Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1314","usgsCitation":"Conn, P.B., Johnson, D., Williams, P.J., Melin, S.R., and Hooten, M., 2018, A guide to Bayesian model checking for ecologists: Ecological Monographs, v. 88, no. 4, p. 526-542, https://doi.org/10.1002/ecm.1314.","productDescription":"17 p.","startPage":"526","endPage":"542","ipdsId":"IP-091408","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":356616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-14","publicationStatus":"PW","scienceBaseUri":"5b98a2c6e4b0702d0e842fe2","contributors":{"authors":[{"text":"Conn, Paul B.","contributorId":87440,"corporation":false,"usgs":true,"family":"Conn","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":743048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":743049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":743050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melin, Sharon R.","contributorId":147080,"corporation":false,"usgs":false,"family":"Melin","given":"Sharon","email":"","middleInitial":"R.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":743051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":742853,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190581,"text":"sir20175100 - 2018 - Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona","interactions":[],"lastModifiedDate":"2019-05-15T09:24:27","indexId":"sir20175100","displayToPublicDate":"2018-05-15T00:00:00","publicationYear":"2018","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":"2017-5100","title":"Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona","docAbstract":"A 3-year study was undertaken to evaluate the suitability of the available modeling tools for characterizing environmental flows in the middle Verde River watershed of central Arizona, describe riparian vegetation throughout the watershed, and estimate sediment mobilization in the river. Existing data on fish and macroinvertebrates were analyzed in relation to basin characteristics, flow regimes, and microhabitat, and a pilot study was conducted that sampled fish and macroinvertebrates and the microhabitats in which they were found. The sampling for the pilot study took place at five different locations in the middle Verde River watershed. This report presents the results of this 3-year study.
The Northern Arizona Groundwater Flow Model (NARGFM) was found to be capable of predicting long-term changes caused by alteration of regional recharge (such as may result from climate variability) and groundwater pumping in gaining, losing, and dry reaches of the major streams in the middle Verde River watershed. Over the period 1910 to 2006, the model simulated an increase in dry reaches, a small increase in reaches losing discharge to the groundwater aquifer, and a concurrent decrease in reaches gaining discharge from groundwater. Although evaluations of the suitability of using the NARGFM and Basin Characteristic Model to characterize various streamflow intervals showed that smallerscale basin monthly runoff could be estimated adequately at locations of interest, monthly stream-flow estimates were found unsatisfactory for determining environmental flows.
Orthoimagery and Moderate Resolution Imaging Spectroradiometer data were used to quantify stream and riparian vegetation properties related to biotic habitat. The relative abundance of riparian vegetation varied along the main channel of the Verde River. As would be expected, more upland plant species and fewer lowland species were found in the upper-middle section compared to the lower-middle section, and vice-versa. Vegetation changes within the upper-middle and lower-middle reaches are related to differences in climate and hydrology. In general, the riparian vegetation of the middle Verde River watershed is that of a healthy ecosystem’s mixed age, mixed patch structure, likely a result of the mostly unaltered disturbance regime.
The frequency of in-river hydrogeomorphic features (pool, riffle, run) varied along the middle Verde River channel. There was a greater abundance of riffle habitat in the upper-middle reach; the lower-middle reach included more pool habitat. The Oak Creek tributary was more homogenous in geomorphic stream habitat composition than West Clear Creek, where runs dominated the upper reaches and pools dominated many of the lower reaches.
On the basis of the period of record and discharges recorded at 15-minute intervals, five flows were found to reach the gravel-transport threshold. Sediment mobilization computed with flows averaged over daily time steps yielded just three flows that reached the gravel-transport threshold, and monthly averaged flows yielded none. In the middle Verde River watershed, 15-minute data should be used when possible to evaluate sediment transport in the river system.
Data from more than 300 fish surveys conducted from 1992 to 2011 were analyzed using two schemes, one that divided the river into five reaches based on basin characteristics, and a second that divided the river into five reaches based on degree of flow alteration (specifically, diversions). Fish community metrics and assemblage data were used to analyze patterns of species composition and abundance in the two approaches. Overall, native and non-native species were regularly interacting and probably competing for similar resources. Fish abundances were also analyzed in response to floods and other flow metrics. Although the data are limited, native fish abundances increased more rapidly than non-native fish abundances in response to large floods. The basin-characteristic reach analysis showed native fish in greater abundance in the upper-middle reaches of the Verde River watershed and generally decreasing with downstream distance. The median relative abundance of native fish decreased by 50 percent from reach 1 to reach 5. Using the reach scheme based on degree of flow alteration, nondiverted reaches were found to have a greater abundance of native fish than diverted reaches. In heavily diverted reaches, non-native species outnumbered native species.
Fish metrics and stream-flow metrics for the 30, 90, and 365-day periods before collection were computed and the results analyzed statistically. Only abundance of all fish species was associated with the 30-day flow metrics. The 90-day flow metrics were generally positively associated with fish metrics, whereas the 365-day flow metrics had more negative correlations. In particular, significant relations were found between fish metrics and the magnitude and frequency of high flows, including maximum monthly flow, median annual number of high-flow events, and median annual maximum streamflow. Native sucker (Catostomidae) populations tended to decrease in periods of extended base flow, and fish in the non-native sunfish family (Centrarchidae) decreased in periods of flashy, high magnitude flows.
A pilot study surveyed fish at five locations in the upper part of the middle Verde River watershed as a means to measure microhabitat availability and quantify native and non-native fish use of that available microhabitat. Results indicated that native and non-native species exhibit some clear differences in microhabitat use. Although at least some native and non-native fish were found in each velocity, depth, and substrate category, preferential microhabitat use was common. On a percentage basis, non-native species had a strong preference for slow-moving and deeper water with silt and sand substrate, with a secondary preference for faster moving and very shallow water and a coarse gravel substrate. Native species showed a general preference for somewhat faster, moderate depth water over coarse gravel and had no clear secondary preference.
Macroinvertebrate-variables index period, high-flow year, and collection location (upper-middle Verde River, lowermiddle Verde River, or Verde River tributaries) were found to be important explanatory variables in differentiating among community metrics. Overall richness (number of unique taxa), Shannon’s diversity index, and the percent of the most dominant taxa were all highly correlated, but their response to each macroinvertebrate variable was different. The percentage of mayfly (order Ephemeroptera) taxa was significantly higher in Oak Creek and the upper-middle and lower-middle Verde River reaches, locations which have higher flows and more urbanization than other reaches. When community metrics were related to hydrologic metrics, caddisfly (order Trichoptera) populations appeared to increase and mayfly populations to decrease in response to less flashy and more stable streamflows. Conversely, caddisfly populations appeared to decrease and mayfly populations to increase in response to greater flow variability.
Six locations along the Verde River were sampled for macroinvertebrates as part of a pilot study associated with this report—(1) below Granite Creek, (2) near Campbell Ranch, (3) at the U.S. Geological Survey Paulden gage, (4) at the Perkinsville Bridge, (5) at the USGS Clarkdale gage, and (6) near the Reitz Ranch property. A nonmetric multidimensional scaling ordination of macroinvertebrate assemblages showed that the Verde River below Granite Creek site was different from the five other sites and that the Perkinsville Bridge and near Reitz Ranch samples had similar community structure. The near Campbell Ranch and Paulden gage locations had similar microhabitat characteristics, with the exception of riparian cover, yet the assemblage structure was very different. The different community composition at Verde River below Granite Creek was likely due to it having the smallest substrate sizes, lowest velocities, shallowest depths, and most riparian cover of the six sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175100","collaboration":"Prepared in cooperation with The Nature Conservancy and Salt River Project","usgsCitation":"Paretti, N.V., Brasher, A.M.D., Pearlstein, S.L., Skow, D.M., Gungle, Bruce, and Garner, B.D., 2018, Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona: U.S. Geological Survey Scientific Investigations Report 2017–5100, 104 p., https://doi.org/10.3133/sir20175100.","productDescription":"Report: xii; 104 p.; 3 Tables","numberOfPages":"120","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084364","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":354141,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table14.csv","text":"Table 14","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"},{"id":354142,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_tables12_14.xlsx","text":"Table 12 and 14","size":"25 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Scientific Investigation Report 2017-5100 Table 12 and 14 Excel file"},{"id":354139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigation Report 2017-5100"},{"id":354138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5100/coverthb.jpg"},{"id":354140,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table12.csv","text":"Table 12","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 34.5\n ],\n [\n -112.5,\n 34.5\n ],\n [\n -112.5,\n 35.5\n ],\n [\n -111.5,\n 35.5\n ],\n [\n -111.5,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Crab Orchard Lake in southern Illinois is one of the largest and most popular recreational lakes in the state. Construction of the nearly 7,000-acre reservoir in the late 1930s created employment opportunities through the Works Progress Administration, and the lake itself was intended to supply water, control flooding, and provide recreational opportunities for local communities (Stall, 1954). In 1942, the Department of War appropriated or purchased more than 20,000 acres of land around Crab Orchard Lake and constructed the Illinois Ordnance Plant, which manufactured bombs and anti-tank mines during World War II. After the war, an Act of Congress transferred the property to the U.S. Department of the Interior. Crab Orchard National Wildlife Refuge was established on August 5, 1947, for the joint purposes of wildlife conservation, agriculture, recreation, and industry. Production of explosives continued, but new industries also moved onsite. More than 200 tenants have held leases with Crab Orchard National Wildlife Refuge and have operated a variety of manufacturing plants (electrical components, plated metal parts, ink, machined parts, painted products, and boats) on-site. Soils, water, and sediments in several areas of the refuge were contaminated with hazardous substances from handling and disposal methods that are no longer acceptable environmental practice (for example, direct discharge to surface water, use of unlined landfills).
Polychlorinated biphenyl (PCB) contamination at the refuge was identified in the 1970s, and a PCB-based fish-consumption advisory has been in effect since 1988 for Crab Orchard Lake. The present advisory covers common carp (Cyprinus carpio) and channel catfish (Ictalurus punctatus); see Illinois Department of Public Health (2017). Some of the most contaminated areas of the refuge were actively remediated, and natural ecosystem recovery processes are expected to further reduce residual PCB concentrations in the lake. The U.S. Fish and Wildlife Service sought technical support to understand environmental drivers of current (2017) PCB residues in fish tissue and patterns in PCB residues through time to inform the fish-consumption advisory for Crab Orchard Lake. This project is planned in two phases (Tasks 1 and 2); the first phase is included in this report.
When Task 1 and Task 2 are complete, resource managers will have (a) a synthesis of existing literature that characterizes the processes influencing the fate of residual PCBs remaining in systems such as Crab Orchard Lake, (b) a summary of natural PCB attenuation processes for use in risk communication with the public, and (c) a summary of existing data on PCBs in fish tissues from Crab Orchard Lake, including exploratory plots of tissue residues through time. Overall, this project will provide data to help resource managers better understand the ecological and public health consequences of residual PCBs in Crab Orchard Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181006","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Kunz, B.K., Hinck, J.E., Calfee, R.D., Linder, G.L., and Little, E.E., 2017, Science support for evaluating natural recovery of polychlorinated biphenyl concentrations in fish from Crab Orchard Lake, Crab Orchard National Wildlife Refuge, Illinois: U.S. Geological Survey Open-File Report 2018–1006, 20 p., https://doi.org/10.3133/ofr20181006.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-084872","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":353853,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1006/ofr20181006.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1006"},{"id":353852,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1006/coverthb2.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Crab Orchard Lake, Crab Orchard 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 -89.025,\n 37.6833\n ],\n [\n -89.0083,\n 37.6833\n ],\n [\n -89.0083,\n 37.6958\n ],\n [\n -89.025,\n 37.6958\n ],\n [\n -89.025,\n 37.6833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
The ability to model freshwater stream habitat and species distributions is limited by the spatially sparse flow data available from long-term gauging stations. Flow data beyond the immediate vicinity of gauging stations would enhance our ability to explore and characterize hydrologic habitat suitability. The southeastern USA supports high aquatic biodiversity, but threats, such as landuse alteration, climate change, conflicting water-resource demands, and pollution, have led to the imperilment and legal protection of many species. The ability to distinguish suitable from unsuitable habitat conditions, including hydrologic suitability, is a key criterion for successful conservation and restoration of aquatic species. We used the example of the critically endangered Tar River Spinymussel (Parvaspina steinstansana) and associated species to demonstrate the value of modeled flow data (WaterFALL™) to generate novel insights into population structure and testable hypotheses regarding hydrologic suitability. With ordination models, we: 1) identified all catchments with potentially suitable hydrology, 2) identified 2 distinct hydrologic environments occupied by the Tar River Spinymussel, and 3) estimated greater hydrological habitat niche breadth of assumed surrogate species associates at the catchment scale. Our findings provide the first demonstrated application of complete, continuous, regional modeled hydrologic data to freshwater mussel distribution and management. This research highlights the utility of modeling and data-mining methods to facilitate further exploration and application of such modeled environmental conditions to inform aquatic species management. We conclude that such an approach can support landscape-scale management decisions that require spatial information at fine resolution (e.g., enhanced National Hydrology Dataset catchments) and broad extent (e.g., multiple river basins).
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/697947","usgsCitation":"Drew, C.A., Eddy, M., Kwak, T.J., Cope, W., and Augspurger, T., 2018, Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows: Freshwater Science, v. 37, no. 2, p. 343-356, https://doi.org/10.1086/697947.","productDescription":"14 p.","startPage":"343","endPage":"356","ipdsId":"IP-095471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","volume":"37","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6bee4b0da30c1bfbd94","contributors":{"authors":[{"text":"Drew, C. Ashton","contributorId":140953,"corporation":false,"usgs":false,"family":"Drew","given":"C.","email":"","middleInitial":"Ashton","affiliations":[],"preferred":false,"id":735242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy, Michele","contributorId":198941,"corporation":false,"usgs":false,"family":"Eddy","given":"Michele","email":"","affiliations":[],"preferred":false,"id":735243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":735244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Augspurger, Tom","contributorId":189894,"corporation":false,"usgs":false,"family":"Augspurger","given":"Tom","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":735245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196888,"text":"70196888 - 2018 - Respiratory disease, behavior, and survival of mountain goat kids","interactions":[],"lastModifiedDate":"2018-07-23T13:00:20","indexId":"70196888","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Respiratory disease, behavior, and survival of mountain goat kids","docAbstract":"Bacterial pneumonia is a threat to bighorn sheep (Ovis canadensis) populations. Bighorn sheep in the East Humboldt Mountain Range (EHR), Nevada, USA, experienced a pneumonia epizootic in 2009–2010. Testing of mountain goats (Oreamnos americanus) that were captured or found dead on this range during and after the epizootic detected bacteria commonly associated with bighorn sheep pneumonia die‐offs. Additionally, in years subsequent to the bighorn sheep epizootic, the mountain goat population had low kid:adult ratios, a common outcome for bighorn sheep populations that have experienced a pneumonia epizootic. We hypothesized that pneumonia was present and negatively affecting mountain goat kids in the EHR. From June–August 2013–2015, we attempted to observe mountain goat kids with marked adult females in the EHR at least once per week to document signs of respiratory disease; identify associations between respiratory disease, activity levels, and subsequent disappearance (i.e., death); and estimate weekly survival. Each time we observed a kid with a marked adult female, we recorded any signs of respiratory disease and collected behavior data that we fit to a 3‐state discrete hidden Markov model (HMM) to predict a kid's state (active vs. sedentary) and its probability of disappearing. We first observed clinical signs of respiratory disease in kids in late July–early August each summer. We observed 8 of 31 kids with marked adult females with signs of respiratory disease on 13 occasions. On 11 of these occasions, the HMM predicted that kids were in the sedentary state, which was associated with increased probability of subsequent death. We estimated overall probability of kid survival from June–August to be 0.19 (95% CI = 0.08–0.38), which was lower than has been reported in other mountain goat populations. We concluded that respiratory disease was present in the mountain goat kids in the EHR and negatively affected their activity levels and survival. Our results raise concerns about potential effects of pneumonia to mountain goat populations and the potential for disease transmission between mountain goats and bighorn sheep where the species are sympatric.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21470","usgsCitation":"Blanchong, J.A., Anderson, C.A., Clark, N.J., Klaver, R.W., Plummer, P.J., Cox, M., Mcadoo, C., and Wolff, P.L., 2018, Respiratory disease, behavior, and survival of mountain goat kids: Journal of Wildlife Management, v. 82, no. 6, p. 1243-1251, https://doi.org/10.1002/jwmg.21470.","productDescription":"9 p.","startPage":"1243","endPage":"1251","ipdsId":"IP-094396","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487211,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/276","text":"External Repository"},{"id":354147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5afee6bee4b0da30c1bfbd96","contributors":{"authors":[{"text":"Blanchong, Julie A.","contributorId":6030,"corporation":false,"usgs":false,"family":"Blanchong","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":13018,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":735235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christopher A.","contributorId":204866,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":735236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Nicholas J.","contributorId":204867,"corporation":false,"usgs":false,"family":"Clark","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":16755,"text":"University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":735237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734914,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plummer, Paul J.","contributorId":204868,"corporation":false,"usgs":false,"family":"Plummer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cox, Mike","contributorId":198457,"corporation":false,"usgs":false,"family":"Cox","given":"Mike","email":"","affiliations":[],"preferred":false,"id":735239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mcadoo, Caleb","contributorId":204869,"corporation":false,"usgs":false,"family":"Mcadoo","given":"Caleb","email":"","affiliations":[],"preferred":false,"id":735240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolff, Peregrine L.","contributorId":69865,"corporation":false,"usgs":true,"family":"Wolff","given":"Peregrine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":735241,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70196883,"text":"70196883 - 2018 - Landscape‐level patterns in fawn survival across North America","interactions":[],"lastModifiedDate":"2018-07-03T11:19:38","indexId":"70196883","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Landscape‐level patterns in fawn survival across North America","docAbstract":"A landscape‐level meta‐analysis approach to examining early survival of ungulates may elucidate patterns in survival not evident from individual studies. Despite numerous efforts, the relationship between fawn survival and habitat characteristics remains unclear and there has been no attempt to examine trends in survival across landscape types with adequate replication. In 2015–2016, we radiomarked 98 white‐tailed deer (Odocoileus virginianus) fawns in 2 study areas in Pennsylvania. By using a meta‐analysis approach, we compared fawn survival estimates from across North America using published data from 29 populations in 16 states to identify patterns in survival and cause‐specific mortality related to landscape characteristics, predator communities, and deer population density. We modeled fawn survival relative to percentage of agricultural land cover and deer density. Estimated average survival to 3–6 months of age was 0.414 ± 0.062 (SE) in contiguous forest landscapes (no agriculture) and for every 10% increase in land area in agriculture, fawn survival increased 0.049 ± 0.014. We classified cause‐specific mortality as human‐caused, natural (excluding predation), and predation according to agriculturally dominated, forested, and mixed (i.e., both agricultural and forest cover) landscapes. Predation was the greatest source of mortality in all landscapes. Landscapes with mixed forest and agricultural cover had greater proportions and rates of human‐caused mortalities, and lower proportions and rates of mortality due to predators, when compared to forested landscapes. Proportion and rate of natural deaths did not differ among landscapes. We failed to detect any relationship between fawn survival and deer density. The results highlight the need to consider multiple spatial scales when accounting for factors that influence fawn survival. Furthermore, variation in mortality sources and rates among landscapes indicate the potential for altered landscape mosaics to influence fawn survival rates. Wildlife managers can use the meta‐analysis to identify factors that will facilitate comparisons of results among studies and advance a better understanding of patterns in fawn survival.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21456","usgsCitation":"Gingery, T.M., Diefenbach, D.R., Wallingford, B.D., and Rosenberry, C.S., 2018, Landscape‐level patterns in fawn survival across North America: Journal of Wildlife Management, v. 82, no. 5, p. 1003-1013, https://doi.org/10.1002/jwmg.21456.","productDescription":"11 p.","startPage":"1003","endPage":"1013","ipdsId":"IP-092016","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbd9a","contributors":{"authors":[{"text":"Gingery, Tess M.","contributorId":204865,"corporation":false,"usgs":false,"family":"Gingery","given":"Tess","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":735227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallingford, Bret D.","contributorId":171632,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":735228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberry, Christopher S.","contributorId":171633,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":735229,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196943,"text":"70196943 - 2018 - Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides","interactions":[],"lastModifiedDate":"2018-08-31T10:59:53","indexId":"70196943","displayToPublicDate":"2018-05-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2432,"text":"Journal of Plant Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides","title":"Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides","docAbstract":"Codominant marker systems are better suited to analyze population structure and assess the source of an individual in admixture analyses. Currently, there is no codominant marker system using microsatellites developed for the sea sandwort, Honckenya peploides (L.) Ehrh., an early colonizer in island systems. We developed and characterized novel microsatellite loci from H. peploides, using reads collected from whole genome shotgun sequencing on a 454 platform. The combined output from two shotgun runs yielded a total of 62,669 reads, from which 58 loci were screened. We identified 12 polymorphic loci that amplified reliably and exhibited disomic inheritance. Microsatellite data were collected and characterized for the 12 polymorphic loci in two Alaskan populations of H. peploides: Fossil Beach, Kodiak Island (n = 32) and Egg Bay, Atka Island (n = 29). The Atka population exhibited a slightly higher average number of alleles (3.9) and observed heterozygosity (0.483) than the Kodiak population (3.3 and 0.347, respectively). The overall probability of identity values for both populations was PID = 2.892e−6 and PIDsib = 3.361e−3. We also screened the 12 polymorphic loci in Wilhelmsia physodes (Fisch. ex Ser.) McNeill, the most closely related species to H. peploides, and only one locus was polymorphic. These microsatellite markers will allow future investigations into population genetic and colonization patterns of the beach dune ruderal H. peploides on new and recently disturbed islands.
","language":"English","publisher":"Springer","doi":"10.1007/s10265-018-1036-7","usgsCitation":"Gravley, M.C., Sage, G.K., Talbot, S.L., and Carlson, M.L., 2018, Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides: Journal of Plant Research, v. 131, no. 5, p. 879-885, https://doi.org/10.1007/s10265-018-1036-7.","productDescription":"7 p.","startPage":"879","endPage":"885","ipdsId":"IP-092972","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XRP","text":"USGS data release","linkHelpText":"Microsatellite Genetic Data for Sea Sandwort (Honckenya peploides) and Merckia (Wilhelmsia physodes), Alaska 2009-2016"},{"id":354106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Atka Island, Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.775390625,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 56.68037378950137\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.3802490234375,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 51.984880139916626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-23","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbda2","contributors":{"authors":[{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":735100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":735101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":735102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson, Matthew L.","contributorId":138686,"corporation":false,"usgs":false,"family":"Carlson","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":12492,"text":"UAA Alaska Natural Heritage Program & Biological Sciences Department","active":true,"usgs":false}],"preferred":false,"id":735103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196826,"text":"sir20185051 - 2018 - Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year","interactions":[],"lastModifiedDate":"2018-05-14T11:09:15","indexId":"sir20185051","displayToPublicDate":"2018-05-11T12:30:00","publicationYear":"2018","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":"2018-5051","title":"Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year","docAbstract":"Streamflow and nutrient concentration data needed to compute nitrogen and phosphorus loads were compiled from Federal, State, Provincial, and local agency databases and also from selected university databases. The nitrogen and phosphorus loads are necessary inputs to Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models are a way to estimate the distribution, sources, and transport of nutrients in streams throughout the Midcontinental region of Canada and the United States. After screening the data, approximately 1,500 sites sampled by 34 agencies were identified as having suitable data for calculating the long-term mean-annual nutrient loads required for SPARROW model calibration. These final sites represent a wide range in watershed sizes, types of nutrient sources, and land-use and watershed characteristics in the Midcontinental region of Canada and the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185051","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Saad, D.A., Benoy, G.A., and Robertson, D.M., 2018, Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year: U.S. Geological Survey Scientific Investigations Report 2018–5051, 14 p., https://doi.org/10.3133/sir20185051.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084092","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":353945,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VT1R1K","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating loads considered for use in the 2002 Midcontinent nutrient SPARROW models, United States and Canada, 1970-2012"},{"id":353943,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5051/sir20185051.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5051"},{"id":353931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5051/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.32617187499999,\n 49.26780455063753\n ],\n [\n -94.3505859375,\n 42.94033923363181\n ],\n [\n -91.7578125,\n 39.16414104768742\n ],\n [\n -88.9013671875,\n 36.63316209558658\n ],\n [\n -86.572265625,\n 35.17380831799959\n ],\n [\n -81.7822265625,\n 37.82280243352756\n ],\n [\n -79.5849609375,\n 40.17887331434696\n ],\n [\n -77.16796875,\n 42.293564192170095\n ],\n [\n -74.794921875,\n 43.739352079154706\n ],\n [\n -75.6298828125,\n 44.933696389694674\n ],\n [\n -78.44238281249999,\n 45.1510532655634\n ],\n [\n -80.2001953125,\n 46.58906908309182\n ],\n [\n -82.2216796875,\n 47.368594345213374\n ],\n [\n -84.287109375,\n 49.781264058178344\n ],\n [\n -87.2314453125,\n 50.233151832472245\n ],\n [\n -90.52734374999999,\n 50.708634400828224\n ],\n [\n -95.1416015625,\n 50.401515322782366\n ],\n [\n -99.66796875,\n 50.064191736659104\n ],\n [\n -100.986328125,\n 51.45400691005982\n ],\n [\n -103.4912109375,\n 51.536085601784755\n ],\n [\n -102.74414062499999,\n 49.97948776108648\n ],\n [\n -104.32617187499999,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Context
Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.
Objective
To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.
Methods
Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.
Results
All connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.
Conclusions
CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.
Groundwater-quality samples and water-level data were collected from 36 wells in the Jerome/Gooding County area of the eastern Snake River Plain aquifer during June 2017. The wells included 30 wells sampled for the U.S. Geological Survey’s National Water-Quality Assessment project, plus an additional 6 wells were selected to increase spatial distribution. The data provide water managers with the ability for an improved understanding of groundwater quality and flow directions in the area. Groundwater-quality samples were analyzed for nutrients, major ions, trace elements, and stable isotopes of water. Quality-assurance and quality-control measures consisted of multiple blank samples and a sequential replicate sample. All data are available online at the USGS National Water Information System.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1085","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources and Idaho Power Company","usgsCitation":"Skinner, K.D., 2018, Groundwater-quality data from the eastern Snake River Plain aquifer, Jerome and Gooding Counties, south-central Idaho, 2017: U.S. Geological Survey Data Series 1085, 20 p., https://doi.org/10.3133/ds1085.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-093930","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1085/ds1085.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1085"},{"id":354091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1085/coverthb.jpg"}],"country":"United States","state":"Idaho","county":"Gooding County, Jerome County","otherGeospatial":"Snake River Plain Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.33,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.917212160086194\n ],\n [\n -115,\n 42.917212160086194\n ],\n [\n -115,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.56218435232186\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
This is the ninth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In fiscal year (FY) 2016, there were 26 active USGS WLCI science-based projects. Of these 26 projects, one project was new for FY2016, and three were completed by the end of the fiscal year (though final products were still in preparation or review). USGS WLCI projects were grouped under five categories: (1) Baseline Synthesis, (2) Long-Term Monitoring, (3) Effectiveness Monitoring, (4) Mechanistic Studies of Wildlife, and (5) Data and Information Management. Each of these topic areas is designed to address WLCI management needs: identifying key drivers of change, identifying the condition and distribution of key wildlife species and habitats and of species’ habitat requirements, development of an integrated inventory and monitoring strategy, use of emerging technologies and development and testing of innovative methods for maximizing the efficiency and efficacy of monitoring efforts, evaluating the effectiveness of habitat treatment projects, evaluating the responses of wildlife to development, and developing a data clearinghouse and information management framework to support and provide access to results of most USGS WLCI projects.
In FY2016, we assisted with updating the WLCI Conservation Action Plan and associated databases as part of the Comprehensive Assessment, and we also assisted with the Bureau of Land Management 2015 WLCI annual report. By the end of FY2016, we completed or had nearly completed assessments of WLCI energy and mineral resources and had submitted a manuscript on modeled effects of oil and gas development on wildlife to a peer-reviewed journal. We also initiated a study on the effects of wind energy on wildlife in the WLCI region. A USGS circular on WLCI long-term monitoring was in review at the end of the fiscal year, and seven projects monitoring water and vegetation (including changes in sagebrush cover and patterns of sagebrush mortality) continued through the year. USGS scientists continued many projects in FY2016 that evaluate the effectiveness of habitat conservation actions (including sagebrush, cheatgrass, and aspen habitat treatments) and provide tools in support of mechanistic studies of wildlife. In FY2016, USGS scientists, along with university and State partners, continued work on five focal wildlife species/communities (pygmy rabbits [Brachylagus idahoensis], greater sage grouse , mule deer, sagebrush songbirds, and native fish). In FY2016, the USGS Information Management Team presented information to WLCI scientists on how USGS tools and resources can be used to fulfill the requirements of new USGS policies regarding data release, data management, and data visualization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181048","usgsCitation":"Bowen, Z.H., Aikens, E., Aldridge, C.L., Anderson, P.J., Assal, T.J., Chalfoun, A.D., Chong, G.W., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Johnston, A., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Walters, A.W., Wheeler, J.D., Wieferich, D., Wilson, A.B., Wyckoff, T.B., and Zeigenfuss, L.C., 2018, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2016 annual report: U.S. Geological Survey Open-File Report 2018–1048, 49 p., https://doi.org/10.3133/ofr20181048.","productDescription":"vii, 49 p.","onlineOnly":"Y","ipdsId":"IP-091604","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science 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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Here, we report the complete mitochondrial genome of the endangered Hine’s emerald dragonfly (HED), Somatochlora hineana Williamson. Data were generated via next generation sequencing (NGS) and assembled using a mitochondrial baiting and iterative mapping approach. The full length circular genome is 15,705 bp with 26.6% GC content. It contains the typical metazoan set of 37 genes: 13 protein-coding genes, 22 transfer RNA (tRNA) and 2 ribosomal RNA (rRNA) genes, and an A + T-rich control region. To our knowledge, this is the first report of the complete HED mitogenome.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23802359.2018.1463824","usgsCitation":"Jackson, C., McCalla, S.G., Amberg, J., Soluk, D., and Britten, H., 2018, The complete mitochondrial genome of Hine’s emerald dragonfly (Somatochlora hineana Williamson) via NGS sequencing: Mitochondrial DNA Part B, v. 3, no. 2, p. 562-563, https://doi.org/10.1080/23802359.2018.1463824.","productDescription":"2 p.","startPage":"562","endPage":"563","ipdsId":"IP-093895","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23802359.2018.1463824","text":"Publisher Index Page"},{"id":359607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","scienceBaseUri":"5bf52b6ae4b045bfcae2800e","contributors":{"authors":[{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soluk, Daniel","contributorId":204438,"corporation":false,"usgs":false,"family":"Soluk","given":"Daniel","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733929,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Hugh","contributorId":204439,"corporation":false,"usgs":false,"family":"Britten","given":"Hugh","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733930,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202574,"text":"70202574 - 2018 - An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries","interactions":[],"lastModifiedDate":"2019-03-12T10:27:17","indexId":"70202574","displayToPublicDate":"2018-05-10T10:27:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries","docAbstract":"The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1471545","usgsCitation":"Schaeffer, B.A., Iiames, J., Dwyer, J.L., Urquhart, E., Salls, W., Rover, J., and Seegers, B., 2018, An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries: International Journal of Remote Sensing, v. 39, no. 22, p. 7789-7805, https://doi.org/10.1080/01431161.2018.1471545.","productDescription":"17 p.","startPage":"7789","endPage":"7805","ipdsId":"IP-096965","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1471545","text":"Publisher Index Page"},{"id":362002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"39","issue":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iiames, John","contributorId":214110,"corporation":false,"usgs":false,"family":"Iiames","given":"John","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":759164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin","contributorId":214111,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salls, Wilson","contributorId":214112,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seegers, Bridget","contributorId":214113,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":759170,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196921,"text":"70196921 - 2018 - Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA","interactions":[],"lastModifiedDate":"2018-05-10T13:45:44","indexId":"70196921","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5015,"text":"Canadian Wildlife Biology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA","docAbstract":"Canada lynx (Lynx canadensis) are found in boreal forests of Canada and Alaska and range southward into the contiguous United States. Much less is understood about lynx in their southern range compared to northern populations. Because lynx are currently listed as threatened under the US Endangered Species Act but have recently been recommended for delisting, information on their southern populations is important for lynx recovery, conservation, and management. We used non-invasive, genetic data collected during lynx snowtracking surveys from 2012-2017 to generate core-area estimates of abundance, trend and density in selected core areas of the Superior National Forest of Minnesota, USA. Lynx abundance estimates averaged 41.8 (SD=14.7, range=24-67) during 2012-2017 in the smaller\ncore areas and averaged 52.3 (SD=8.3, range=43-59) during 2015-2017 in the larger core areas. We found no evidence for a decrease or increase in abundance during either period. Lynx density estimates were approximately 7-10 times lower than densities of lynx in northern populations at the low of the snowshoe hare (Lepus americanus) population cycle. To our knowledge, our results are the first attempt to estimate abundance, trend and density of lynx in Minnesota using non-invasive genetic capture-mark-recapture. Estimates such as ours provide useful benchmarks for future comparisons by providing a context with which to assess 1) potential changes in forest management that may affect lynx recovery and conservation, and 2) possible effects of climate change on the depth, density, and duration of annual snow cover and correspondingly, potential effects on snowshoe hares as well.","language":"English","publisher":"Alpha Wildlife Publications","usgsCitation":"Barber-Meyer, S., Ryan, D., Grosshuesch, D., Catton, T., and Malick-Wahls, S., 2018, Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA: Canadian Wildlife Biology and Management, v. 7, no. 1, p. 46-55.","productDescription":"10 p.","startPage":"46","endPage":"55","ipdsId":"IP-092810","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research 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Daniel","contributorId":204781,"corporation":false,"usgs":false,"family":"Ryan","given":"Daniel","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":734998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosshuesch, David","contributorId":204782,"corporation":false,"usgs":false,"family":"Grosshuesch","given":"David","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":734999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Catton, Timothy","contributorId":204783,"corporation":false,"usgs":false,"family":"Catton","given":"Timothy","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":735000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Malick-Wahls, Sarah","contributorId":204784,"corporation":false,"usgs":false,"family":"Malick-Wahls","given":"Sarah","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":735001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196155,"text":"sir20185046 - 2018 - Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","interactions":[],"lastModifiedDate":"2018-09-25T05:33:19","indexId":"sir20185046","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","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":"2018-5046","title":"Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","docAbstract":"This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both.
Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described.
Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency-
analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.
Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
A challenge for making conservation decisions is predicting how wildlife populations respond to multiple, concurrent threats and potential management strategies, usually under substantial uncertainty. Integrated modeling approaches can improve estimation of demographic rates necessary for making predictions, even for rare or cryptic species with sparse data, but their use in management applications is limited. We developed integrated models for a population of diamondback terrapins (Malaclemys terrapin) impacted by road-associated threats to (i) jointly estimate demographic rates from two mark-recapture datasets, while directly estimating road mortality and the impact of management actions deployed during the study; and (ii) project the population using population viability analysis under simulated management strategies to inform decision-making. Without management, population extirpation was nearly certain due to demographic impacts of road mortality, predators, and vegetation. Installation of novel flashing signage increased survival of terrapins that crossed roads by 30%. Signage, along with small roadside barriers installed during the study, increased population persistence probability, but the population was still predicted to decline. Management strategies that included actions targeting multiple threats and demographic rates resulted in the highest persistence probability, and roadside barriers, which increased adult survival, were predicted to increase persistence more than other actions. Our results support earlier findings showing mitigation of multiple threats is likely required to increase the viability of declining populations. Our approach illustrates how integrated models may be adapted to use limited data efficiently, represent system complexity, evaluate impacts of threats and management actions, and provide decision-relevant information for conservation of at-risk populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2018.03.023","usgsCitation":"Crawford, B.A., Moore, C.T., Norton, T., and Maerz, J.C., 2018, Integrated analysis for population estimation, management impact evaluation, and decision-making for a declining species: Biological Conservation, v. 222, p. 33-43, https://doi.org/10.1016/j.biocon.2018.03.023.","productDescription":"11 p.","startPage":"33","endPage":"43","ipdsId":"IP-083097","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"222","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c1e4b0da30c1bfbdc0","contributors":{"authors":[{"text":"Crawford, Brian A.","contributorId":204802,"corporation":false,"usgs":false,"family":"Crawford","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":735035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norton, Terry M.","contributorId":71020,"corporation":false,"usgs":true,"family":"Norton","given":"Terry M.","affiliations":[],"preferred":false,"id":735036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maerz, John C.","contributorId":171763,"corporation":false,"usgs":false,"family":"Maerz","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":735037,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227678,"text":"70227678 - 2018 - Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA","interactions":[],"lastModifiedDate":"2022-01-26T16:42:09.642427","indexId":"70227678","displayToPublicDate":"2018-05-09T10:37:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA","title":"Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA","docAbstract":"Wildlife–vehicle collisions are a human safety issue and may negatively impact wildlife populations. Most wildlife–vehicle collision studies predict high-risk road segments using only collision data. However, these data lack biologically relevant information such as wildlife population densities and successful road-crossing locations. We overcome this shortcoming with a new method that combines successful road crossings with vehicle collision data, to identify road segments that have both high biological relevance and high risk. We used moose (Alces americanus) road-crossing locations from 20 moose collared with Global Positioning Systems as well as moose–vehicle collision (MVC) data in the state of Massachusetts, USA, to create multi-scale resource selection functions. We predicted the probability of moose road crossings and MVCs across the road network and combined these surfaces to identify road segments that met the dual criteria of having high biological relevance and high risk for MVCs. These road segments occurred mostly on larger roadways in natural areas and were surrounded by forests, wetlands, and a heterogenous mix of land cover types. We found MVCs resulted in the mortality of 3% of the moose population in Massachusetts annually. Although there have been only three human fatalities related to MVCs in Massachusetts since 2003, the human fatality rate was one of the highest reported in the literature. The rate of MVCs relative to the size of the moose population and the risk to human safety suggest a need for road mitigation measures, such as fencing, animal detection systems, and large mammal-crossing structures on roadways in Massachusetts.
","language":"English","publisher":"Springer Link","doi":"10.1007/s00267-018-1058-x","usgsCitation":"Zeller, K., Wattles, D., and Destefano, S., 2018, Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA: Environmental Management, v. 62, p. 518-528, https://doi.org/10.1007/s00267-018-1058-x.","productDescription":"11 p.","startPage":"518","endPage":"528","ipdsId":"IP-068512","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"62","noUsgsAuthors":false,"publicationDate":"2018-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeller, Katherine 0000-0002-2913-6660","orcid":"https://orcid.org/0000-0002-2913-6660","contributorId":255403,"corporation":false,"usgs":false,"family":"Zeller","given":"Katherine","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":831702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wattles, David","contributorId":255402,"corporation":false,"usgs":false,"family":"Wattles","given":"David","affiliations":[{"id":51525,"text":"Massachusetts Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":831703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Destefano, Stephen 0000-0003-2472-8373","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":272197,"corporation":false,"usgs":true,"family":"Destefano","given":"Stephen","email":"","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196839,"text":"ofr20181079 - 2018 - Compilation and analysis of multiple groundwater-quality datasets for Idaho","interactions":[],"lastModifiedDate":"2018-05-14T10:25:12","indexId":"ofr20181079","displayToPublicDate":"2018-05-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1079","title":"Compilation and analysis of multiple groundwater-quality datasets for Idaho","docAbstract":"Groundwater is an important source of drinking and irrigation water throughout Idaho, and groundwater quality is monitored by various Federal, State, and local agencies. The historical, multi-agency records of groundwater quality include a valuable dataset that has yet to be compiled or analyzed on a statewide level. The purpose of this study is to combine groundwater-quality data from multiple sources into a single database, to summarize this dataset, and to perform bulk analyses to reveal spatial and temporal patterns of water quality throughout Idaho. Data were retrieved from the Water Quality Portal (https://www.waterqualitydata.us/), the Idaho Department of Environmental Quality, and the Idaho Department of Water Resources. Analyses included counting the number of times a sample location had concentrations above Maximum Contaminant Levels (MCL), performing trends tests, and calculating correlations between water-quality analytes. The water-quality database and the analysis results are available through USGS ScienceBase (https://doi.org/10.5066/F72V2FBG).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181079","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality","usgsCitation":"Hundt, S.A., and Hopkins, C.B., 2018, Compilation and analysis of multiple groundwater-quality datasets for Idaho: U.S. Geological Survey Open-File Report 2018-1079, 3 p., plus presentation, https://doi.org/10.3133/ofr20181079.","productDescription":"Report: iv, 3 p.; Presentation: 46 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088346","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354031,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1079/ofr20181079.pdf","text":"Report","size":"181 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1079"},{"id":354032,"rank":3,"type":{"id":2,"text":"Additional Report 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\"}}]}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Submarine groundwater fluxes across the seafloor facilitate important hydrological and biogeochemical exchanges between oceans and seabed sediment, yet few studies have investigated spatially distributed groundwater fluxes in deep‐ocean environments such as continental slopes. Heat has been previously applied as a submarine groundwater tracer using an analytical solution to a heat flow equation assuming steady state conditions and homogeneous thermal conductivity. These assumptions are often violated in shallow seabeds due to ocean bottom temperature changes or sediment property variations. Here heat tracing analysis techniques recently developed for terrestrial settings are applied in concert to examine the influences of groundwater flow, ocean temperature changes, and seabed thermal conductivity variations on deep‐ocean sediment temperature profiles. Temperature observations from the sediment and bottom ocean water on the Scotian Slope off eastern Canada are used to demonstrate how simple thermal methods for tracing groundwater can be employed if more comprehensive techniques indicate that the simplifying assumptions are valid. The spatial distribution of the inferred groundwater fluxes on the slope suggests a downward groundwater flow system with recharge occurring over the upper‐middle slope and discharge on the lower slope. We speculate that the downward groundwater flow inferred on the Scotian Slope is due to density‐driven processes arising from underlying salt domes, in contrast with upward slope systems driven by geothermal convection. Improvements in the design of future submarine hydrogeological studies are proposed for thermal data collection and groundwater flow analysis, including new equations that quantify the minimum detectable flux magnitude for a given sensor accuracy and profile length.
","language":"English","publisher":"Wiley","doi":"10.1029/2017WR022353","usgsCitation":"Kurylyk, B.L., Irvine, D.J., Mohammed, A., Bense, V.F., Briggs, M.A., Loder, J., and Geshelin, Y., 2018, Rethinking the use of seabed sediment temperature profiles to trace submarine groundwater flow: Water Resources Research, v. 54, no. 7, p. 4595-4614, https://doi.org/10.1029/2017WR022353.","productDescription":"20 p.","startPage":"4595","endPage":"4614","ipdsId":"IP-095613","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017wr022353","text":"Publisher Index Page"},{"id":360328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Scotian Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63,\n 41\n ],\n [\n -60,\n 41\n ],\n [\n -60,\n 43\n ],\n [\n -63,\n 43\n ],\n [\n -63,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-07","publicationStatus":"PW","scienceBaseUri":"5c14cfb8e4b006c4f8545d3f","contributors":{"authors":[{"text":"Kurylyk, Barret L.","contributorId":176296,"corporation":false,"usgs":false,"family":"Kurylyk","given":"Barret","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":754277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":754278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, A.A.","contributorId":211492,"corporation":false,"usgs":false,"family":"Mohammed","given":"A.A.","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":754279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bense, V. F.","contributorId":211493,"corporation":false,"usgs":false,"family":"Bense","given":"V.","email":"","middleInitial":"F.","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":754280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loder, J.W.","contributorId":211494,"corporation":false,"usgs":false,"family":"Loder","given":"J.W.","email":"","affiliations":[{"id":38259,"text":"Bedford Institute of Oceanography, Dartmouth, Canada","active":true,"usgs":false}],"preferred":false,"id":754281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Geshelin, Y.","contributorId":211495,"corporation":false,"usgs":false,"family":"Geshelin","given":"Y.","email":"","affiliations":[{"id":38259,"text":"Bedford Institute of Oceanography, Dartmouth, Canada","active":true,"usgs":false}],"preferred":false,"id":754282,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206538,"text":"70206538 - 2018 - A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","interactions":[],"lastModifiedDate":"2020-06-19T16:12:17.863811","indexId":"70206538","displayToPublicDate":"2018-05-07T10:04:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","docAbstract":"The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2018.00051","usgsCitation":"Fausto, R., Box, J.E., Baptiste Vandecrux, van As, D., Steffen, K., MacFerrin, M.J., Machguth, H., Colgan, W., Mcgrath, D., Koenig, L.S., Charalampidis, C., and Braithwaite, R.J., 2018, A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling: Frontiers in Earth Science, v. 6, 51, 10 p., https://doi.org/10.3389/feart.2018.00051.","productDescription":"51, 10 p.","ipdsId":"IP-082355","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":468774,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00051","text":"Publisher Index 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PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Fausto, Robert","contributorId":220400,"corporation":false,"usgs":false,"family":"Fausto","given":"Robert","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Box, Jason E.","contributorId":198809,"corporation":false,"usgs":false,"family":"Box","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":774906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baptiste Vandecrux","contributorId":220401,"corporation":false,"usgs":false,"family":"Baptiste Vandecrux","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van As, Dirk","contributorId":220402,"corporation":false,"usgs":false,"family":"van As","given":"Dirk","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774908,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steffen, Konrad","contributorId":220461,"corporation":false,"usgs":false,"family":"Steffen","given":"Konrad","email":"","affiliations":[],"preferred":false,"id":774970,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"MacFerrin, Michael J.","contributorId":220462,"corporation":false,"usgs":false,"family":"MacFerrin","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774971,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Machguth, 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S.","contributorId":220465,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":774974,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Charalampidis, Charalampos","contributorId":220466,"corporation":false,"usgs":false,"family":"Charalampidis","given":"Charalampos","email":"","affiliations":[],"preferred":false,"id":774975,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Braithwaite, Roger J.","contributorId":220467,"corporation":false,"usgs":false,"family":"Braithwaite","given":"Roger","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774976,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70255991,"text":"70255991 - 2018 - Separable correlation and maximum likelihood","interactions":[],"lastModifiedDate":"2024-07-12T11:46:44.505225","indexId":"70255991","displayToPublicDate":"2018-05-07T06:45:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18005,"text":"arXiv","active":true,"publicationSubtype":{"id":10}},"title":"Separable correlation and maximum likelihood","docAbstract":"We consider estimation of the covariance matrix of a multivariate normal distribution when the correlation matrix is separable in the sense that it factors as a Kronecker product of two smaller matrices. A computationally convenient coordinate descent-type algorithm is developed for maximum likelihood estimation. Simulations indicate our method often gives smaller estimation error than some common alternatives when correlation is separable, and that correctly sized tests for correlation separability can be obtained using a parametric bootstrap. Using dissolved oxygen data from the Upper Mississippi River, we illustrate how our model can lead to interesting scientific findings that may be missed when using competing models.
Management actions aimed at eradicating exotic fish species from riverine ecosystems can be better informed by forecasting abilities of mechanistic models. We illustrate this point with an example of the Logan River, Utah, originally populated with endemic cutthroat trout (Oncorhynchus clarkii utah), which compete with exotic brown trout (Salmo trutta). The coexistence equilibrium was disrupted by a large scale, experimental removal of the exotic species in 2009–2011 (on average, 8.2% of the stock each year), followed by an increase in the density of the native species. We built a spatially-explicit, reaction-diffusion model encompassing four key processes: population growth in heterogeneous habitat, competition, dispersal, and a management action. We calibrated the model with detailed long-term monitoring data (2001–2016) collected along the 35.4-km long river main channel. Our model, although simple, did a remarkable job reproducing the system steady state prior to the management action. Insights gained from the model independent predictions are consistent with available knowledge and indicate that the exotic species is more competitive; however, the native species still occupies more favorable habitat upstream. Dynamic runs of the model also recreated the observed increase of the native species following the management action. The model can simulate two possible distinct long-term outcomes: recovery or eradication of the exotic species. The processing of available knowledge using Bayesian methods allowed us to conclude that the chance for eradication of the invader was low at the beginning of the experimental removal (0.7% in 2009) and increased (20.5% in 2016) by using more recent monitoring data. We show that accessible mathematical and numerical tools can provide highly informative insights for managers (e.g., outcome of their conservation actions), identify knowledge gaps, and provide testable theory for researchers.
Post‐Miocene tectonic uplift along fault‐cored anticlines within central Washington produced the Yakima Fold Province, a region of active NNE‐SSW shortening in the Cascadian backarc. The relative timing and rate of deformation along individual structures is coarsely defined yet imperative for seismic hazard assessment. In this work, we use geomorphic and geophysical mapping, stream profile inversion, and balanced cross‐section methods to constrain fault geometries and slip rates in the Yakima Canyon region. We extract stream profiles from LiDAR data and analytically solve for the rate of relative rock uplift along several active fault‐cored anticlines. To constrain the fault geometries at depth and the long‐term magnitude of deformation, we constructed two line‐balanced cross sections across the folds with forward‐modeled magnetic and gravity anomaly data. Our stream profile results indicate an increase of incision rates in the Pleistocene, and we infer the increase is tectonically controlled. We estimate modern slip rates between 0.4 and 0.6 mm/year accommodated on reverse faults that core the Manastash Ridge, Umtanum Ridge, and Selah Butte anticlines and establish that these faults reactivate and invert older normal faults in basement rocks. Finally, we calculate the time required to accumulate sufficient strain energy for a large magnitude earthquake (M ≥ 7) along individual structures in the Yakima Fold Province. Results show that the Yakima folds likely accommodate large magnitude earthquakes and that it takes several hundred to several thousand years to accumulate sufficient strain energy for an M ≥ 7 earthquake.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017TC004916","usgsCitation":"Staisch, L.M., Blakely, R.J., Kelsey, H., Styron, R., and Sherrod, B.L., 2018, Crustal structure and quaternary acceleration of deformation rates in central Washington revealed by stream profile inversion, potential field geophysics, and structural geology of the Yakima folds: Tectonics, v. 37, no. 6, p. 1750-1770, https://doi.org/10.1029/2017TC004916.","productDescription":"21 p.","startPage":"1750","endPage":"1770","ipdsId":"IP-092865","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":468777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017tc004916","text":"Publisher Index Page"},{"id":355670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima Folds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.364990234375,\n 45.40616374516014\n ],\n [\n -117.7734375,\n 45.40616374516014\n ],\n [\n -117.7734375,\n 49.0738659012854\n ],\n [\n -125.364990234375,\n 49.0738659012854\n ],\n [\n -125.364990234375,\n 45.40616374516014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-06","publicationStatus":"PW","scienceBaseUri":"5b6fc450e4b0f5d57878ea4f","contributors":{"authors":[{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":739973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Harvey","contributorId":106978,"corporation":false,"usgs":true,"family":"Kelsey","given":"Harvey","affiliations":[],"preferred":false,"id":739976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Styron, Richard","contributorId":201082,"corporation":false,"usgs":false,"family":"Styron","given":"Richard","email":"","affiliations":[],"preferred":false,"id":739974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":739975,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195030,"text":"sir20185018 - 2018 - Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016","interactions":[],"lastModifiedDate":"2018-09-25T06:00:30","indexId":"sir20185018","displayToPublicDate":"2018-05-07T00:00:00","publicationYear":"2018","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":"2018-5018","title":"Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016","docAbstract":"Coastal lagoons are common features of the Puerto Rico shoreline that provide habitat for commercial and recreational species and serve important roles in the nutrient cycle of the ecosystems. The U.S. Geological Survey, in cooperation with the Puerto Rico Environmental Quality Board, conducted a limnological study at Caño Boquerón in Cabo Rojo and at Puerto Mosquito on Isla de Vieques, Puerto Rico, to assess the principal mechanisms affecting the hydrology and water-quality characteristics of these coastal lagoons and provide baseline information to the regulatory agencies responsible for the management and conservation of these coastal waters and the preservation of their aquatic life.
Field measurements and water samples were collected and processed during July 2015–July 2016 for analysis of physical, chemical, biological, and bacteriological characteristics. In addition, bathymetric surveys were made and sediment cores were collected in each lagoon to determine water volume and sediment deposition rate. Physicochemical properties assessed at Caño Boquerón indicated values were generally in compliance with Puerto Rico Environmental Quality Board standards; turbidity was occasionally slightly greater than the established standards, and dissolved oxygen concentration at bottom depths was lower than standards limits. Water transparency was evaluated through the Secchi disk method, and the average depth of disappearance was 1.0 meter (m) for Caño Boquerón and 1.9 m for Puerto Mosquito.
Assessment of biological characteristics at both sites included primary productivity calculations as well as carbon production equivalents and monthly water sampling for bacteriological and nutrient analyses. For Caño Boquerón, gross plankton primary productivity averaged 3.38 grams of oxygen per cubic meter per day (gO2/m3-d); this value was computed as the sum of net phytoplankton primary productivity (0.74 gO2/m3-d) and plankton respiration (2.64 gO2/m3-d). Net community primary productivity averaged 1.44 gO2/m3-d, and the community respiration rate averaged 8.10 gO2/m3-d, which indicates that the biological community, aside from phytoplankton, acts as a net consumer rather than a net producer of biomass. In Puerto Mosquito, gross plankton primary productivity averaged 2.07 gO2/m3-d, of which 0.39 gO2/m3-d could be ascribed to net phytoplankton primary productivity, and 1.68 gO2/m3-d could be ascribed to plankton respiration. Diel studies conducted at Puerto Mosquito reflected an average net community primary productivity of 2.43 gO2/m3-d, and the average respiration rate was 6.72 gO2/m3-d.
In a bathymetric survey conducted in August 2015, the water volume for the Caño Boquerón lagoon was calculated as 967,000 cubic meters (m3), and the water volume at Puerto Mosquito was calculated as 1,182,200 m3, with an average depth of 1.5 m for Caño Boquerón and 1.8 m for Puerto Mosquito. The daily seawater exchange for Caño Boquerón and Puerto Mosquito was 13 and 5 percent of their water volumes referenced to mean sea level, respectively. A total of 20 sediment samples were processed and analyzed for cesium-137 (137Cs) and lead-210 (210Pb) radioisotopes. Analyses indicated that the sediment deposition rate at Caño Boquerón ranged from 0.32 to 0.36 centimeter per year, based on age dating analysis of 137Cs and 210Pb data; in Puerto Mosquito, the sediment deposition rate ranged from 0.26 to 0.27 centimeter per year, based on 137Cs and 210Pb data.
Bacteriological analyses at Caño Boquerón and Puerto Mosquito indicated that fecal coliform and enterococci concentrations were below Puerto Rico Environmental Quality Board standards during the study. The highest concentrations of fecal coliform (22 colonies per 100 milliliters) and enterococci (9 colonies per 100 milliliters) at Caño Boquerón occurred in July, which coincided with the busiest season of vacation rentals near the lagoon. Bacteria concentrations were generally lower in Puerto Mosquito than in Caño Boquerón; maximum concentrations of fecal coliform and enterococci bacteria were measured in November 2015. The potential sources of contamination for Puerto Mosquito are limited, because it is within a conservation area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185018","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Gómez-Fragoso, J.M., and Santiago-Sáez, J.A., 2018, Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016: U.S. Geological Survey Scientific Investigations Report 2018–5018, 34 p., https://doi.org/10.3133/sir20185018.","productDescription":"Report: ix, 34 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-078162","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":437920,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WH2P6K","text":"USGS data release","linkHelpText":"Gomez-Fragoso, Julieta, 2017, Data for the Hydrologic and Water-Quality Characterization of Cano Boqueron, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015-July 2016: U.S. Geological Survey data release"},{"id":353903,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7WH2P6K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for the Hydrologic and Water-Quality Characterization of Puerto Mosquito, Vieques and Caño Boquerón, Cabo Rojo, Puerto Rico, July 2015–July 2016"},{"id":353901,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5018/coverthb2.jpg"},{"id":353902,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5018/sir20185018.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5018"}],"country":"United States","otherGeospatial":"Caño Boquerón, Cabo Rojo, Puerto Mosquito, Isla de Vieques, Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2167,\n 17.9833\n ],\n [\n -67.1333,\n 17.9833\n ],\n [\n -67.1333,\n 18.04\n ],\n [\n -67.2167,\n 18.04\n ],\n [\n -67.2167,\n 17.9833\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.4833,\n 18.0833\n ],\n [\n -65.4,\n 18.0833\n ],\n [\n -65.4,\n 18.1333\n ],\n [\n -65.4833,\n 18.1333\n ],\n [\n -65.4833,\n 18.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559