Adequate diversity and abundance of native seed for large-scale grassland restorations often require commercially produced seed from distant sources. However, as sourcing distance increases, the likelihood of inadvertent introduction of multiple novel, non-native weed species as seed contaminants also increases. We created a model to determine an “optimal maximum distance” that would maximize availability of native prairie seed from commercial sources while minimizing the risk of novel invasive weeds via contamination. The model focused on the central portion of the Level II temperate prairie ecoregion in the Midwest US. The median optimal maximum distance from which to source seed was 272 km (169 miles). In addition, we weighted the model to address potential concerns from restoration practitioners: 1. sourcing seed via a facilitated migration strategy (i.e., direct movement of species from areas south of a given restoration site to assist species’ range expansion) to account for warming due to climate change; and 2. emphasizing non-native, exotic species with a federal mandate to control. Weighting the model for climate change increased the median optimal maximum distance to 398 km (247 miles), but this was not statistically different from the distance calculated without taking sourcing for climate adaptation into account. Weighting the model for federally mandated exotic species increased the median optimal maximum distance only slightly to 293 km (182 miles), so practitioners may not need to adjust their sourcing strategy, compared to the original model. This decision framework highlights some potential inadvertent consequences from species translocations and provides insight on how to balance needs for prairie seed against those risks.
Habitat fragmentation and loss contribute to isolation of wildlife populations and increased extinction risks for various species, including many large carnivores. We studied a small and isolated population of American black bears (Ursus americanus) that is of conservation concern in central Georgia, USA (i.e., central Georgia bear population [CGBP]). Our goal was to evaluate the potential for demographic and genetic interchange from neighboring bear populations to the CGBP. To evaluate resource selection and movement potential, we used 35,487 global positioning system locations collected every 20 minutes from 2012 to 2014 from 33 male bears in the CGBP. We then developed a step selection function model based on conditional logistic regression. Male bears chose steps that avoided crops, roads, and human developments and were closer to forests and woody wetlands than expected based on availability. We used a geographic information system to simulate 300 bear movement paths from nearby bear populations in northern Florida, northern Georgia, and southern Georgia to estimate the potential for immigration to the CGBP. Only 4 simulated movement paths from the nearby populations intersected the CGBP. The creation of a hypothetical 1-km-wide corridor between the southern Georgia population and the CGBP produced only minor improvements in interchange. Our findings suggest that demographic connectivity between the CGBP and surrounding bear populations may be limited, and coupled with previous works showing genetic isolation in the CGBP, that creation of corridors may have only marginal effects on restoring gene flow, at least in the near term. Management actions such as translocation and the establishment of stepping stone populations may be needed to increase the genetic diversity and demographic stability of bears in the CGBP. © 2021 The Wildlife Society.
Supplemental feeding of wildlife is a common practice often undertaken for recreational or management purposes, but it may have unintended consequences for animal health. Understanding cryptic effects of diet supplementation on the gut microbiomes of wild mammals is important to inform conservation and management strategies. Multiple laboratory studies have demonstrated the importance of the gut microbiome for extracting and synthesizing nutrients, modulating host immunity, and many other vital host functions, but these relationships can be disrupted by dietary perturbation. The well-described interplay between diet, the microbiome, and host health in laboratory and human systems highlights the need to understand the consequences of supplemental feeding on the microbiomes of free-ranging animal populations. This study describes changes to the gut microbiomes of wild elk under different supplemental feeding regimes. We demonstrated significant cross-sectional variation between elk at different feeding locations and identified several relatively low-abundance bacterial genera that differed between fed versus unfed groups. In addition, we followed four of these populations through mid-season changes in supplemental feeding regimes and demonstrated a significant shift in microbiome composition in a single population that changed from natural forage to supplementation with alfalfa pellets. Some of the taxonomic shifts in this population mirrored changes associated with ruminal acidosis in domestic livestock. We discerned no significant changes in the population that shifted from natural forage to hay supplementation, or in the populations that changed from one type of hay to another. Our results suggest that supplementation with alfalfa pellets alters the native gut microbiome of elk, with potential implications for population health.
","language":"English","publisher":"Public Library of Sciences","doi":"10.1371/journal.pone.0249521","usgsCitation":"Couch, C.E., Wise, B., Scurlock, B., Rogerson, J.D., Fuda, R.K., Cole, E.K., Szcodronski, K.E., Sepulveda, A., Hutchins, P.R., and Cross, P., 2021, Effects of supplemental feeding on the fecal bacterial communities of Rocky Mountain elk in the Greater Yellowstone Ecosystem: PLoS ONE, v. 16, no. 4, e0249521, 16 p., https://doi.org/10.1371/journal.pone.0249521.","productDescription":"e0249521, 16 p.","ipdsId":"IP-118898","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":452771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0249521","text":"Publisher Index Page"},{"id":385150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n 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K.","contributorId":203303,"corporation":false,"usgs":false,"family":"Fuda","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":814361,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cole, Eric K 0000-0002-2229-5853","orcid":"https://orcid.org/0000-0002-2229-5853","contributorId":248406,"corporation":false,"usgs":false,"family":"Cole","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":814362,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Szcodronski, Kimberly E 0000-0002-2387-5649","orcid":"https://orcid.org/0000-0002-2387-5649","contributorId":224232,"corporation":false,"usgs":true,"family":"Szcodronski","given":"Kimberly","email":"","middleInitial":"E","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science 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Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":814366,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70219478,"text":"70219478 - 2021 - A new addition to the embalmed fauna of ancient Egypt: Güldenstaedt’s White-toothed Shrew, Crocidura gueldenstaedtii (Pallas, 1811) (Mammalia: Eulipotyphla: Soricidae)","interactions":[],"lastModifiedDate":"2021-04-09T12:31:22.988665","indexId":"70219478","displayToPublicDate":"2021-04-07T07:26:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A new addition to the embalmed fauna of ancient Egypt: Güldenstaedt’s White-toothed Shrew, Crocidura gueldenstaedtii (Pallas, 1811) (Mammalia: Eulipotyphla: Soricidae)","docAbstract":"The Falcon Necropolis at Quesna in the Nile Delta of Egypt is considered to have been founded by the priest Djedhor, the Saviour, of Athribis (Tell Atrib in modern Benha) at the beginning of the Ptolemaic Period. Recent excavations here have revealed abundant avian remains from mummies dedicated to the ancient Egyptian god Horus Khenty-Khety. Among the few mammal remains from the site are five species of shrews (Eulipotyphla: Soricidae), including some that we identified as Güldenstaedt’s White-toothed Shrew, Crocidura gueldenstaedtii (Pallas, 1811). Discovery of this species at Quesna increases the number of shrews recovered from ancient Egyptian archaeological sites to eight species. Crocidura gueldenstaedtii no longer occurs in the Nile Delta, and its presence in a diverse shrew fauna at Quesna that includes one other extirpated species, Crocidura fulvastra (Sundevall, 1843), supports the hypothesis of a moister regional environment 2000–3000 years ago. Inadvertently preserved local faunas, such as that from Quesna, can provide valuable information about ancient environments and subsequent turnover in faunal communities.
Predicting rainfall-induced landslide motion is challenging because shallow groundwater flow is extremely sensitive to the preexisting moisture content in the ground. Here, we use groundwater hydrology theory and numerical modeling combined with five years of field monitoring to illustrate how unsaturated groundwater flow processes modulate the seasonal pore water pressure rise and therefore the onset of motion for slow-moving landslides. The onset of landslide motion at Oak Ridge earthflow in California’s Diablo Range occurs after an abrupt water table rise to near the landslide surface 52–129 days after seasonal rainfall commences. Model results and theory suggest that this abrupt rise occurs from the advection of a nearly saturated wetting front, which marks the leading edge of the integrated downward flux of seasonal rainfall, to the water table. Prior to this abrupt rise, we observe little measured pore water pressure response within the landslide due to rainfall. However, once the wetting front reaches the water table, we observe nearly instantaneous pore water pressure transmission within the landslide body that is accompanied by landslide acceleration. We cast the timescale to reach a critical pore water pressure threshold using a simple mass balance model that considers variable moisture storage with depth and explains the onset of seasonal landslide motion with a rainfall intensity-duration threshold. Our model shows that the seasonal response time of slow-moving landslides is controlled by the dry season vadose zone depth rather than the total landslide thickness.
The Yellowstone Ecosystem Subcommittee (YES) asked the Interagency Grizzly Bear Study Team (IGBST) to re-assess a technique used in annual population estimation and trend monitoring of grizzly bears in the Greater Yellowstone Ecosystem (GYE). This technique is referred to as the Chao2 approach and estimates the number of females with cubs-of-the-year (hereafter, females with cubs) and, in association with other demographic data, is used by the IGBST to produce annual population estimates. Females with cubs are an easily recognizable population segment, and trends for this reproductive segment of the population are assumed to be representative of trend for the entire population.
The overarching objective of the analyses presented in this report was to provide a more accurate representation of the GYE grizzly bear population using the current methodologies in place. Specifically, we addressed two limitations of the current Chao2 approach: 1) underestimation bias associated with a distance criterion used to differentiate annual sightings of females with cubs into unique individuals and 2) limitations of the model-averaging approach to effectively distinguish among potential future population trajectories (decline, stability, and growth).
The first issue addressed in this report is the underestimation bias associated with the rule set that Knight et al. (1995) developed to differentiate sightings of females with cubs into unique individuals (i.e., unique family groups). The rule set was originally designed to be conservative by reducing the risk of identifying more females with cubs than actually existed, primarily through use of a distance criterion of 30 km to separate sightings of unique females. This approach resulted in an underestimation bias, and previous research demonstrated that this bias increases with increasing number of females with cubs. Using location data from radio-marked females with cubs, we evaluated alternative distance criteria by simulating scenarios with varying numbers of true females with cubs and sightings. Findings from these analyses demonstrate that bias in estimates of females with cubs can be substantially reduced by changing the 30-km distance criterion in the rule set to 16 km, which produced relatively unbiased estimates. Findings also indicate, however, the importance of adaptability with regard to the distance criteria because of the complex relationships and biases among the various parameters involved in estimation of unique females with cubs. The total number of annual sightings and the true number of females with cubs play particularly important roles. Whereas these analyses remind us that there is no perfect approach to estimating the number of females with cubs from sightings under various scenarios, they provide us with new tools to determine when and how to adapt the monitoring program.
The second issue we were tasked to investigate was the potential for improvement of the technique referred to as model-averaging, which serves to smooth relatively high variation in annual estimates. This technique was chosen by YES as the basis for monitoring the Yellowstone grizzly bear population, as described in the 2016 Conservation Strategy. This choice was made in part because the technique has been well documented and population estimates derived from counts of females with cubs are conservative. Using simulations of population trends, we demonstrate why the model-averaging technique currently used cannot distinguish between plausible future trend scenarios. As a suitable alternative to model averaging, we propose the use of generalized additive models (GAMs). Using a suite of simulated trend dynamics relevant to management, we demonstrate GAM performance for tracking trends in females with cubs within the context of the annual monitoring program. We demonstrate the ability to not only document directional changes in population trend but also patterns of stabilization or resiliency after such changes. Furthermore, the proposed monitoring framework provides objective measures useful for early detection of directional changes in trend. The new framework is flexible, allowing retrospective analysis of Chao2-based estimates and future applications to time series of other population metrics, such as vital rates.
The aforementioned updates provide us with new tools to determine when and how to adapt the monitoring program. Within the context of current monitoring protocols and effort, and considering the full suite of simulations presented in this report and previous studies, the IGBST plans to incorporate the following changes to the population monitoring protocol: 1) modify the distance criterion, starting with 16 km under current sampling conditions and 2) revise the population monitoring framework using GAMs as the basis for smoothing of annual estimates and detecting trends and changes in trend.
Implementation of the 16-km distance criterion combined with use of GAM techniques would affect some of the population metrics (e.g., annual population size and uncertainty, population trend, mortality rates) used to inform management responses. A primary consideration is that the 16-km distance criterion results in total population estimates derived from the Chao2 estimates that are greater than those we have reported in the past. This increase is due to a change in the implementation of the technique and more accurately represents the number of females with cubs in the GYE grizzly bear population. Additionally, interpretation of retrospective trend patterns may change due to the combination of a different distance criterion and enhanced trend monitoring based on the GAM approach we present here. Implementation will require relatively minor changes in the monitoring protocols described in Appendices B and C of the 2016 Conservation Strategy. Finally, we note that the IGBST has ongoing investigations into the merits of an Integrated Population Model (IPM), for which annual Chao2-based estimates are important input data. The IGBST plans to continue those investigations using the 16-km distance criterion to derive Chao2 estimates.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"van Manen, F.T., Ebinger, M.R., Haroldson, M.A., Bjornlie, D., Clapp, J., Thompson, D.J., Frey, K.L., Costello, C., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2021, A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem, viii, 77 p.","productDescription":"viii, 77 p.","ipdsId":"IP-126615","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":385125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384856,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/science/interagency-grizzly-bear-study-team?qt-science_center_objects=0#qt-science_center_objects"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone 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Section, Wyoming Game & Fish Department, 260 Buena Vista, Lander, WY 82520, USA","active":true,"usgs":false}],"preferred":false,"id":813484,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frey, Kevin L.","contributorId":124580,"corporation":false,"usgs":false,"family":"Frey","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":5125,"text":"Montana Fish Wildlife and Parks, Bear Management Office, 1400 South 19th Avenue, Bozeman, MT 59718","active":true,"usgs":false}],"preferred":false,"id":813485,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":813486,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendricks, 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Katharine R.","contributorId":244265,"corporation":false,"usgs":false,"family":"Wilmot","given":"Katharine","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":813490,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":813491,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fortin-Noreus, Jennifer","contributorId":200746,"corporation":false,"usgs":false,"family":"Fortin-Noreus","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":813492,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hnilicka, Pat","contributorId":256935,"corporation":false,"usgs":false,"family":"Hnilicka","given":"Pat","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813493,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tyers, Daniel B.","contributorId":124587,"corporation":false,"usgs":false,"family":"Tyers","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":5129,"text":"U.S. Forest Service, 2327 University Way, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":813494,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70219448,"text":"70219448 - 2021 - Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","interactions":[],"lastModifiedDate":"2021-04-08T13:12:45.82359","indexId":"70219448","displayToPublicDate":"2021-04-06T08:10:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites","docAbstract":"Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.
Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00256-8","usgsCitation":"Hance, D., Moriarty, K.M., Hollen, B.A., and Perry, R., 2021, Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states: Movement Ecology, v. 9, 17, 22 p., https://doi.org/10.1186/s40462-021-00256-8.","productDescription":"17, 22 p.","ipdsId":"IP-123520","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":452803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00256-8","text":"Publisher Index Page"},{"id":384927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.55224609375,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 41.88592102814744\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moriarty, Katie M.","contributorId":256976,"corporation":false,"usgs":false,"family":"Moriarty","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":51930,"text":"National Council for Air and Stream Improvement, Inc., Corvallis, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":813626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hollen, Bruce A.","contributorId":256977,"corporation":false,"usgs":false,"family":"Hollen","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":51933,"text":"USDI Bureau of Land Management, Regional Office, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":813627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219254,"text":"sir20215011 - 2021 - Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","interactions":[],"lastModifiedDate":"2023-04-10T18:30:08.234211","indexId":"sir20215011","displayToPublicDate":"2021-04-05T11:15:06","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5011","displayTitle":"Aquaculture and Irrigation Water-Use Model (AIWUM) Version 1.0—An Agricultural Water-Use Model Developed for the Mississippi Alluvial Plain, 1999–2017","title":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","docAbstract":"Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (<) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 37.16031654673677\n ],\n [\n -89.296875,\n 37.33522435930639\n ],\n [\n -89.9560546875,\n 37.71859032558816\n ],\n [\n -90.8349609375,\n 36.914764288955936\n ],\n [\n -91.5380859375,\n 35.96022296929667\n ],\n [\n -92.1533203125,\n 34.66935854524543\n ],\n [\n -92.373046875,\n 33.50475906922609\n ],\n [\n -91.0546875,\n 33.100745405144245\n ],\n [\n -89.7802734375,\n 33.50475906922609\n ],\n [\n -88.9453125,\n 35.639441068973944\n ],\n [\n -88.9892578125,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Kansas River provides drinking water to about 800,000 people in northeastern Kansas. Water-treatment facilities that use the Kansas River as a water-supply source use chemical and physical processes during water treatment to remove contaminants before public distribution. Advanced notification of changing water-quality conditions near water-supply intakes allows water-treatment facilities to proactively adjust treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne, collected water-quality data at the Kansas River at Wamego (USGS site 06887500; hereafter referred to as the “Wamego site”) and De Soto (USGS site 06892350; hereafter referred to as the “De Soto site”) monitoring sites to update previously published regression models relating continuous water-quality sensor measurements, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Linear regression analysis was used to update and develop models for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and fecal indicator bacteria at the Wamego and De Soto monitoring sites using data collected during July 2012 through September 2019. The water-quality information documented in this report can be used as guidance for water-treatment processes and to characterize changes in water-quality conditions in the Kansas River over time that would not be otherwise possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211018","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Williams, T.J., 2021, Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019: U.S. Geological Survey Open-File Report 2021–1018, 18 p., https://doi.org/10.3133/ofr20211018.","productDescription":"Report: vii, 18 p.; Appendixes: 1–32; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120556","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1018/downloads","text":"Appendixes 1–32","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1018 Appendixes 1–32"},{"id":384811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1018/ofr20211018.pdf","text":"Report","size":"1.16 MB","description":"OFR 2021–1018"},{"id":384810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1018/coverthb.jpg"},{"id":384813,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.66845703124999,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 38.151837403006766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Emergent late Pliocene and Pleistocene shoreline deposits, morphologically identifiable Pleistocene shoreline units, and seaward-facing scarps characterize the easternmost Atlantic Coastal Plain (ACP) of the United States of America. In some areas of the ACP, these deposits, units, and scarps have been studied in detail. Within these areas, temporal and spatial data are sufficient for time-depositional frameworks for shoreline-evolution to have been developed and published. For other areas, such as the southeastern Atlantic Coastal Plain (SEACP), available data are conflicting and (or) insufficient to develop such a framework, or to make shoreline correlations. Differential epeirogenic uplift and shoreline deformation, resulting from mantle-flow and climate-induced isostatic adjustments, complicate regional shoreline correlations. In the SEACP, the topographically prominent Orangeburg Scarp (hereafter, the Scarp) rises tens of meters in elevation from southeastern Georgia to southeastern North Carolina. The degree to which the Scarp and shoreline units seaward of the Scarp are deformed continues to be debated, but there is general agreement that the lower Savannah River area (LSRA) of Georgia and South Carolina is the least deformed area of the SEACP.
This paper synthesizes published and previously unpublished numerical age and stratigraphic data for emergent Pliocene and younger shoreline deposits in the LSRA in Georgia. Age data are applied to these shoreline deposits as they are delineated (map units) on the 1976 geologic map of Georgia by Lawton and others. Age assignments are based on stratigraphic position, fossil content, soil and weathering diagnostic properties, and numerical ages as determined by meteoric Beryllium‑10 paleosol residence time (10BePRT), optically stimulated luminescence (OSL), uranium disequilibrium series (U-series), amino acid racemization (AAR), and radiocarbon (14C) analyses. These data provide a preliminary Pliocene-Pleistocene geochronology for the Orangeburg Scarp and shoreline deposits seaward of the Scarp in the LSRA of Georgia. Minimum ages and age ranges indicate the following:
Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
The National Cooperative Geologic Mapping Program (NCGMP) is publishing a strategic plan titled Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge (Brock and others, in press). The plan provides a vision, mission, and goals for the program during the years 2020–2030, which are:
In order to achieve the goals outlined in the strategic plan, the NCGMP has developed an implementation plan. This plan will guide the annual review of projects carried out by USGS staff (FEDMAP) described in the plan and the development of the annual FEDMAP prospectus that will ensure the effective application of the NCGMP strategy.
This publication describes the implementation plan of the NCGMP strategy for the southern Pacific Border and Sierra-Cascade Mountains provinces, as defined by Fenneman (1917, 1928, and 1946). This implementation plan focuses on the geology of California and a sliver of Nevada surrounding Lake Tahoe. The southern Pacific Border and Sierra-Cascade Mountains provinces encompass the varied landscapes of the high Sierra Nevada, the Central Valley, and Coast Ranges in northern and central California and the Peninsular Ranges, Continental Borderland, Los Angeles Basin-San Gabriel-San Bernardino valleys, western and central Transverse Ranges, and northernmost Salton Trough in southern California. Societal demands create a need for earth-science data in each of these landscapes. The broader San Francisco Bay area, Central Valley, Los Angeles-San Gabriel-San Bernardino lowlands, and the coastal lowlands that border the Peninsular Ranges are densely populated (about 30 million people) areas at high risk of natural hazards. The mountains of the Sierra Nevada, Peninsular Ranges, and Transverse Ranges, and the coast all provide numerous recreational opportunities that attract visitors from around the world, whereas previously these ranges attracted people to mine their resources. The agricultural capacity of the Central Valley is a critical resource for the Nation that is increasingly water limited.
The southern. Pacific Border and Sierra-Cascade Mountains provinces, at the edge of the North American continent, were profoundly influenced by subduction zone tectonics during the Mesozoic and early Cenozoic (ongoing in northernmost California) and subsequently by the inception, development, and present activity of the San Andreas transform margin system. Although the geology of this region is the poster child of fundamental conceptual models of subduction zone complexes, forearc basins, ophiolite obductions, magmatic arcs, and suspect terranes, as well as hosting one of Earth’s most notorious continental transform faults—the San Andreas Fault—important questions that have important societal consequences remain to be answered. Most of California’s population reside in these provinces and live within 30 miles of an active fault (according to www.earthquakeauthority.com) yet new faults continue to be discovered, highlighting the importance of deformation off the main San Andreas Fault. Bedrock, surficial, and three-dimensional (3D) geologic maps depicting stratigraphic structure and depth to crystalline basement rocks provide critical context and information for understanding fault rupture, distributed deformation, fault connectivity, and history in addition to providing crucial data that enable forecasting of shaking amplitude and length from hypothetical earthquake scenarios.
The tectonic evolution of California produced not only stunning mountains, with associated hazards from landslides and active volcanoes, but also fertile valleys that make California the top agricultural producer in the country in terms of cash receipts (according to www.ers.usda.gov/faqs). These valleys lie atop large basins that not only store groundwater but, in many cases, host oil and gas fields, contributing to the fourth highest hydrocarbon production by State in the country in 2016 (according to https://www.aei.org/carpe-diem/animated-chart-of-us-oil-production-by-state-1981-2017). Water is a key resource increasingly stressed by growing agricultural, industrial, and residential needs. Warmer and drier conditions have led to an increased reliance on extracting groundwater resources, whose availability and quality are dictated at the first order by the 3D spatial distribution of bedrock and Quaternary surficial deposits. Thus, assessment of this critical resource is inextricably tied to knowledge of the surficial and subsurface geologic structure and material types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211012","usgsCitation":"Langenheim, V.E., Graymer, R.W., Powell, R.E., Schmidt, K.M., and Sweetkind, D.S., 2021, Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces: U.S. Geological Survey Open-File Report 2021–1012, 11 p., https://doi.org/10.3133/ofr20211012.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-121693","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":384840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1012/ofr20211012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1012/covrthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.69486597787505\n ],\n [\n -117.20214843749999,\n 34.415973384481866\n ],\n [\n -116.806640625,\n 36.491973470593685\n ],\n [\n -119.35546875000001,\n 38.34165619279595\n ],\n [\n -119.3115234375,\n 39.30029918615029\n ],\n [\n -120.10253906249999,\n 40.212440718286466\n ],\n [\n -121.86035156249999,\n 42.06560675405716\n ],\n [\n -124.3212890625,\n 42.06560675405716\n ],\n [\n -124.541015625,\n 40.51379915504413\n ],\n [\n -123.70605468750001,\n 38.71980474264237\n ],\n [\n -122.607421875,\n 37.19533058280065\n ],\n [\n -121.59667968749999,\n 35.817813158696616\n ],\n [\n -120.58593749999999,\n 34.45221847282654\n ],\n [\n -117.94921874999999,\n 33.54139466898275\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -115.26855468749999,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
American Shad Alosa sapidissima is an anadromous species with populations ranging along the U.S. Atlantic coast. Past American Shad stock assessments have been data limited and estimating system-specific growth parameters or instantaneous natural mortality (M) was not possible. This precluded system-specific stock assessment and management due to reliance on these parameters for estimating other population dynamics (such as yield per recruit). Furthermore, climate-informed biological reference points remain a largely unaddressed need in American Shad stock assessment. Population abundance estimates of American Shad and other species often rely heavily on M derived from von Bertalanffy growth function (VBGF) parameters. Therefore, we developed Bayesian hierarchical models to estimate coastwide, regional, and system-specific VBGF parameters and M using data collected from 1982 to 2017. We tested predictive performance of models that included effects of various climate variables on VBGF parameters within these models. System-specific models were better supported than regional or coast-wide models. Mean asymptotic length (L∞) decreased with increasing mean annual sea surface temperature (SST) and degree days (DD) experienced by fish during their lifetime. Although uncertain, K (Brody growth coefficient) decreased over the same range of lifetime SST and DD. Assuming no adaptation, we projected changes in VBGF parameters and M through 2099 using modeled SST from two climate projection scenarios (Representative Concentration Pathways 4.5 and 8.5). We predicted reduced growth under both scenarios, and M was projected to increase by about 0.10. It is unclear how reduced growth and increased mortality may influence population productivity or life history adaptation in the future, but our results may inform stock assessment models to assess those trade-offs.
In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range-Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits.
Northeastern United States
Carnivora
We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6-year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient.
We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats (Lynx rufus) and coyotes (Canis latrans) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx (Lynx canadensis), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species.
We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community-wide hypotheses at macroecological scales and identify coarse-scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14112","usgsCitation":"Siren, A., Sutherland, C., Bernier, C., Royar, K., Kilborn, J.R., Callahan, C., Cliche, R., Prout, L.S., and Morelli, T.L., 2021, Abiotic stress and biotic factors mediate range dynamics on opposing edges: Journal of Biogeography, v. 48, no. 7, p. 1758-1772, https://doi.org/10.1111/jbi.14112.","productDescription":"15 p.","startPage":"1758","endPage":"1772","ipdsId":"IP-125344","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":452813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.14112","text":"Publisher Index Page"},{"id":410693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.47563781677009,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 45.75938677061683\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Siren, Alexej P. K.","contributorId":236810,"corporation":false,"usgs":false,"family":"Siren","given":"Alexej P. 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Intrusion whole-rock geochemistry and phenocryst and/or mineral trace element compositions coupled with new zircon U-Pb geo-chronology and zircon in situ Lu-Hf isotopes document distinct pulses of magma from beneath the caldera complex. Fourteen intrusions, the Chiquito Peak Tuff, and the dacite of Fisher Gulch were dated, showing intrusive magmatism began after the 28.8 Ma eruption of the Chiquito Peak Tuff and continued to 24 Ma. Additionally, magmatic-hydrothermal mineralization is associated with the intrusive magmatism within and around the margins of the Platoro caldera complex.
After caldera collapse, three plutons were emplaced within the subsided block between ca. 28.8 and 28.6 Ma. These have broadly similar modal miner-alogy and whole-rock geochemistry. Despite close temporal relations between the tuff and the intrusions, mineral textures and compositions indicate that the larger two intracaldera intrusions are discrete later pulses of magma. Intrusions outside the caldera are younger, ca. 28–26.3 Ma, and smaller in exposed area. They contain abundant glomerocrysts and show evidence of open-system processes such as magma mixing and crystal entrainment. The protracted magmatic history at the Platoro caldera complex documents the diversity of the multiple discrete magma pulses needed to generate large composite volcanic fields.
Elizabeth Gierlowski-Kordesch (1956–2016) was a leader and innovator in the specialty field of limnogeology since its beginnings in the late 1980s. Her excitement for field work and examining sediments was contagious, and she was always testing new research ideas. Beth would have been thrilled with the diversity of papers presented in the volume and the wide array of techniques used to determine the history, geochemistry, paleontology, and paleoclimate preserved in the sediments in basins that are located on every continent except Australia and Antarctica. She would also have been delighted that half the chapters were first authored by highly cited women scientists. Beth spent her career teaching, mentoring, conducting research with students and colleagues, and planning limnogeology conferences, books, and field trips. Her contributions span deep-time lakes from North and South America, Africa, Asia, and Europe, starting with her work on the Lower Jurassic East Berlin Formation where she conducted her Ph.D. research. Her work with Kerry Kelts at the University of Minnesota produced two books summarizing global lake research. These volumes are still used by many researchers, particularly as a starting point in their limnogeological studies. Her collaboration with Springer Nature® resulted in the series entitled Syntheses in Limnogeology, a publication that likely would not exist without her enthusiasm and perseverance. The papers in this second volume in the series describe a variety of Jurassic to modern lakes that range from fresh to hypersaline, shallow to deep, vary in size from <1 km2 to 100s of km2, and are found in a number of tectonic settings. Various proxies, including microfossils and trace fossils and analyses of lacustrine sedimentology, stratigraphy, and stable isotopes are used to evaluate the sediment cores and stratigraphic sections to evaluate human and climate influences on the environment, the effects of tectonic, seismic, and volcanic activity, and variations in hydrology. The contributions in this volume reflect the diverse research that Beth conducted herself and we hope is a fitting honor to one of the founding scientists of Limnogeology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66576-0_1","usgsCitation":"Rosen, M., Park Boush, L., Finkelstein, D., and Pla-Pueyo, S., 2021, Introduction to limnogeology: Progress, challenges, and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, chap. of Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, p. 3-16, https://doi.org/10.1007/978-3-030-66576-0_1.","productDescription":"14 p.","startPage":"3","endPage":"16","ipdsId":"IP-122491","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":413425,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosen, Michael R. 0000-0003-3991-0522","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":224435,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park Boush, Lisa 0000-0002-8169-4600","orcid":"https://orcid.org/0000-0002-8169-4600","contributorId":302674,"corporation":false,"usgs":false,"family":"Park Boush","given":"Lisa","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finkelstein, David 0000-0002-9787-1675","orcid":"https://orcid.org/0000-0002-9787-1675","contributorId":302675,"corporation":false,"usgs":false,"family":"Finkelstein","given":"David","email":"","affiliations":[{"id":65529,"text":"Hobart and William Smith Colleges","active":true,"usgs":false}],"preferred":false,"id":865072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pla-Pueyo, Sila 0000-0003-4884-4096","orcid":"https://orcid.org/0000-0003-4884-4096","contributorId":302677,"corporation":false,"usgs":false,"family":"Pla-Pueyo","given":"Sila","email":"","affiliations":[{"id":33422,"text":"University of Granada","active":true,"usgs":false}],"preferred":false,"id":865073,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237363,"text":"70237363 - 2021 - Graph-based reinforcement learning for active learning in real time: An application in modeling river networks","interactions":[],"lastModifiedDate":"2022-10-11T16:57:48.969639","indexId":"70237363","displayToPublicDate":"2021-04-01T11:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Graph-based reinforcement learning for active learning in real time: An application in modeling river networks","docAbstract":"Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2021 SIAM International Conference on Data Mining","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"2021 SIAM International Conference on Data Mining","conferenceDate":"April 29-May 1, 2021","conferenceLocation":"Online","language":"English","publisher":"SIAM","doi":"10.1137/1.9781611976700.70","usgsCitation":"Jia, X., Lin, B., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., and Read, J., 2021, Graph-based reinforcement learning for active learning in real time: An application in modeling river networks, in Proceedings of the 2021 SIAM International Conference on Data Mining, Online, April 29-May 1, 2021, p. 621-629, https://doi.org/10.1137/1.9781611976700.70.","productDescription":"9 p.","startPage":"621","endPage":"629","ipdsId":"IP-123542","costCenters":[{"id":37316,"text":"WMA - 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However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service's species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee, and Balduina atropurpurea) in the Southeastern United States. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs.
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Considering their relative strengths and weaknesses, integrated analysis to these data would likely improve harvest management, but tools for integration have not yet been developed. Managers currently use an aerial count survey on the wintering grounds, the mid-winter survey, to set harvest regulations. We developed an integrated population model (IPM) for Atlantic brant that uses multiple data sources to simultaneously estimate population abundance, survival, and productivity. The IPM abundance estimates for data from 1975–2018 were less variable than annual mid-winter survey counts or Lincoln estimates, presumably reflecting better accounting for observer error and incorporation of demographic estimates by the IPM. Posterior estimates of adult survival were high (0.77–0.87), and harvest rates of adults and juveniles were positively correlated with more liberal hunting regulations (i.e., hunting days and the daily bag limit). Productivity was variable, with the percent of juveniles in the winter population ranging from 1% to >40%. We found no evidence for environmental relationships with productivity. Using IPM-predicted population abundances rather than mid-winter survey counts alone would have meant fewer annual changes to hunting regulations since 2004. Use of the IPM could improve harvest management for Atlantic brant by providing the ability to predict abundance before annual hunting regulations are set, and by providing more stable hunting regulations, with fewer annual changes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22037","usgsCitation":"Roberts, A., Dooly, J., Ross, B., Nichols, T., Leafloor, J., and Dufour, K., 2021, An integrated population model for harvest management of Atlantic brant: Journal of Wildlife Management, v. 85, no. 5, p. 897-908, https://doi.org/10.1002/jwmg.22037.","productDescription":"12 p.","startPage":"897","endPage":"908","ipdsId":"IP-119298","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":482337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.8343877582402,\n 62.41285920640681\n ],\n [\n -69.27612610313905,\n 62.9825936579802\n ],\n [\n -66.9360611726691,\n 66.43037175085522\n ],\n [\n -74.41355122876546,\n 71.18930968714878\n ],\n [\n -92.1985681614551,\n 73.69165787492997\n ],\n [\n -94.52383256314731,\n 72.23639925807228\n ],\n [\n -95.00348613973863,\n 68.4831465437872\n ],\n [\n -91.73869598878188,\n 66.06127536766604\n ],\n [\n -89.4591220679694,\n 64.29556910964087\n ],\n [\n -82.6298769556148,\n 61.229545172462025\n ],\n [\n -78.54052925132643,\n 61.70675111212782\n ],\n [\n -77.8343877582402,\n 62.41285920640681\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, A.J.","contributorId":351178,"corporation":false,"usgs":false,"family":"Roberts","given":"A.J.","affiliations":[{"id":36209,"text":"U.S. FWS","active":true,"usgs":false}],"preferred":false,"id":928111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dooly, J.L.","contributorId":351179,"corporation":false,"usgs":false,"family":"Dooly","given":"J.L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, T.C.","contributorId":351180,"corporation":false,"usgs":false,"family":"Nichols","given":"T.C.","affiliations":[{"id":83933,"text":"New Jersey Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leafloor, J.O.","contributorId":351181,"corporation":false,"usgs":false,"family":"Leafloor","given":"J.O.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dufour, K.W.","contributorId":351182,"corporation":false,"usgs":false,"family":"Dufour","given":"K.W.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928116,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228564,"text":"70228564 - 2021 - Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","interactions":[],"lastModifiedDate":"2022-02-14T15:58:57.838707","indexId":"70228564","displayToPublicDate":"2021-04-01T09:48:22","publicationYear":"2021","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}},"displayTitle":"Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","title":"Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","docAbstract":"Polymorphism facilitates coexistence of divergent morphs (e.g., phenotypes) of the same species by minimizing intraspecific competition, especially when resources are limiting. Arctic char (Salvelinus sp.) are a Holarctic fish often forming morphologically, and sometimes genetically, divergent morphs. In this study, we assessed the morphological and genetic diversity and divergence of 263 individuals from seven populations of arctic char with varying length-frequency distributions across two distinct groups of lakes in northern Alaska. Despite close geographic proximity, each lake group occurs on landscapes with different glacial ages and surface water connectivity, and thus was likely colonized by fishes at different times. Across lakes, a continuum of physical (e.g., lake area, maximum depth) and biological characteristics (e.g., primary productivity, fish density) exists, likely contributing to characteristics of present-day char populations. Although some lakes exhibit bimodal size distributions, using model-based clustering of morphometric traits corrected for allometry, we did not detect morphological differences within and across char populations. Genomic analyses using 15,934 SNPs obtained from genotyping by sequencing demonstrated differences among lake groups related to historical biogeography, but within lake groups and within individual lakes, genetic differentiation was not related to total body length. We used PERMANOVA to identify environmental and biological factors related to observed char size structure. Significant predictors included water transparency (i.e., a primary productivity proxy), char density (fish·ha-1), and lake group. Larger char occurred in lakes with greater primary production and lower char densities, suggesting less intraspecific competition and resource limitation. Thus, char populations in more productive and connected lakes may prove more stable to environmental changes, relative to food-limited and closed lakes, if lake productivity increases concomitantly. Our findings provide some of the first descriptions of genomic characteristics of char populations in arctic Alaska, and offer important consideration for the persistence of these populations for subsistence and conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.7211","usgsCitation":"Klobucar, S., Rick, J., Mandeville, E., Wagner, C.E., and Budy, P., 2021, Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska: Ecology and Evolution, v. 11, no. 7, p. 3040-3057, https://doi.org/10.1002/ece3.7211.","productDescription":"18 p.","startPage":"3040","endPage":"3057","ipdsId":"IP-117493","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.7211","text":"Publisher Index Page"},{"id":395888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Mountain Range, Toolik Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.74365234374997,\n 68.49604022839505\n ],\n [\n -148.95538330078125,\n 68.49604022839505\n ],\n [\n -148.95538330078125,\n 68.70448628851169\n ],\n [\n -149.74365234374997,\n 68.70448628851169\n ],\n [\n -149.74365234374997,\n 68.49604022839505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Klobucar, Stephen L.","contributorId":172291,"corporation":false,"usgs":false,"family":"Klobucar","given":"Stephen L.","affiliations":[],"preferred":false,"id":834610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rick, Jessica A.","contributorId":276155,"corporation":false,"usgs":false,"family":"Rick","given":"Jessica A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":834611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":834612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":834613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834609,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238781,"text":"70238781 - 2021 - Heterotrophic respiration and the divergence of productivity and carbon sequestration","interactions":[],"lastModifiedDate":"2022-12-12T15:08:10.493171","indexId":"70238781","displayToPublicDate":"2021-04-01T09:00:59","publicationYear":"2021","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":"Heterotrophic respiration and the divergence of productivity and carbon sequestration","docAbstract":"Net primary productivity (NPP) and net ecosystem production (NEP) are often used interchangeably, as their difference, heterotrophic respiration (soil heterotrophic CO2 efflux, RSH = NPP−NEP), is assumed a near-fixed fraction of NPP. Here, we show, using a range-wide replicated experimental study in loblolly pine (Pinus taeda) plantations that RSH responds differently than NPP to fertilization and drought treatments, leading to the divergent responses of NPP and NEP. Across the natural range of the species, the moderate responses of NPP (+11%) and RSH (−7%) to fertilization combined such that NEP increased nearly threefold in ambient control and 43% under drought treatment. A 13% decline in RSH under drought led to a 26% increase in NEP while NPP was unaltered. Such drought benefit for carbon sequestration was nearly twofold in control, but disappeared under fertilization. Carbon sequestration efficiency, NEP:NPP, varied twofold among sites, and increased up to threefold under both drought and fertilization.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL092366","usgsCitation":"Noormets, A., Bracho, R., Ward, E., Seiler, J., Strahm, B., Lin, W., McElligott, K., Domec, J., Gonzalez-Benecke, C., Jokela, E.J., Markewitz, D.M., Meek, C., Miao, G., McNulty, S.G., King, J., Samuelson, L., Sun, G., Teskey, R., Vogel, J., Will, R.E., Yang, J., and Martin, T.A., 2021, Heterotrophic respiration and the divergence of productivity and carbon sequestration: Geophysical Research Letters, v. 48, no. 7, e2020GL092366, 10 p., https://doi.org/10.1029/2020GL092366.","productDescription":"e2020GL092366, 10 p.","ipdsId":"IP-128106","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452842,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl092366","text":"External 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especially as transportation of light and heavy crude oils has substantially increased across river corridors and coasts in the Great Lakes Basin. The persistent sheening from accumulated OPA along 60 km of the Kalamazoo River in Michigan’s lower peninsula resulted in a lengthy and expensive cleanup for the 2010 Enbridge Line 6B pipeline rupture. The interaction of oil with river mineral sediment and organic matter and its long-term fate depend on the physical properties of the oil and particles as well as the environmental setting of river, its climate, morphology, currents and mixing opportunities. This research brief describes the expanded work conducted for the cleanup for the 2010 Enbridge Line 6B pipeline rupture and includes laboratory experiments of aggregate characteristics with Cold Lake Blend and a range of sediment particle sizes, addition of an OPA formation algorithm to an existing sediment contaminant transport model, and development of a simplified, particle-tracking based rapid response model of OPA formation, transport, and deposition. 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2021 - Predicting the spatiotemporal exposure of aquatic species to intrusions of fire retardant in streams with limited data","interactions":[],"lastModifiedDate":"2021-04-15T12:51:24.287992","indexId":"70219585","displayToPublicDate":"2021-04-01T07:50:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the spatiotemporal exposure of aquatic species to intrusions of fire retardant in streams with limited data","docAbstract":"Because fire retardant can enter streams and harm aquatic species including endangered fish, agencies such as the U.S. Forest Service (USFS) must estimate the downstream extent of toxic effects every time fire retardant enters streams (denoted as an “intrusion”). A challenge in estimating the length of stream affected by the intrusion and the exposure time of species in the affected reach is the lack of data typically available on the stream's geometry and flow characteristics. Previously, the USFS estimated the affected reach length assuming instantaneous mixing of the retardant over the reach; however, this approach neglects key river mixing processes. An approach is described that accounts for advection and dispersion of the retardant as well as the downstream growth of the stream. Applied to 13 intrusions documented by the USFS, the new approach shows affected reach lengths range between 8.0 and 362 km; all 13 cases exceeded previous estimates from an instantaneous mixing model. The time that a stationary individual in the affected reach is exposed to concentrations above a pre-defined toxicity threshold (10% of 96-hour LC50, for example) ranges from 0.17 to 2.73 h, with all but one case having a maximum exposure time less than 1.5 h. Results from 1152 hypothetical intrusions provided by the USFS confirm that exposure times rarely exceed 5 h. This result suggests that 96-hour tests to determine toxicity (LC50) to various species should be reconsidered. Although the approach described can be improved in several ways, it provides a first estimate of the effects of fire retardant intrusions.