","contributorId":73043,"corporation":false,"usgs":true,"family":"Palacios","given":"Vicente","email":"","affiliations":[],"preferred":false,"id":800444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marti‐Domken, Barbara","contributorId":242598,"corporation":false,"usgs":false,"family":"Marti‐Domken","given":"Barbara","affiliations":[],"preferred":false,"id":800445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, Lori","contributorId":192924,"corporation":false,"usgs":false,"family":"Schmidt","given":"Lori","affiliations":[],"preferred":false,"id":800446,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213214,"text":"70213214 - 2020 - What are the effects of climate variability and change on ungulate life-histories, population dynamics, and migration in western North America? A systematic map protocol","interactions":[],"lastModifiedDate":"2020-09-15T15:39:29.520745","indexId":"70213214","displayToPublicDate":"2020-09-11T08:21:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5897,"text":"Environmental Evidence","active":true,"publicationSubtype":{"id":10}},"title":"What are the effects of climate variability and change on ungulate life-histories, population dynamics, and migration in western North America? A systematic map protocol","docAbstract":"Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors, and can affect the growth, development, fecundity, dispersal, and demographic trends of populations. Changes in temperature and precipitation, and resulting shifts in plant phenology, winter severity, drought and wildfire conditions, invasive species distribution and abundance, predation, and disease have the potential to directly or indirectly affect ungulates. However, ungulate responses to climate variability and change are not uniform and vary by species and geography. Here, we present a systematic map protocol aiming to describe the abundance and distribution of evidence on the effects of climate variability and change on ungulate life-histories, population dynamics, and migration in North America. This map will help to identify knowledge gaps and clusters of evidence, and can be used to inform future research directions and adaptive management strategies.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-020-00204-w","usgsCitation":"Malpeli, K., Weiskopf, S.R., Thompson, L., and Amanda R. Hardy, 2020, What are the effects of climate variability and change on ungulate life-histories, population dynamics, and migration in western North America? A systematic map protocol: Environmental Evidence, v. 9, 21, 9 p., https://doi.org/10.1186/s13750-020-00204-w.","productDescription":"21, 9 p.","ipdsId":"IP-121485","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":455340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-020-00204-w","text":"Publisher Index Page"},{"id":378393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2020-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Malpeli, Kate 0000-0003-0780-918X","orcid":"https://orcid.org/0000-0003-0780-918X","contributorId":217755,"corporation":false,"usgs":true,"family":"Malpeli","given":"Kate","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":798612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":798613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":798614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amanda R. Hardy","contributorId":240655,"corporation":false,"usgs":false,"family":"Amanda R. Hardy","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":798615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}