Identifying which environmental and genetic factors affect growth pattern phenotypes can help biologists predict how organisms distribute finite energy resources in response to varying environmental conditions and physiological states. This information may be useful for monitoring and managing populations of cryptic, endangered, and invasive species. Consequently, we assessed the effects of food availability, clutch, and sex on the growth of invasive Burmese pythons (Python bivittatus Kuhl) from the Greater Everglades Ecosystem in Florida, USA. Though little is known from the wild, Burmese pythons have been physiological model organisms for decades, with most experimental research sourcing individuals from the pet trade. Here, we used 60 hatchlings collected as eggs from the nests of two wild pythons, assigned them to High or Low feeding treatments, and monitored growth and meal consumption for 12 weeks, a period when pythons are thought to grow very rapidly. None of the 30 hatchlings that were offered food prior to their fourth week post-hatching consumed it, presumably because they were relying on internal yolk stores. Although only two clutches were used in the experiment, we found that nearly all phenotypic variation was explained by clutch rather than feeding treatment or sex. Hatchlings from clutch 1 (C1) grew faster and were longer, heavier, in better body condition, ate more frequently, and were bolder than hatchlings from clutch 2 (C2), regardless of food availability. On average, C1 and C2 hatchling snout-vent length (SVL) and weight grew 0.15 cm d−1 and 0.10 cm d−1, and 0.20 g d−1 and 0.03 g d−1, respectively. Additional research may be warranted to determine whether these effects remain with larger clutch sample sizes and to identify the underlying mechanisms and fitness implications of this variation to help inform risk assessments and management.
","language":"English","publisher":"The Company of Biologists","doi":"10.1242/bio.058739","usgsCitation":"Josimovich, J.M., Falk, B., Grajal-Puche, A., Hanslowe, E.B., Bartoszek, I., Reed, R., and Currylow, A.F., 2021, Clutch may predict growth of hatchling Burmese pythons better than food availability or sex: Biology Open, v. 10, no. 11, bio058739, 10 p., https://doi.org/10.1242/bio.058739.","productDescription":"bio058739, 10 p.","ipdsId":"IP-121446","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450169,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1242/bio.058739","text":"Publisher Index Page"},{"id":436113,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WHSSJ6","text":"USGS data release","linkHelpText":"Hatchling Growth Experiment Dataset from Invasive Burmese Pythons Captured in 2015 in Southern Florida"},{"id":391972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Josimovich, Jillian Maureen 0000-0002-7523-3496 jjosimovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7523-3496","contributorId":257058,"corporation":false,"usgs":true,"family":"Josimovich","given":"Jillian","email":"jjosimovich@usgs.gov","middleInitial":"Maureen","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falk, Bryan G. 0000-0002-9690-5626","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":265395,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":827123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grajal-Puche, Alejandro 0000-0003-1807-4799","orcid":"https://orcid.org/0000-0003-1807-4799","contributorId":265397,"corporation":false,"usgs":false,"family":"Grajal-Puche","given":"Alejandro","affiliations":[{"id":54677,"text":"Department of Biological Sciences, P.O. Box 5640, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":827124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanslowe, Emma B. 0000-0003-4331-6729","orcid":"https://orcid.org/0000-0003-4331-6729","contributorId":265394,"corporation":false,"usgs":false,"family":"Hanslowe","given":"Emma","email":"","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":827125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartoszek, Ian A.","contributorId":269426,"corporation":false,"usgs":false,"family":"Bartoszek","given":"Ian A.","affiliations":[{"id":55974,"text":"Conservancy of Southwest Florida, Naples, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":827126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827128,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228203,"text":"70228203 - 2021 - Impacts of a non-indigenous ecosystem engineer, the American beaver (Castor canadensis), in a biodiversity hotspot","interactions":[],"lastModifiedDate":"2022-02-28T19:08:05.762991","indexId":"70228203","displayToPublicDate":"2021-11-18T09:38:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impacts of a non-indigenous ecosystem engineer, the American beaver (Castor canadensis), in a biodiversity hotspot","title":"Impacts of a non-indigenous ecosystem engineer, the American beaver (Castor canadensis), in a biodiversity hotspot","docAbstract":"Non-native species having high per capita impacts in invaded communities are those that modulate resource availability and alter disturbance regimes in ways that are biologically incompatible with the native biota. In areas where it has been introduced by humans, American beaver (Castor canadensis) is an iconic example of such species due to its capacity to alter trophic dynamics of entire ecosystems and create new invasional pathways for other non-native species. The species is problematic in several watersheds within the Southern California-Northern Baja California Coast Ecoregion, a recognized hotspot of biodiversity, due to its ability to modify habitat in ways that favor invasive predators and competitors over the region's native species and habitat. Beaver was deliberately introduced across California in the mid-1900s and generally accepted as non-native to the region up to the early 2000s; however, articles promoting the idea that beaver may be a natural resident have gained traction in recent years, due in large part to the species' charismatic nature rather than by presentation of sound evidence. Here, we discuss the problems associated with beaver disturbance and its effects on conserving the region's native fauna and flora. We refute arguments underlying the claim that beaver is native to the region, and review paleontological, zooarchaeological, and historical survey data from renowned field biologists and naturalists over the past ~160 years to show that no evidence exists that beaver arrived by any means other than deliberate human introduction. Managing this ecosystem engineer has potential to reduce the richness and abundance of other non-native species because the novel, engineered habitat now supporting these species would diminish in beaver-occupied watersheds. At the same time, hydrologic functionality would shift toward more natural, ephemeral conditions that favor the regions' native species while suppressing the dominance of the most insidious invaders.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2021.752400","usgsCitation":"Richmond, J.Q., Swift, C.C., Wake, T.A., Brehme, C.S., Preston, K.L., Kus, B., Ervin, E., Tremor, S., Matsuda, T., and Fisher, R.N., 2021, Impacts of a non-indigenous ecosystem engineer, the American beaver (Castor canadensis), in a biodiversity hotspot: Frontiers in Conservation Science, v. 2, p. 1-14, https://doi.org/10.3389/fcosc.2021.752400.","productDescription":"752400, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-134539","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2021.752400","text":"Publisher Index Page"},{"id":395531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": 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cbrehme@usgs.gov","orcid":"https://orcid.org/0000-0001-8904-3354","contributorId":3419,"corporation":false,"usgs":true,"family":"Brehme","given":"Cheryl","email":"cbrehme@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Preston, Kristine L. 0000-0002-6958-1128 kpreston@usgs.gov","orcid":"https://orcid.org/0000-0002-6958-1128","contributorId":207765,"corporation":false,"usgs":true,"family":"Preston","given":"Kristine","email":"kpreston@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kus, Barbara E. 0000-0002-3679-3044 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California","active":true,"usgs":false}],"preferred":false,"id":833408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matsuda, Tritia 0000-0001-9271-7671","orcid":"https://orcid.org/0000-0001-9271-7671","contributorId":213956,"corporation":false,"usgs":true,"family":"Matsuda","given":"Tritia","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833409,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833410,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70226776,"text":"70226776 - 2021 - Multi-model comparison of computed debris flow runout for the 9 January 2018 Montecito, California post-wildfire event","interactions":[],"lastModifiedDate":"2021-12-13T13:10:17.544528","indexId":"70226776","displayToPublicDate":"2021-11-18T07:00:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Multi-model comparison of computed debris flow runout for the 9 January 2018 Montecito, California post-wildfire event","docAbstract":"Hazard assessment for post-wildfire debris flows, which are common in the steep terrain of the western United States, has focused on the susceptibility of upstream basins to generate debris flows. However, reducing public exposure to this hazard also requires an assessment of hazards in downstream areas that might be inundated during debris flow runout. Debris flow runout models are widely available, but their application to hazard assessment for post-wildfire debris flows has not been extensively tested. Necessary inputs to these models include the total volume of the mobilized flow, flow properties (either inherent material properties or calibration coefficients), and site topography. Estimates of volume are possible in post-event (“back calculation”) studies, yet before an event, volume is an uncertain quantity. We simulated debris flow runout for the well-constrained 9 January 2018 Montecito event using three models (RAMMS, FLO2D, and D-Claw) to determine the relative importance of volume and flow properties. We broke the impacted area into three domains, and for each model-domain combination, we performed a numerical sampling study in which volume and flow properties varied within a wide, but plausible range. We assessed model performance based on inundation patterns and peak flow depths. We found all models could simulate the event with comparable results. Simulation performance was most sensitive to flow volume and less sensitive to flow properties. Our results emphasize the importance of reducing uncertainty in pre-event estimates of flow volume for hazard assessment.
Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States.
Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records).
Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons.
Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale.
Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions.
Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.
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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Turtle body size is associated with demographic and other traits like mating success, reproductive output, maturity, and survival. As such, growth analyses are valuable for testing life history theory, demographic modeling, and conservation planning. Two important but unsettled research areas relate to growth after maturity and growth rate variation. If individuals exhibit indeterminate growth after maturity, older adults may have an advantage in fecundity, survival, or both over younger/smaller adults. Similarly, depending on how growth varies, a portion of the population may mature earlier, grow larger, or both. We used 23-years of capture-mark-recapture data to study growth and maturity in the Spotted Turtle (Clemmys guttata), a species suffering severe population declines and for which demographic data are needed for development of effective conservation and management strategies. There was strong support for models incorporating sex as a factor, with the interval growth model reparametrized for capture-mark-recapture data producing later mean maturation estimates than the age-based growth model. We found most individuals (94%) continued growing after maturity, but the instantaneous relative annual plastral growth rate was low. We recommend future studies examine the possible contribution of such slow, continued adult growth to fecundity and survival. Even seemingly negligible amounts of annual adult growth can have demographic consequences affecting the population vital rates for long-lived species.
Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. To address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006492","usgsCitation":"Waring, B.G., Smith, K.R., Grote, E.E., Howell, A.J., Reibold, R.H., Tucker, C.L., and Reed, S., 2021, Climatic controls on soil carbon accumulation and loss in a dryland ecosystems: Journal of Geophysical Research, v. 126, no. 12, e2021JG006492, 13 p., https://doi.org/10.1029/2021JG006492.","productDescription":"e2021JG006492, 13 p.","ipdsId":"IP-133338","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450184,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1978553","text":"External Repository"},{"id":393749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.46605682373047,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.58896696823242\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kenneth R","contributorId":270738,"corporation":false,"usgs":false,"family":"Smith","given":"Kenneth","email":"","middleInitial":"R","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grote, Edmund E. 0000-0002-9103-9482 ed_grote@usgs.gov","orcid":"https://orcid.org/0000-0002-9103-9482","contributorId":4271,"corporation":false,"usgs":true,"family":"Grote","given":"Edmund","email":"ed_grote@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howell, Armin J. 0000-0003-1243-0238 ahowell@usgs.gov","orcid":"https://orcid.org/0000-0003-1243-0238","contributorId":196798,"corporation":false,"usgs":true,"family":"Howell","given":"Armin","email":"ahowell@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":829897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829898,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225532,"text":"sir20215109 - 2021 - Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","interactions":[],"lastModifiedDate":"2021-11-23T13:06:28.021637","indexId":"sir20215109","displayToPublicDate":"2021-11-17T07:15:00","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":"2021-5109","displayTitle":"Documentation and Mapping of Flooding From the January and March 2018 Nor’easters in Coastal New England","title":"Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","docAbstract":"In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Ecological transformation creates many challenges for public natural resource management and requires managers to grapple with new relationships to change and new ways to manage it. In the context of unfamiliar trajectories of ecological change, a manager can resist, accept, or direct change, choices that make up the resist-accept-direct (RAD) framework. In this article, we provide a conceptual framework for how to think about this new decision space that managers must navigate. We identify internal factors (mental models) and external factors (social feasibility, institutional context, and scientific uncertainty) that shape management decisions. We then apply this conceptual framework to the RAD strategies (resist, accept, direct) to illuminate how internal and external factors shape those decisions. Finally, we conclude with a discussion of how this conceptual framework shapes our understanding of management decisions, especially how these decisions are not just ecological but also social, and the implications for research and management.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biab086","usgsCitation":"Clifford, K.R., Cravens, A.E., and Knapp, C.N., 2021, Responding to ecological transformation: Mental models, external constraints, and manager decision-making: BioScience, v. 72, no. 1, p. 57-70, https://doi.org/10.1093/biosci/biab086.","productDescription":"14 p.","startPage":"57","endPage":"70","ipdsId":"IP-127232","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab086","text":"Publisher Index Page"},{"id":394010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clifford, Katherine R. 0000-0002-1385-8765","orcid":"https://orcid.org/0000-0002-1385-8765","contributorId":259886,"corporation":false,"usgs":true,"family":"Clifford","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knapp, Corrine N.","contributorId":270993,"corporation":false,"usgs":false,"family":"Knapp","given":"Corrine","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":830321,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226753,"text":"70226753 - 2021 - Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","interactions":[],"lastModifiedDate":"2021-12-09T12:35:17.454202","indexId":"70226753","displayToPublicDate":"2021-11-17T06:32:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","docAbstract":"Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.
The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space-for-time substitution). Still, the efficacy of fitting occupancy models using a space-for-time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay-Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space-for-time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space-for-time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long-term monitoring data to understand the distribution of species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8292","usgsCitation":"Duarte, A., and Peterson, J., 2021, Space-for-time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay-Delta: Ecology and Evolution, v. 11, no. 23, p. 16727-16744, https://doi.org/10.1002/ece3.8292.","productDescription":"18 p.","startPage":"16727","endPage":"16744","ipdsId":"IP-123647","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8292","text":"Publisher Index Page"},{"id":489260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"San Francisco Bay-Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.21496769714439,\n 38.746434916955366\n ],\n [\n -122.69121998415443,\n 38.746434916955366\n ],\n [\n -122.69121998415443,\n 37.862368554497834\n ],\n [\n -121.21496769714439,\n 37.862368554497834\n ],\n [\n -121.21496769714439,\n 38.746434916955366\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"23","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":79822,"corporation":false,"usgs":true,"family":"Duarte","given":"Adam","email":"","affiliations":[],"preferred":false,"id":938750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938749,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225747,"text":"sir20215115 - 2021 - Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","interactions":[],"lastModifiedDate":"2021-11-16T15:03:52.608004","indexId":"sir20215115","displayToPublicDate":"2021-11-16T10:00:00","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":"2021-5115","displayTitle":"Update of the Groundwater Flow Model for the Great Miami Buried-Valley Aquifer in the Vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","title":"Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","docAbstract":"A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade-1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade-1, while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade-1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology
Medicine Lake Highlands/Fall River Springs Aquifer System, located in northeastern California, is home to some of the largest first-order springs in the United States. This work assesses the likely effects of projected climate change on spring flow. Four anticipated climate futures (GFDL A2, GFDL B1, CCSM4 rcp 8.5, CNRM rcp 8.5) for California, which predict a range of conditions (generally warming and transitioning from snow to rain with variable amounts of total precipitation), are postulated to affect groundwater recharge primarily by changing evapotranspiration. The linkages between climate variables and spring flow are evaluated using a water balance model that represents the physics of evapotranspiration and recharge, the Basin Characterization Model. Three of the four climate scenarios (GFDL A2, GFDL B1, CCSM4 rcp 8.5) project that by the year 2100, groundwater recharge (and consequently decreased spring flow) will decrease by 27%, 21%, and 9%, respectively. The fourth scenario (CNRM rcp 8.5) showed an increase in recharge of 32% due to a significant increase in precipitation (27%). Evapotranspiration increases due to a shift in the type of precipitation and a longer growing season. While the likelihood of each scenario is outside the scope of this work, unless total precipitation increases dramatically in the future, increased temperatures and decreasing precipitation will likely result in reduced spring flows, along with warmer water temperatures in downstream habitats.
Magma-water interaction can dramatically influence the explosivity of volcanic eruptions. However, syn- and post-eruptive diffusion of external (non-magmatic) water into volcanic glass remains poorly constrained and may bias interpretation of water in juvenile products. Hydrogen isotopes in ash from the 2009 eruption of Redoubt Volcano, Alaska, record syn-eruptive hydration by vaporized glacial meltwater. Both ash aggregation and hydration occurred in the wettest regions of the plume, which resulted in the removal and deposition of the most hydrated ash in proximal areas <50 km from the vent. Diffusion models show that the high temperatures of pyroclast-water interactions (>400°C) are more important than the cooling rate in facilitating hydration. These observations suggest that syn-eruptive glass hydration occurred where meltwater was entrained at high temperature, in the plume margins near the vent. Ash in the drier plume interior remained insulated from entrained meltwater until it cooled sufficiently to avoid significant hydration.
The origin and parental magma for layered cumulates in the Lower Banded series (LBS) and the J-M Reef Pd-Pt deposit of the Stillwater Complex remains poorly constrained. We present whole-rock lithogeochemistry and mineral chemistry from LBS rocks collected from drill holes and surface samples from the Mountain View area of the complex that in total span nearly the entirety of the LBS stratigraphy. Excess S, Pt, and Pd in the noritic and gabbronoritic cumulates of the LBS indicate that small amounts of high tenor sulfide liquid generated at very low degrees of sulfide oversaturation were ubiquitous parts of the cumulate assemblage. We show that a simple two-stage thermodynamic model of assimilation-batch crystallization of a komatiitic parental magma in the lower crust, produces a close match to a common suite of fine-grained gabbronorite dikes and sills that intrude both the complex and its footwall. After fractionating ultramafic cumulates in the lower crust, the model contaminated komatiitic liquid produces upper crustal cumulates by batch crystallization en route to or at the level of the intrusion. The modeled rocks have compositions and mineral assemblages closely resembling pyroxenite of the Bronzitite zone and both norite and gabbronorite cumulates in the lower LBS. The trends from the Bronzitite zone through Norite zone I and Gabbronorite zone I can be understood as the result of deposition of crystals from successive batches of the same contaminated parental magma, with an upward trend toward greater amounts of cooling before the separation of crystals from liquid. The olivine-bearing suite of Olivine-bearing zone I, which includes the J-M Reef, can be modeled by partial remelting of the same norite and gabbronorite cumulates due to a temporarily increased flux of hot, moderately less contaminated LBS parental magma that infiltrated partially molten cumulates because its density exceeded that of the interstitial liquid. This model suggests that infiltration of hot Mg-rich parental liquid into moderately PGE-enriched footwall cumulates may be fundamental to the formation of the extremely high tenor sulfide mineralization in the J-M Reef ore zone, and perhaps other reef-type deposits worldwide. The same metal tenors that would require silicate/sulfide mass ratios (i.e., R-factors) of 105 to 106 in a single stage of equilibration would be attained during this second stage of interaction by the incremental infiltration and passage of LBS parental magma through previously sulfide saturated cumulate mush.
In support of nutrient reduction efforts, total phosphorus loads and yields were computed using turbidity-surrogate and LOAD ESTimator (LOADEST) models for the Cedar River at Palo, Iowa, for January 1, 2009, to December 15, 2020. Sample data were used to create a total phosphorus concentration turbidity-surrogate model. Total phosphorus loads also were computed from two streamflow-based LOADEST load models for the periods 2009–20 and 2016–20. The 2009–20 model was used for comparison with previously published loads at this site. The 2016–20 LOADEST model was used with the turbidity-surrogate model before sensor deployment and during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents computed loads and methods needed to compute site-specific loads accurately and track annual progress toward nutrient reduction goals within the State.
A comparison of loads from Weighted Regressions on Time, Discharge, and Season; LOADEST; and surrogate models indicated substantial differences at this site among these methods. Changes in both monitoring approaches (high-frequency sensor and surrogate data) and changes in load-calculation methods present potential challenges in assessing trends, such as assessment of load reduction.
Annual total phosphorus loads for the Cedar River at Palo, Iowa, ranged from 1,370 to 2,360 U.S. short tons per year for 2018–20, based on the turbidity-surrogate model with gaps in sensor data filled with the 2016–20 LOADEST model. Annual total phosphorus yields for the Cedar River ranged from 0.67 to 1.16 pounds per acre per year for 2018–20. Although this load estimate is lower than previous estimates for the benchmark period of 2006–10, when normalized by streamflow, nearly all the apparent reduction can be attributed to differences in the load-calculation methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215127","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Garrett, J.D., 2021, Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20: U.S. Geological Survey Scientific Investigations Report 2021–5127, 15 p., https://doi.org/10.3133/sir20215127.","productDescription":"Report vi, 15 p.: Database; Related Work","onlineOnly":"Y","ipdsId":"IP-127065","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5127/coverthb.jpg"},{"id":391621,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5127/sir20215127.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5127"},{"id":391622,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":391623,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20185090","text":"Transport of nitrogen and phosphorus in the Cedar River Basin, Iowa and Minnesota, 2000–15"}],"country":"United States","state":"Palo","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.83197021484375,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.02889410108475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Axis deer (Axis axis) are invasive species that threaten native ecosystems and agriculture on Maui Island. To mitigate negative effects, it is necessary to understand current abundance, population trajectory, and how to most effectively reduce the population. Our objectives were to examine the population history of Maui axis deer, estimate observed population growth, and use species-specific demographic parameters in a VORTEX population viability analysis to examine removal scenarios that would most effectively reduce the population. Only nine deer were introduced in 1959, but recent estimates of >10,000 deer suggest population growth rates (r) ranging between 0.147 and 0.160 even though >11,200 have been removed by hunters and resource managers. In VORTEX simulations, we evaluated an initial population size of 6,000 females and 4,000 males, reflecting the probable 3F:2M sex ratio, with annual removal rates of 10%, 20%, and 30% over a 10-year period. A removal rate of 10% resulted in a positive growth rate of 0.103 ± 0.001. A 20% removal rate resulted in only a slightly negative growth, while a 30% removal rate resulted in –0.130 ± 0.004. By increasing the ratio of females removed to 4F:1M in the 30% harvest scenario, the decline nearly doubled, resulting in –0.223 ± 0.004. Effectively reducing axis deer will most likely require an annual removal of approximately 20–30% of the population and with a greater proportion of females to increase the population decline. Selective removal of males may not only be inefficient, but also counterproductive to population reduction goals.
We evaluate the seismic performance of modern seismically designed wood light-frame (WLF) buildings, considering regional seismic hazard characteristics that influence ground motion duration and frequency content and, thus, seismic risk. Results show that WLF building response correlates strongly with ground motion spectral shape but weakly with duration. Due to the flatter spectral shape of ground motions from subduction events, WLF buildings at sites affected by these earthquakes may experience double the economic losses for a given intensity of shaking, and collapse capacities may be reduced by up to 50%, compared to those at sites affected by crustal earthquakes. These differences could motivate significant increases in design values at sites affected by subduction earthquakes to achieve the uniform risk targets of the American Society of Civil Engineers (ASCE) 7 standard.
","language":"English","publisher":"Springer","doi":"10.1007/s10518-021-01258-y","usgsCitation":"Chase, R.E., Liel, A.B., Luco, N., and Bullock, Z., 2021, Hazard-consistent seismic losses and collapse capacities for light-frame wood buildings in California and Cascadia: Bulletin of Earthquake Engineering, v. 19, p. 6615-6639, https://doi.org/10.1007/s10518-021-01258-y.","productDescription":"25 p.","startPage":"6615","endPage":"6639","ipdsId":"IP-129311","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450224,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10518-021-01258-y","text":"Publisher Index Page"},{"id":394517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, Oregon Washington","city":"Anchorage, Eugene, Los Angeles, Portland, San Francisco, 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-122.76123046875,\n 45.44471679159555\n ],\n [\n -122.38494873046875,\n 45.44471679159555\n ],\n [\n -122.38494873046875,\n 45.60395019421033\n ],\n [\n -122.76123046875,\n 45.60395019421033\n ],\n [\n -122.76123046875,\n 45.44471679159555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1728744506836,\n 44.00343436215528\n ],\n [\n -123.04309844970705,\n 44.00343436215528\n ],\n [\n -123.04309844970705,\n 44.109281923355645\n ],\n [\n -123.1728744506836,\n 44.109281923355645\n ],\n [\n -123.1728744506836,\n 44.00343436215528\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4591064453125,\n 47.47637579720933\n ],\n [\n -122.24212646484375,\n 47.47637579720933\n ],\n [\n -122.24212646484375,\n 47.758714187846294\n ],\n [\n -122.4591064453125,\n 47.758714187846294\n ],\n [\n -122.4591064453125,\n 47.47637579720933\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.08697509765622,\n 61.062272494474065\n ],\n [\n -149.67498779296875,\n 61.062272494474065\n ],\n [\n -149.67498779296875,\n 61.28739102214365\n ],\n [\n -150.08697509765622,\n 61.28739102214365\n ],\n [\n -150.08697509765622,\n 61.062272494474065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2021-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Chase, Robert Edward 0000-0002-8155-6830","orcid":"https://orcid.org/0000-0002-8155-6830","contributorId":271198,"corporation":false,"usgs":true,"family":"Chase","given":"Robert","email":"","middleInitial":"Edward","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":831149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liel, Abbie B.","contributorId":184158,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":831150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":831151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bullock, Zach","contributorId":271199,"corporation":false,"usgs":false,"family":"Bullock","given":"Zach","email":"","affiliations":[{"id":56314,"text":"Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125","active":true,"usgs":false}],"preferred":false,"id":831152,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229519,"text":"70229519 - 2021 - Mineral deposit discovery order and three-part quantitative assessments","interactions":[],"lastModifiedDate":"2022-03-11T13:08:11.334928","indexId":"70229519","displayToPublicDate":"2021-11-11T07:07:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Mineral deposit discovery order and three-part quantitative assessments","docAbstract":"Larger oil pools tending to be discovered earlier in an exploration play suggests the same pattern might exist for mineral deposits and could be used in predicting sizes of undiscovered deposits in mineral assessments. The volume of individual petroleum pools is highly correlated with surface projection area of pools in basins. The gradual additions to individual oil pool reserves over time adds to the appearance of larger pools being discovered earlier.
Comparisons of surface projected areas of mineral deposits to their tonnages showed significant positive relationships in all 10 deposit types analyzed, suggesting that larger deposits should be discovered earlier than small deposits.
Analysis of deposits consistent with three-part mineral assessments identified 9 combinations of mineral deposit types in large regions each containing multiple geological permissive tracts showing negative and 1 positive relationships of deposit size with discovery date significant at the 1% level. Twenty other tests of regions containing multiple permissive settings had either negative or positive relationships, none significantly different from those that might occur by chance. The large regions are mostly based on political boundaries. These results suggest mineral deposit discovery order is not the same as observed in oil pool exploration.
The widely employed three-part quantitative mineral resource assessments are an obvious choice to benefit from patterns of declining deposit sizes with order of discovery. The 30 tests of relationships of discovery dates to deposit sizes demonstrated here were performed with deposits consistent with those in three-part assessments, but the large areas are not consistent with permissive tracts used in these assessments because they also contain substantial non-permissive geology.
In 100 permissive tracts assessed with three-part assessments of multiple deposit types located throughout the world, the median number of known well-explored deposits is 1 and 90 percent of tracts report less than 9 deposits. The number of well-explored deposits in three-part assessed tracts tends to be quite small, limiting any ability to recognize a discovery order versus size relationship.
In a three-part assessment of undiscovered porphyry copper deposits of South America, only 7 of 26 delineated tracts contained more than 2 known deposits and only 1 had a significant negative relationship between tonnage of known deposits and year of discovery (p = 0.04). Most predicted undiscovered deposits in this tract were expected to be under extensive unexplored post-mineralization cover, meaning the general grade and tonnage model should be applied because the discovery order process starts over. Projection of deposit sizes based on discovery order would provide a biased estimate of the undiscovered deposit sizes in this case. Thus, although a discovery order versus size relationship could exist in three-part mineral assessments, only rarely might the pattern be useful to predict sizes of undiscovered deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2021.104566","usgsCitation":"Singer, D., and Zientek, M., 2021, Mineral deposit discovery order and three-part quantitative assessments: Ore Geology Reviews, v. 139, no. Part B, 104566, 9 p., https://doi.org/10.1016/j.oregeorev.2021.104566.","productDescription":"104566, 9 p.","ipdsId":"IP-127845","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467221,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2021.104566","text":"Publisher Index Page"},{"id":397016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396983,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2021.104566"}],"volume":"139","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Singer, Donald A. 0000-0001-6812-6441","orcid":"https://orcid.org/0000-0001-6812-6441","contributorId":288318,"corporation":false,"usgs":false,"family":"Singer","given":"Donald A.","affiliations":[],"preferred":false,"id":837729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zientek, Michael L. 0000-0002-8522-9626","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":210763,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":837728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228315,"text":"70228315 - 2021 - Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system","interactions":[],"lastModifiedDate":"2022-02-08T13:03:32.116162","indexId":"70228315","displayToPublicDate":"2021-11-11T06:59:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10098,"text":"Marine Ecology Progress Series (MEPS)","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system","docAbstract":"The distribution of plankton in the ocean is patchy across a wide range of spatial and temporal scales. One type of oceanographic feature that exemplifies this patchiness is a ‘thin layer’. Thin layers are subsurface aggregations of plankton that range in vertical thickness from centimeters to a few meters, which may extend horizontally for kilometers and persist for days. We undertook a field campaign to characterize the biological, physical, and chemical properties of thin layers in Monterey Bay, California (USA), an area where these features can be persistent. The particle aggregates (marine snow) sampled in the study had several quantifiable properties indicating how the layer was formed and how its structure was maintained. Particles were more elongated above the layer, and then changed orientation angle and increased in size within the layer, suggesting passive accumulation of particles along a physical gradient. The shift in particle aggregate orientation angle near the pycnocline suggests that shear may also have played a role in generating the thin layer. Pseudo-nitzschia spp. were the most abundant phytoplankton within the thin layer. Further, both dissolved and particulate domoic acid were highest within the thin layer. We suggest that phosphate stress is responsible for the formation of Pseudo-nitzschia spp. aggregates. This stress together with increased nitrogen in the layer may lead to increased bloom toxicity in the subsurface blooms of Pseudo-nitzschia spp. Several zooplankton groups were observed to aggregate above and below the layer. With the knowledge that harmful algal bloom events can occur in subsurface thin layers, modified sampling methods to monitor for these hidden incubators could greatly improve the efficacy of early-warning systems designed to detect harmful algal blooms in coastal waters.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13879","usgsCitation":"McManus, M., Greer, A.T., Timmerman, A.H., Sevadjian, J.C., Woodson, C.B., Cowen, R., Fong, D.A., Monismith, S.G., and Cheriton, O.M., 2021, Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system: Marine Ecology Progress Series (MEPS), v. 678, p. 17-35, https://doi.org/10.3354/meps13879.","productDescription":"19 p.","startPage":"17","endPage":"35","ipdsId":"IP-129200","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13879","text":"Publisher Index Page"},{"id":395606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"678","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McManus, Margaret A","contributorId":275122,"corporation":false,"usgs":false,"family":"McManus","given":"Margaret A","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":833672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greer, Adam T","contributorId":275123,"corporation":false,"usgs":false,"family":"Greer","given":"Adam","email":"","middleInitial":"T","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":833673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Timmerman, Amanda HV","contributorId":275126,"corporation":false,"usgs":false,"family":"Timmerman","given":"Amanda","email":"","middleInitial":"HV","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":833674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sevadjian, Jeff C","contributorId":275129,"corporation":false,"usgs":false,"family":"Sevadjian","given":"Jeff","email":"","middleInitial":"C","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":833675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodson, C. Brock","contributorId":275132,"corporation":false,"usgs":false,"family":"Woodson","given":"C.","email":"","middleInitial":"Brock","affiliations":[{"id":56710,"text":"School of ECAM Engineering, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":833676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cowen, Robert","contributorId":275135,"corporation":false,"usgs":false,"family":"Cowen","given":"Robert","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":833677,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fong, Derek A","contributorId":275136,"corporation":false,"usgs":false,"family":"Fong","given":"Derek","email":"","middleInitial":"A","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":833678,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Monismith, Stephen G.","contributorId":196322,"corporation":false,"usgs":false,"family":"Monismith","given":"Stephen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":833679,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833680,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226847,"text":"70226847 - 2021 - Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","interactions":[],"lastModifiedDate":"2021-12-15T12:40:09.70423","indexId":"70226847","displayToPublicDate":"2021-11-11T06:37:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","docAbstract":"The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R
The Buenos Aires National Wildlife Refuge is located in the southern part of Altar Valley, southwest of Tucson in southeastern Arizona. The primary water-supply well at the Buenos Aires National Wildlife Refuge has experienced a two-decade decrease in groundwater levels in the well, as have other wells in the southern part of Altar Valley. In part to understand this trend, a study was undertaken by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to summarize what is known about the geohydrologic system on the refuge and analyze groundwater-level trends and precipitation-groundwater correlations. In addition, available data were compiled where possible on the climate, land cover, soils, geology, and hydrology to provide a foundation for future modeling of the system.
Altar Valley is a sedimentary basin bounded by a mixture of Paleozoic to Tertiary sedimentary, volcanic, granitic, and metamorphic rocks. The valley fill is undifferentiated Tertiary to Quaternary sediments underlain by middle Miocene to Pliocene rocks that consist of moderately to strongly consolidated conglomerate and sandstone. Surface water, when present in the predominantly ephemeral streams of the valley, flows from south to north. Arivaca Creek has a cienega (or wetland) where groundwater surfaces before it flows as a short perennial reach out of Arivaca Basin. Groundwater maps compiled between 1934 and 2016 showed groundwater flowing from south to north. Before the 1980s, temporal patterns of groundwater levels in wells in Altar Valley varied substantially from one well to another. In the mid-1980s, comparatively high levels of precipitation occurred: the 1980s median value was 15.3 inches, whereas the median for the period of record was 13.2 inches. In addition, apparently corresponding groundwater level increases were seen in nearly all wells studied. After this initial increase, two different groundwater-level trends began to be observed in two spatially distinct sets of wells: in the northern part, groundwater levels were relatively steady, whereas in the southern part, groundwater levels declined from 10 to 20 feet between 1990 and 2019. Annual groundwater pumpage declined substantially in the northern part of the valley beginning in the early 1980s, but it began to increase again in the 1990s. Pumpage in the southern part has remained low and relatively steady compared to the northern part. Although the precise reasons for the declining groundwater levels in the southern part remain unclear, groundwater levels may be affected by factors such as climate cycles, long-term drought, and temperature-induced declines in recharge, resulting in increased evapotranspiration.
Preliminary analyses of two wells, one selected from each part of the valley, using linear regression and lag correlation to investigate correlation between annual precipitation and groundwater levels, showed a maximum correlation at a lag of about 17 years in the southern part of the valley and about 25 years in the northern part, indicating that, although variable sources and traveltimes of recharged water may be needed to propagate to each location, the strongest correlation at each well is with precipitation that was recharged 17 and 25 years prior to the groundwater response in that well. Assuming a constant flow of groundwater from the southern to the northern part of the valley, a decrease in recharge is expected to lead to a decrease in aquifer storage. As to the comparatively stable groundwater levels in the northern part, pumpage is still only about one-half what it was in the early 1980s, even though pumpage has increased there since the 1990s. Water levels in most wells in the northern part were drawn down prior to the decrease in pumping in the early 1980s, possibly owing to a combination of pumping and the nearly 20-year midcentury drought that occurred between 1940 and 1960. Water levels were in the process of recovering when the increase in pumping occurred in the 1990s. Because the water levels were recovering (increasing) instead of remaining static, the increased pumping may have only limited the recovery rather than causing a decrease in water levels, as a new quasi-equilibrium state may have been reached. Additional possible causes for the stable groundwater levels include (1) upgradient aquifer transmissivity that was high enough to offset pumping, (2) a low-permeability barrier, such as bedrock or clay, at the north end of the valley that caused groundwater pooling, (3) higher lateral inflow of groundwater in the northern part of the valley, (4) a delay in the effect of storage declines propagating from the south, or (5) some combination thereof.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215050","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Owen-Joyce, S.J., Callegary, J.B., and Rosebrough, A.E., 2021, Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2021–5050, 29 p., https://doi.org/10.3133/sir20215050.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-118417","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":391517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5050/sir20215050.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391518,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QST8OX","linkHelpText":"Groundwater well data and annual groundwater pumpage data (1984–2019) in Altar Valley, Arizona"},{"id":391516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5050/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Altar Valley, Buenos Aires National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.56341552734375,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.459125370764387\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719