Angler groups and water-body types interact to create a complex social-ecological system. Network analysis could inform detailed mechanistic models on, and provide managers better information about, basic patterns of fishing activity. Differences in behavior and reservoir selection among angler groups in a regional fishery, the Salt Valley fishery in southeastern Nebraska, USA, were assessed using a combination of cluster and network analyses. The four angler groups assessed ranged from less active, unskilled anglers (group One) to highly active, very skilled anglers (group Four). Reservoir use patterns and the resulting network communities of these four angler groups differed; the number of reservoir communities for these groups ranged from two to three and appeared to be driven by reservoir location (group One), reservoir size and its associated attributes (groups Two and Four), or an interaction between reservoir size and location (group Three). Network analysis is a useful tool to describe differences in participation among angler groups within a regional fishery, and provides new insights about possible recruitment of anglers. For example, group One anglers fished reservoirs closer to home and had a greater probability of dropping out if local reservoir access were restricted.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2017.05.007","usgsCitation":"Martin, D., Shizuka, D., Chizinski, C.J., and Pope, K.L., 2017, Network analysis of a regional fishery: Implications for management of natural resources, and recruitment and retention of anglers: Fisheries Research, v. 194, p. 31-41, https://doi.org/10.1016/j.fishres.2017.05.007.","productDescription":"11 p.","startPage":"31","endPage":"41","ipdsId":"IP-064777","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"194","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425b3e4b0dc0b45b4531d","contributors":{"authors":[{"text":"Martin, Dustin R.","contributorId":43482,"corporation":false,"usgs":true,"family":"Martin","given":"Dustin R.","affiliations":[],"preferred":false,"id":721047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shizuka, Daizaburo","contributorId":62048,"corporation":false,"usgs":true,"family":"Shizuka","given":"Daizaburo","email":"","affiliations":[],"preferred":false,"id":721048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chizinski, Christopher J.","contributorId":7178,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717244,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193653,"text":"70193653 - 2017 - Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance","interactions":[],"lastModifiedDate":"2017-11-13T14:41:28","indexId":"70193653","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance","docAbstract":"It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1587","usgsCitation":"Clare, J., McKinney, S.T., DePue, J.E., and Loftin, C., 2017, Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance: Ecological Applications, v. 27, no. 7, p. 2031-2047, https://doi.org/10.1002/eap.1587.","productDescription":"17 p.","startPage":"2031","endPage":"2047","ipdsId":"IP-072877","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","volume":"27","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"5a60fb3ae4b06e28e9c22e1d","contributors":{"authors":[{"text":"Clare, John","contributorId":200304,"corporation":false,"usgs":false,"family":"Clare","given":"John","affiliations":[],"preferred":false,"id":721849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKinney, Shawn T. smckinney@usgs.gov","contributorId":5175,"corporation":false,"usgs":true,"family":"McKinney","given":"Shawn","email":"smckinney@usgs.gov","middleInitial":"T.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":721850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DePue, John E.","contributorId":200305,"corporation":false,"usgs":false,"family":"DePue","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":719764,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192177,"text":"70192177 - 2017 - Causes of distal volcano-tectonic seismicity inferred from hydrothermal modeling","interactions":[],"lastModifiedDate":"2017-11-06T12:48:41","indexId":"70192177","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Causes of distal volcano-tectonic seismicity inferred from hydrothermal modeling","docAbstract":"Distal volcano-tectonic (dVT) seismicity typically precedes eruption at long-dormant volcanoes by days to years. Precursory dVT seismicity may reflect magma-induced fluid-pressure pulses that intersect critically stressed faults. We explored this hypothesis using an open-source magmatic-hydrothermal code that simulates multiphase fluid and heat transport over the temperature range 0 to 1200 °C. We calculated fluid-pressure changes caused by a small (0.04 km3) intrusion and explored the effects of flow geometry (channelized vs. radial flow), magma devolatilization rates (0–15 kg/s), and intrusion depths (5 and 7.5 km, above and below the brittle-ductile transition). Magma and host-rock permeabilities were key controlling parameters and we tested a wide range of permeability (k) and permeability anisotropies (kh/kv), including k constant, k(z), k(T), and k(z, T, P) distributions, examining a total of ~ 1600 realizations to explore the relevant parameter space. Propagation of potentially causal pressure changes (ΔP ≥ 0.1 bars) to the mean dVT location (6 km lateral distance, 6 km depth) was favored by channelized fluid flow, high devolatilization rates, and permeabilities similar to those found in geothermal reservoirs (k ~ 10− 16 to 10− 13 m2). For channelized flow, magma-induced thermal pressurization alone can generate cases of ∆ P ≥ 0.1 bars for all permeabilities in the range 10− 16 to 10− 13 m2, whereas in radial flow regimes thermal pressurization causes ∆ P < 0.1 bars for all permeabilities. Changes in distal fluid pressure occurred before proximal pressure changes given modest anisotropies (kh/kv ~ 10–100). Invoking k(z,T,P) and high, sustained devolatilization rates caused large dynamic fluctuations in k and P in the near-magma environment but had little effect on pressure changes at the distal dVT location. Intrusion below the brittle-ductile transition damps but does not prevent pressure transmission to the dVT site.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.07.011","usgsCitation":"Coulon, C.A., Hsieh, P.A., White, R.A., Lowenstern, J.B., and Ingebritsen, S.E., 2017, Causes of distal volcano-tectonic seismicity inferred from hydrothermal modeling: Journal of Volcanology and Geothermal Research, v. 345, p. 98-108, https://doi.org/10.1016/j.jvolgeores.2017.07.011.","productDescription":"11 p.","startPage":"98","endPage":"108","ipdsId":"IP-087283","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469487,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.07.011","text":"Publisher Index Page"},{"id":348271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"345","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e873e4b09af898c8cb74","contributors":{"authors":[{"text":"Coulon, Cecile A.","contributorId":197905,"corporation":false,"usgs":false,"family":"Coulon","given":"Cecile","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":714560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":714562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":714561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":714558,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193413,"text":"70193413 - 2017 - Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?","interactions":[],"lastModifiedDate":"2017-11-20T13:35:31","indexId":"70193413","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?","docAbstract":"Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk-based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs.
","language":"English","publisher":"SETAC Press","doi":"10.1002/etc.3842","usgsCitation":"Burns, E.E., Thomas-Oates, J., Kolpin, D.W., Furlong, E.T., and Boxall, A.B., 2017, Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?: Environmental Toxicology and Chemistry, v. 36, no. 10, p. 2823-2832, https://doi.org/10.1002/etc.3842.","productDescription":"10 p.","startPage":"2823","endPage":"2832","ipdsId":"IP-084959","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":469564,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orcid.org/0000-0003-4236-6409>,","text":"External Repository"},{"id":349138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","city":"City of York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -1.1940765380859375,\n 53.911619008118656\n ],\n [\n -0.9976959228515625,\n 53.911619008118656\n ],\n [\n -0.9976959228515625,\n 54.05374516606874\n ],\n [\n -1.1940765380859375,\n 54.05374516606874\n ],\n [\n -1.1940765380859375,\n 53.911619008118656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"10","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-06","publicationStatus":"PW","scienceBaseUri":"5a60fb44e4b06e28e9c22e91","contributors":{"authors":[{"text":"Burns, Emily E.","contributorId":199400,"corporation":false,"usgs":false,"family":"Burns","given":"Emily","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":718961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas-Oates, Jane","contributorId":195997,"corporation":false,"usgs":false,"family":"Thomas-Oates","given":"Jane","email":"","affiliations":[],"preferred":false,"id":718962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Furlong, Edward T. 0000-0002-7305-4603 efurlong@usgs.gov","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":740,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","email":"efurlong@usgs.gov","middleInitial":"T.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boxall, Alistair B.A.","contributorId":187614,"corporation":false,"usgs":false,"family":"Boxall","given":"Alistair","email":"","middleInitial":"B.A.","affiliations":[],"preferred":false,"id":718963,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191913,"text":"70191913 - 2017 - Challenges and solutions for applying the travel cost demand model to geographically remote visitor destinations: A case study of bear viewing at Katmai National Park and Preserve","interactions":[],"lastModifiedDate":"2017-10-19T13:08:10","indexId":"70191913","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1909,"text":"Human Dimensions of Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Challenges and solutions for applying the travel cost demand model to geographically remote visitor destinations: A case study of bear viewing at Katmai National Park and Preserve","docAbstract":"Remote and unique destinations present difficulties when attempting to construct traditional travel cost models to value recreation demand. The biggest limitation comes from the lack of variation in the dependent variable, defined as the number of trips taken over a set time frame. There are various approaches that can be used for overcoming limitations of the traditional travel cost model in the context of remote destinations. This study applies an adaptation of the standard model to estimate recreation benefits of bear viewing at Katmai National Park and Preserve in Alaska, which represents a once-in-a-lifetime experience for many visitors. Results demonstrate that visitors to this park’s Brooks Camp area are willing to pay an average of US$287 per day of bear viewing. Implications of these findings for valuing recreation at other remote destinations are discussed.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10871209.2017.1369196","usgsCitation":"Richardson, L., Huber, C., and Loomis, J.B., 2017, Challenges and solutions for applying the travel cost demand model to geographically remote visitor destinations: A case study of bear viewing at Katmai National Park and Preserve: Human Dimensions of Wildlife, v. 22, no. 6, p. 550-563, https://doi.org/10.1080/10871209.2017.1369196.","productDescription":"14 p.","startPage":"550","endPage":"563","ipdsId":"IP-078280","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and Preserve","volume":"22","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-15","publicationStatus":"PW","scienceBaseUri":"59e9b993e4b05fe04cd65c60","contributors":{"authors":[{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":713669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":713670,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194440,"text":"70194440 - 2017 - Optimal control of an invasive species using a reaction-diffusion model and linear programming","interactions":[],"lastModifiedDate":"2017-11-29T13:24:24","indexId":"70194440","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Optimal control of an invasive species using a reaction-diffusion model and linear programming","docAbstract":"Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the models that we considered. Including different possible models allows an examination of how the strategy is expected to perform in different scenarios. Then, a strategy that accounts for the risk attitude of the decision-maker can be designed.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1979","usgsCitation":"Bonneau, M., Johnson, F.A., Smith, B.J., Romagosa, C.M., Martin, J., and Mazzotti, F., 2017, Optimal control of an invasive species using a reaction-diffusion model and linear programming: Ecosphere, v. 8, no. 10, p. 1-17, https://doi.org/10.1002/ecs2.1979.","productDescription":"Article e01979; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-079217","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469476,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1979","text":"Publisher Index Page"},{"id":349539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.59364318847656,\n 25.26146360779529\n ],\n [\n -80.29769897460938,\n 25.26146360779529\n ],\n [\n -80.29769897460938,\n 25.572175556682115\n ],\n [\n -80.59364318847656,\n 25.572175556682115\n ],\n [\n -80.59364318847656,\n 25.26146360779529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"10","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"5a60fb3ae4b06e28e9c22e11","contributors":{"authors":[{"text":"Bonneau, Mathieu","contributorId":150041,"corporation":false,"usgs":false,"family":"Bonneau","given":"Mathieu","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":723816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":723815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Brian J. 0000-0002-0531-0492 bjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":899,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","email":"bjsmith@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":723817,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romagosa, Christina M.","contributorId":200925,"corporation":false,"usgs":false,"family":"Romagosa","given":"Christina","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":723818,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, Julien 0000-0002-7375-129X julienmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":5785,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","email":"julienmartin@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":723819,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":723820,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195394,"text":"70195394 - 2017 - Role of a naturally varying flow regime in Everglades restoration","interactions":[],"lastModifiedDate":"2018-02-13T13:34:06","indexId":"70195394","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Role of a naturally varying flow regime in Everglades restoration","docAbstract":"The Everglades is a low-gradient floodplain predominantly on organic soil that undergoes seasonally pulsing sheetflow through a network of deepwater sloughs separated by slightly higher elevation ridges. The seasonally pulsing flow permitted the coexistence of ridge and slough vegetation, including the persistence of productive, well-connected sloughs that seasonally concentrated prey and supported wading bird nesting success. Here we review factors contributing to the origin and to degradation of the ridge and slough ecosystem in an attempt to answer “How much flow is needed to restore functionality”? A key restoration objective is to increase sheetflow lost during the past century to reestablish interactions between flow, water depth, vegetation production and decomposition, and transport of flocculent organic sediment that build and maintain ridge and slough distinctions. Our review finds broad agreement that perturbations of water level depth and its fluctuations were primary in the degradation of landscape functions, with critical contributions from perturbed water quality, and flow velocity and direction. Whereas water levels are expected to be improved on average across a range of restoration scenarios that replace between 79 and 91% of predrainage flows, the diminished microtopography substantially decreases the probability of timely improvements in some areas whereas others that retain microtopographic differences are poised for restoration benefits. New advances in predicting restoration outcomes are coming from biophysical modeling of ridge–slough dynamics, system-wide measurements of landscape functionality, and large-scale flow restoration experiments, including active management techniques to kick-start slough regeneration.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.12558","usgsCitation":"Harvey, J., Wetzel, P.R., Lodge, T.E., Engel, V.C., and Ross, M.S., 2017, Role of a naturally varying flow regime in Everglades restoration: Restoration Ecology, v. 25, no. S1, p. S27-S38, https://doi.org/10.1111/rec.12558.","productDescription":"12 p.","startPage":"S27","endPage":"S38","ipdsId":"IP-080490","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":351531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.18896484375,\n 25.137825490722225\n ],\n [\n -80.211181640625,\n 25.137825490722225\n ],\n [\n -80.211181640625,\n 26.676913083105454\n ],\n [\n -81.18896484375,\n 26.676913083105454\n ],\n [\n -81.18896484375,\n 25.137825490722225\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"S1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"5afee7eae4b0da30c1bfc39f","contributors":{"authors":[{"text":"Harvey, Judson 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":140228,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":728390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wetzel, Paul R.","contributorId":202429,"corporation":false,"usgs":false,"family":"Wetzel","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":36432,"text":"Smith College, Northhampton, MA","active":true,"usgs":false}],"preferred":false,"id":728391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lodge, Thomas E.","contributorId":202430,"corporation":false,"usgs":false,"family":"Lodge","given":"Thomas","email":"","middleInitial":"E.","affiliations":[{"id":36433,"text":"Thomas E. Lodge Ecological Advisors, Inc.","active":true,"usgs":false}],"preferred":false,"id":728392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engel, Victor C. 0000-0002-3858-7308 vengel@usgs.gov","orcid":"https://orcid.org/0000-0002-3858-7308","contributorId":2329,"corporation":false,"usgs":true,"family":"Engel","given":"Victor","email":"vengel@usgs.gov","middleInitial":"C.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":728394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ross, Michael S.","contributorId":202431,"corporation":false,"usgs":false,"family":"Ross","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":36434,"text":"Florida International University, Miami, FL","active":true,"usgs":false}],"preferred":false,"id":728393,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192134,"text":"70192134 - 2017 - Climate impacts on agricultural land use in the USA: the role of socio-economic scenarios","interactions":[],"lastModifiedDate":"2017-10-23T14:40:19","indexId":"70192134","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate impacts on agricultural land use in the USA: the role of socio-economic scenarios","docAbstract":"We examine the impacts of climate on net returns from crop and livestock production and the resulting impact on land-use change across the contiguous USA. We first estimate an econometric model to project effects of weather fluctuations on crop and livestock net returns and then use a semi-reduced form land-use share model to study agricultural land-use changes under future climate and socio-economic scenarios. Estimation results show that crop net returns are more sensitive to thermal and less sensitive to moisture variability than livestock net returns; other agricultural land uses substitute cropland use when 30-year averaged degree-days or precipitation are not beneficial for crop production. Under future climate and socio-economic scenarios, we project that crop and livestock net returns are both increasing, but with crop net returns increasing at a higher rate; cropland increases with declines of marginal and pastureland by the end of the twenty-first century. Projections also show that impacts of future climate on agricultural land uses are substantially different and a larger variation of land-use change is evident when socio-economic scenarios are incorporated into the climate impact analysis.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-2033-x","usgsCitation":"Mu, J.E., Sleeter, B.M., Abatzoglou, J.T., and Antle, J.M., 2017, Climate impacts on agricultural land use in the USA: the role of socio-economic scenarios: Climatic Change, v. 144, no. 2, p. 329-345, https://doi.org/10.1007/s10584-017-2033-x.","productDescription":"17 p.","startPage":"329","endPage":"345","ipdsId":"IP-088868","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469477,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10584-017-2033-x","text":"Publisher Index Page"},{"id":347139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n\n\n","volume":"144","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-09","publicationStatus":"PW","scienceBaseUri":"59eeffa4e4b0220bbd988f6d","contributors":{"authors":[{"text":"Mu, Jianhong E.","contributorId":75840,"corporation":false,"usgs":true,"family":"Mu","given":"Jianhong","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":714358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":714359,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antle, John M.","contributorId":197804,"corporation":false,"usgs":false,"family":"Antle","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":714360,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193534,"text":"70193534 - 2017 - Estimating Mudpuppy (Necturus maculosus) abundance in the Lamoille River, Vermont, USA","interactions":[],"lastModifiedDate":"2017-11-14T13:37:04","indexId":"70193534","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating Mudpuppy (Necturus maculosus) abundance in the Lamoille River, Vermont, USA","title":"Estimating Mudpuppy (Necturus maculosus) abundance in the Lamoille River, Vermont, USA","docAbstract":"The Mudpuppy (Necturus maculosus) is classified as a Species of Greatest Conservation Need by the state of Vermont. There is concern regarding status of populations in the Lake Champlain basin because of habitat alteration and potential effects of 3-trifluromethyl-4-nitrophenol (TFM), a chemical used to control Sea Lamprey (Petromyzon marinus). The purpose of our research was to assess Mudpuppy capture methods and abundance in the Lamoille River, Vermont, USA. We sampled Mudpuppies under a mark-recapture framework, using modified, baited minnow traps set during two winter-spring periods. We marked each Mudpuppy with a passive integrated transponder (PIT) tag and released individuals after collecting morphological measurements. We collected 80 individuals during 2,581 trap days in 2008–2009 (year 1), and 81 individuals during 3,072 trap days in 2009–2010 (year 2). We estimated abundance from spring trapping periods in 2009 and 2010, during which capture rates were sufficient for analysis. Capture probability was low (< 0.04), but highest following precipitation events in spring, during periods of higher river flow, when water temperatures were approximately 3 to 6° C. During October 2009, management agencies treated the Lamoille River with TFM. Surveyors recovered more than 500 dead Mudpuppies during the post-treatment assessment. Overall, Mudpuppy captures did not change between sampling periods; however, we captured fewer females during year 2 compared to year 1, and the sex ratio changed from 0.79:1 (M:F) during year 1 to 3:1 (M:F) during year 2. Our data may help wildlife managers assess population status of Mudpuppies in conjunction with fisheries management techniques.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Chellman, I.C., Parrish, D.L., and Donovan, T., 2017, Estimating Mudpuppy (Necturus maculosus) abundance in the Lamoille River, Vermont, USA: Herpetological Conservation and Biology, v. 12, no. 2, p. 422-434.","productDescription":"13 p.","startPage":"422","endPage":"434","ipdsId":"IP-056683","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Lamoille River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.17598342895506,\n 44.630489423286996\n ],\n [\n -73.16044807434082,\n 44.630489423286996\n ],\n [\n -73.16044807434082,\n 44.63983415674708\n ],\n [\n -73.17598342895506,\n 44.63983415674708\n ],\n [\n -73.17598342895506,\n 44.630489423286996\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb44e4b06e28e9c22e8e","contributors":{"authors":[{"text":"Chellman, Isaac C.","contributorId":200358,"corporation":false,"usgs":false,"family":"Chellman","given":"Isaac","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":722045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parrish, Donna L. 0000-0001-9693-6329 dparrish@usgs.gov","orcid":"https://orcid.org/0000-0001-9693-6329","contributorId":138661,"corporation":false,"usgs":true,"family":"Parrish","given":"Donna","email":"dparrish@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Therese M. tdonovan@usgs.gov","contributorId":2653,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese M.","email":"tdonovan@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":722046,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192065,"text":"70192065 - 2017 - Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments","interactions":[],"lastModifiedDate":"2017-10-19T14:04:04","indexId":"70192065","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments","docAbstract":"Nutritional ecology forms the interface between environmental variability and large herbivore behaviour, life history characteristics, and population dynamics. Forage conditions in arid and semi-arid regions are driven by unpredictable spatial and temporal patterns in rainfall. Diet selection by herbivores should be directed towards overcoming the most pressing nutritional limitation (i.e. energy, protein [nitrogen, N], moisture) within the constraints imposed by temporal and spatial variability in forage conditions. We investigated the influence of precipitation-induced shifts in forage nutritional quality and subsequent large herbivore responses across widely varying precipitation conditions in an arid environment. Specifically, we assessed seasonal changes in diet breadth and forage selection of adult female desert bighorn sheep Ovis canadensis mexicana in relation to potential nutritional limitations in forage N, moisture and energy content (as proxied by dry matter digestibility, DMD). Succulents were consistently high in moisture but low in N and grasses were low in N and moisture until the wet period. Nitrogen and moisture content of shrubs and forbs varied among seasons and climatic periods, whereas trees had consistently high N and moderate moisture levels. Shrubs, trees and succulents composed most of the seasonal sheep diets but had little variation in DMD. Across all seasons during drought and during summer with average precipitation, forages selected by sheep were higher in N and moisture than that of available forage. Differences in DMD between sheep diets and available forage were minor. Diet breadth was lowest during drought and increased with precipitation, reflecting a reliance on few key forage species during drought. Overall, forage selection was more strongly associated with N and moisture content than energy content. Our study demonstrates that unlike north-temperate ungulates which are generally reported to be energy-limited, N and moisture may be more nutritionally limiting for desert ungulates than digestible energy.
","language":"English","publisher":"Wiley","doi":"10.1111/oik.04282","usgsCitation":"Cain, J.W., Gedir, J.V., Marshal, J.P., Krausman, P.R., Allen, J.D., Duff, G.C., Jansen, B., and Morgart, J.R., 2017, Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments: Oikos, v. 126, no. 10, p. 1459-1471, https://doi.org/10.1111/oik.04282.","productDescription":"13 p.","startPage":"1459","endPage":"1471","ipdsId":"IP-072425","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":438201,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7B856CS","text":"USGS data release","linkHelpText":"Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments"},{"id":346983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cabeza Prieta National Wildlife Refuge","volume":"126","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-15","publicationStatus":"PW","scienceBaseUri":"59e9b993e4b05fe04cd65c56","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":714069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshal, Jason P.","contributorId":197680,"corporation":false,"usgs":false,"family":"Marshal","given":"Jason","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":714070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krausman, Paul R.","contributorId":31467,"corporation":false,"usgs":true,"family":"Krausman","given":"Paul","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":714071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Jamison D.","contributorId":171736,"corporation":false,"usgs":false,"family":"Allen","given":"Jamison","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":714072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duff, Glenn C.","contributorId":171737,"corporation":false,"usgs":false,"family":"Duff","given":"Glenn","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":714073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansen, Brian","contributorId":191917,"corporation":false,"usgs":false,"family":"Jansen","given":"Brian","email":"","affiliations":[],"preferred":false,"id":714074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morgart, John R.","contributorId":10891,"corporation":false,"usgs":true,"family":"Morgart","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":714075,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70191113,"text":"70191113 - 2017 - Age, year‐class strength variability, and partial age validation of Kiyis from Lake Superior","interactions":[],"lastModifiedDate":"2018-03-29T13:04:16","indexId":"70191113","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Age, year‐class strength variability, and partial age validation of Kiyis from Lake Superior","docAbstract":"ge estimates of Lake Superior Kiyis Coregonus kiyi from scales and otoliths were compared and 12 years (2003–2014) of length frequency data were examined to assess year‐class strength and validate age estimates. Ages estimated from otoliths were precise and were consistently older than ages estimated from scales. Maximum otolith‐derived ages were 20 years for females and 12 years for males. Age estimates showed high numbers of fish of ages 5, 6, and 11 in 2014, corresponding to the 2009, 2008, and 2003 year‐classes, respectively. Strong 2003 and 2009 year‐classes, along with the 2005 year‐class, were also evident based on distinct modes of age‐1 fish (<110 mm) in the length frequency distributions from 2004, 2010, and 2006, respectively. Modes from these year‐classes were present as progressively larger fish in subsequent years. Few to no age‐1 fish (<110 mm) were present in all other years. Ages estimated from otoliths were generally within 1 year of the ages corresponding to strong year‐classes, at least for age‐5 and older fish, suggesting that Kiyi age may be reliably estimated to within 1 year by careful examination of thin‐sectioned otoliths.
","language":"English","publisher":"Wiley","doi":"10.1080/02755947.2017.1350222","usgsCitation":"Lepak, T.A., Ogle, D.H., and Vinson, M., 2017, Age, year‐class strength variability, and partial age validation of Kiyis from Lake Superior: North American Journal of Fisheries Management, v. 37, no. 5, p. 1151-1160, https://doi.org/10.1080/02755947.2017.1350222.","productDescription":"10 p.","startPage":"1151","endPage":"1160","ipdsId":"IP-085857","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":438197,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7NT9","text":"USGS data release","linkHelpText":"Lake Superior Kiyi scale and otolith age estimates in 2014 with Kiyi sampling locations from 2003-2014"},{"id":352941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.208251953125,\n 46.36967413462374\n ],\n [\n -84.320068359375,\n 46.36967413462374\n ],\n [\n -84.320068359375,\n 49.059469847170526\n ],\n [\n -92.208251953125,\n 49.059469847170526\n ],\n [\n -92.208251953125,\n 46.36967413462374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-06","publicationStatus":"PW","scienceBaseUri":"5afee7eae4b0da30c1bfc3a9","contributors":{"authors":[{"text":"Lepak, Taylor A.","contributorId":196719,"corporation":false,"usgs":false,"family":"Lepak","given":"Taylor","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogle, Derek H. 0000-0002-0370-9299","orcid":"https://orcid.org/0000-0002-0370-9299","contributorId":196718,"corporation":false,"usgs":false,"family":"Ogle","given":"Derek","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":711265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":711264,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191162,"text":"sir20175116 - 2017 - Flood-inundation maps for the Meramec River at Valley Park and at Fenton, Missouri, 2017","interactions":[],"lastModifiedDate":"2017-10-02T11:02:21","indexId":"sir20175116","displayToPublicDate":"2017-09-29T00:00:00","publicationYear":"2017","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":"2017-5116","title":"Flood-inundation maps for the Meramec River at Valley Park and at Fenton, Missouri, 2017","docAbstract":"Two sets of digital flood-inundation map libraries that spanned a combined 16.7-mile reach of the Meramec River that extends upstream from Valley Park, Missouri, to downstream from Fenton, Mo., were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, St. Louis Metropolitan Sewer District, Missouri Department of Transportation, Missouri American Water, and Federal Emergency Management Agency Region 7. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages on the Meramec River at Valley Park, Mo., (USGS station number 07019130) and the Meramec River at Fenton, Mo. (USGS station number 07019210). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites vllm7 and fnnm7, respectively).
Flood profiles were computed for the stream reaches by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka streamgage (USGS station number 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute two sets of water-surface profiles: one set for the streamgage at Valley Park, Mo. (USGS station number 07019130), and one set for the USGS streamgage on the Meramec River at Fenton, Mo. (USGS station number 07019210). The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Eureka streamgage (USGS station number 07019000). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2017.09.057","usgsCitation":"Grinde, A.R., Hiemi, G.J., Sturtevant, B.R., Panci, H., Thogmartin, W.E., and Wolter, P., 2017, Importance of scale, land cover, and weather on the abundance of bird species in a managed forest: Forest Ecology and Management, v. 405, p. 295-308, https://doi.org/10.1016/j.foreco.2017.09.057.","productDescription":"14 p.","startPage":"295","endPage":"308","ipdsId":"IP-083833","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/239","text":"Publisher Index Page"},{"id":352524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Chippewa National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.71588134765625,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 46.80193957664001\n ],\n [\n -93.22998046875,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 47.8666165573186\n ],\n [\n -94.71588134765625,\n 46.80193957664001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"405","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee7ebe4b0da30c1bfc3b3","contributors":{"authors":[{"text":"Grinde, Alexis R.","contributorId":196778,"corporation":false,"usgs":false,"family":"Grinde","given":"Alexis","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hiemi, Gerald J.","contributorId":196780,"corporation":false,"usgs":false,"family":"Hiemi","given":"Gerald","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturtevant, Brian R.","contributorId":190143,"corporation":false,"usgs":false,"family":"Sturtevant","given":"Brian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panci, Hannah","contributorId":196779,"corporation":false,"usgs":false,"family":"Panci","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":711558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":711556,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolter, Peter","contributorId":196781,"corporation":false,"usgs":false,"family":"Wolter","given":"Peter","affiliations":[],"preferred":false,"id":711561,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191144,"text":"ofr20171124 - 2017 - Tracking riverborne sediment and contaminants in Commencement Bay, Washington, using geochemical signatures","interactions":[],"lastModifiedDate":"2017-10-20T10:49:49","indexId":"ofr20171124","displayToPublicDate":"2017-09-29T00:00:00","publicationYear":"2017","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":"2017-1124","title":"Tracking riverborne sediment and contaminants in Commencement Bay, Washington, using geochemical signatures","docAbstract":"Large rivers carry terrestrial sediment, contaminants, and other materials to the coastal zone where they can affect marine biogeochemical cycles and ecosystems. This U.S. Geological Survey study combined river and marine sediment geochemistry and organic contaminant analyses to identify riverborne sediment and associated contaminants at shoreline sites in Commencement Bay, Puget Sound, Washington, that could be used by adult forage fish and other marine organisms. Geochemical signatures distinguished the fine fraction (<0.063 millimeter, mm) of Puyallup River sediment—which originates from Mount Rainier, a Cascade volcano—from glacial fine sediment in lowland bluffs that supply sediment to beaches. In combination with activities of beryllium-7 (7Be), a short-lived radionuclide, geochemical signatures showed that winter 2013–14 sediment runoff from the Puyallup River was transported to and deposited along the north shore of Commencement Bay, then mixed downward into the sediment column. The three Commencement Bay sites at which organic contaminants were measured in surface sediment did not have measurable 7Be activities in that layer, so their contaminant assemblages were attributed to sources from previous years. Concentrations of organic contaminants (the most common of which were polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and fecal sterols) were higher in the <0.063-mm fraction compared to the <2-mm fraction, in winter compared to summer, in river suspended sediment compared to river bar and bank sediment, and in marine sediment compared to river sediment. The geochemical property barium/aluminum (Ba/Al) showed that the median percentage of Puyallup River derived fine surface sediment along the shoreline of Commencement Bay was 77 percent. This finding, in combination with higher concentrations of organic contaminants in marine rather than river sediment, indicates that riverborne sediment-bound contaminants are retained in shallow marine habitats of Commencement Bay. The retention of earlier inputs complicates efforts to identify recent inputs and sources. Understanding modern sources and fates of riverborne sediment and contaminants and their potential ecological impacts will therefore require a suite of targeted geochemical studies in such marine depositional environments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171124","usgsCitation":"Takesue, R.K., Conn, K.E., and Dinicola, R.S., 2017, Tracking riverborne sediment and contaminants in Commencement Bay, Washington, using geochemical signatures: U.S. Geological Survey Open-File Report 2017–1124, 31 p., https://doi.org/10.3133/ofr20171124.","productDescription":"vii, 31 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-086001","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":346256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1124/coverthb.jpg"},{"id":346257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1124/ofr20171124.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1124"}],"country":"United States","state":"Washington","otherGeospatial":"Puyallup River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.46185302734375,\n 46.86582490125156\n ],\n [\n -121.60491943359375,\n 46.86582490125156\n ],\n [\n -121.60491943359375,\n 47.23262467463881\n ],\n [\n -122.46185302734375,\n 47.23262467463881\n ],\n [\n -122.46185302734375,\n 46.86582490125156\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho (Oncorhynchus kisutch) salmon for the Sauk-Suiattle Indian Tribe of Washington State.
The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon (Oncorhynchus gorbuscha) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171125","collaboration":"Prepared in cooperation with Sauk-Suiattle Indian Tribe","usgsCitation":"Woodward, Andrea, Kirby, Grant, and Morris, Scott, 2017, Skagit River coho salmon life history model—Users’ guide: U.S. Geological Survey Open-File Report 2017–1125, 57 p., https://doi.org/10.3133/ofr20171125.","productDescription":"vi, 57 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-081907","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":346300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1125/ofr20171125.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1125"},{"id":346299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1125/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 47.95314495015594\n ],\n [\n -120.75347900390624,\n 49\n ],\n [\n -122.5,\n 49\n ],\n [\n -122.5,\n 47.95314495015594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.08.017","usgsCitation":"Jarnevich, C.S., Talbert, M., Morisette, J.T., Aldridge, C.L., Brown, C., Kumar, S., Manier, D.J., Talbert, C., and Holcombe, T.R., 2017, Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection: Ecological Modelling, v. 363, p. 48-56, https://doi.org/10.1016/j.ecolmodel.2017.08.017.","productDescription":"9 p.","startPage":"48","endPage":"56","ipdsId":"IP-073503","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":346154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":711395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":711396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":711397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Cynthia 0000-0001-8486-7119","orcid":"https://orcid.org/0000-0001-8486-7119","contributorId":196741,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia","affiliations":[],"preferred":false,"id":711398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":711399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manier, Daniel J. 0000-0002-1105-1327 manierd@usgs.gov","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":127553,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","email":"manierd@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711401,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":711402,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190590,"text":"sir20175098 - 2017 - Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013","interactions":[],"lastModifiedDate":"2017-09-28T14:29:06","indexId":"sir20175098","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2017","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":"2017-5098","displayTitle":"Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma,On September 8, 1981, the Oklahoma Water Resources Board established regulatory limits on the maximum annual yield of groundwater (343,042 acre-feet per year) and equal-proportionate-share (EPS) pumping rate (1.0 acre-foot per acre per year) for the North Fork Red River aquifer. The maximum annual yield and EPS were based on a hydrologic investigation that used a numerical groundwater-flow model to evaluate the effects of potential groundwater withdrawals on groundwater availability in the North Fork Red River aquifer. The Oklahoma Water Resources Board is statutorily required (every 20 years) to update the hydrologic investigation on which the maximum annual yield and EPS were based. Because 20 years have elapsed since the final order was issued, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, conducted an updated hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the North Fork Red River aquifer in Oklahoma. This report describes a hydrologic investigation of the North Fork Red River aquifer that includes an updated summary of the aquifer hydrogeology. As part of this investigation, groundwater flow and availability were simulated by using a numerical groundwater-flow model.
The North Fork Red River aquifer in Beckham, Greer, Jackson, Kiowa, and Roger Mills Counties in Oklahoma is composed of about 777 square miles (497,582 acres) of alluvium and terrace deposits along the North Fork Red River and tributaries, including Sweetwater Creek, Elk Creek, Otter Creek, and Elm Fork Red River. The North Fork Red River is the primary source of surface-water inflow to Lake Altus, which overlies the North Fork Red River aquifer. Lake Altus is a U.S. Bureau of Reclamation reservoir with the primary purpose of supplying irrigation water to the Lugert-Altus Irrigation District.
A hydrogeologic framework was developed for the North Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of a numerical groundwater-flow model of the North Fork Red River aquifer described in this report. A conceptual model of aquifer inflows and outflows was developed for the North Fork Red River aquifer to constrain the construction and calibration of a numerical groundwater-flow model that reasonably represented the groundwater-flow system. The conceptual-model water budget estimated mean annual inflows to and outflows from the North Fork Red River aquifer for the period 1980–2013 and included a sub-accounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from Lake Altus. The numerical groundwater-flow model simulated the period 1980–2013 and was calibrated to water-table-altitude observations at selected wells, monthly base flow at selected streamgages, net streambed seepage as estimated for the conceptual model, and Lake Altus stage.
Groundwater-availability scenarios were performed by using the calibrated numerical groundwater-flow model to (1) estimate the EPS pumping rate that guarantees a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical (10-year) drought on base flow and groundwater storage. The results of the groundwater-availability scenarios could be used by the Oklahoma Water Resources Board to reevaluate the maximum annual yield of groundwater from the North Fork Red River aquifer.
EPS scenarios for the North Fork Red River aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 0.59, 0.52, and 0.52 acre-foot per acre per year, respectively. Given the 497,582-acre aquifer area, these rates correspond to annual yields of about 294,000, 259,000, and 259,000 acre-feet per year, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 951,000 acre-feet, or about 1,317,000 acre-feet (58 percent) less than the starting EPS scenario storage. This decrease in storage was equivalent to a mean water-level decline of about 22 feet. Most areas of the active alluvium near the North Fork Red River, Elk Creek, and Elm Fork Red River remained partially saturated through the end of the EPS scenario because of streambed seepage. Lake Altus storage was reduced to zero after 6–7 years of EPS pumping in each scenario.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the North Fork Red River aquifer and base flows in the North Fork Red River upstream from Lake Altus. The effects of well withdrawals were evaluated by comparing changes in groundwater storage and base flow between four 50-year scenarios using (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2013), (3) 2013 pumping rates, and (4) increasing demand pumping rates. The increasing demand pumping rates assumed a 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwest Oklahoma.
Groundwater storage after 50 years with no pumping was about 2,606,000 acre-feet, or 137,000 acre-feet (5.5 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 2.3 feet. Groundwater storage after 50 years with the mean pumping rate for the study period (1980–2013) was about 2,476,000 acre-feet, or about 7,000 acre-feet (0.3 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-level increase of 0.1 foot. Groundwater storage at the end of the 50-year period with 2013 pumping rates was about 2,398,000 acre-feet, or about 70,000 acre-feet (2.8 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.2 feet. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was about 2,361,000 acre-feet, or about 107,000 acre-feet (4.3 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-level decline of 1.8 feet. Mean annual base flow simulated at the Carter streamgage (07301500) on North Fork Red River increased by about 4,000 acre-feet (10 percent) after 50 years with no pumping and decreased by about 5,400 acre-feet (13 percent) after 50 years with increasing demand pumping rates. Mean annual base flow simulated at the North Fork Red River inflow to Lake Altus increased by about 7,400 acre-feet (15 percent) after 50 years with no pumping and decreased by about 5,800 acre-feet (12 percent) after 50 years with increasing demand pumping rates.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage and Lake Altus stage and storage. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1984–1993). Groundwater storage at the end of the drought period was about 2,271,000 acre-feet, or about 426,000 acre-feet (15.8 percent) less than the groundwater storage of the calibrated numerical model. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 7.1 feet. At the end of the 10-year hypothetical drought period, base flows at the Sweetwater (07301420), Carter (07301500), Headrick (07305000), and Snyder (07307010) streamgages had decreased by about 37, 61, 44, and 45 percent, respectively. The minimum Lake Altus storage simulated during the drought period was 403 acre-feet, which was a decline of 92 percent from the nondrought storage. Reduced base flows in the North Fork Red River were the primary cause of Lake Altus storage declines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175098","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., Ellis, J.H., Wagner, D.L., and Peterson, S.M., 2017, Hydrogeology and simulated groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2017–5098, 107 p., https://doi.org/10.3133/sir20175098.","productDescription":"Report: xii, 107 p.; Data Release","numberOfPages":"124","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-071702","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5098/sir20175098.pdf","text":"Report","size":"29.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5098"},{"id":346138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5098/coverthb.jpg"},{"id":346140,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JQ0ZXH","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow and availability in the North Fork Red River aquifer, southwest Oklahoma, 1980–2013"}],"country":"United States","state":"Oklahoma","otherGeospatial":"North Fork Red River Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 34.5\n ],\n [\n -98.8,\n 34.5\n ],\n [\n -98.8,\n 35.45\n ],\n [\n -100,\n 35.45\n ],\n [\n -100,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th
Oklahoma City, OK 73116
Detecting populations of rare or cryptic species is essential for their conservation. For species like giant gartersnakes (Thamnophis gigas), conventional survey methods can be expensive and inefficient. These sampling difficulties might be overcome by modern techniques that detect deoxyribonucleic acid (DNA) shed by organisms into the environment (eDNA). We evaluated the efficacy of detecting giant gartersnake eDNA in water samples from the laboratory and at locations with known giant gartersnake populations in the Sacramento Valley of California, and failed to detect giant gartersnake DNA in most laboratory and all field samples. Aspects of giant gartersnake biology—such as highly keratinized skin and spending extensive time in the terrestrial environment, as well as hot, sunny, and turbid conditions in wetlands and canals of the Sacramento Valley—likely contributed to low detection probabilities. Although detection of eDNA shows promise under many conditions, further development is needed before sampling for eDNA is a viable option for detecting giant gartersnake populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171123","collaboration":"Prepared in cooperation with the Central Valley Project—Bureau of Reclamation and U.S. Fish and Wildlife Service","usgsCitation":"Halstead, B.J., Wood, D.A, Bowen, Lizabeth, Waters, Shannon, Vandergast, A.G., Ersan, J.S.M., Skalos, S.M., and Casazza, M.L., 2017, An evaluation of the efficacy of using environmental DNA (eDNA) to detect giant gartersnakes (Thamnophis gigas): U.S. Geological Survey Open-File Report 2017-1123, 41 p., https://doi.org/10.3133/ofr20171123.","productDescription":"vi, 41 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-086792","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":346165,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1123/ofr20171123.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1123"},{"id":346180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1123/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Colusa National Wildlife Refuge, Natomas Basin","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175087","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"McShane, R.R., Driscoll, K.P., and Sando, Roy, 2017, A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents: U.S. Geological Survey Scientific Investigations Report 2017–5087, 19 p., https://doi.org/10.3133/sir20175087.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-083112","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":346073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5087/coverthb.jpg"},{"id":346074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5087/sir20175087.pdf","text":"Report","size":"678 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5087"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Dauphin Island is a 26-km-long barrier island located southwest of Mobile Bay, Alabama, in the north-central Gulf of Mexico. The island contains sandy beaches, dunes, maritime forests, freshwater ponds and intertidal wetlands, providing habitat for many endangered and threatened species. Dauphin Island also provides protection for and maintains estuarine conditions within Mississippi Sound, supporting oyster habitat and seagrasses. Wetland marshes along the Alabama mainland are protected by the island from wave-induced erosion during storms approaching from the Gulf of Mexico. Over the years, the island has been eroded by storms, most recently by Hurricane Ivan (2004) and Hurricane Katrina (2005) (Ivan/Katrina), which breached the island along its narrowest extent and caused damage to infrastructure. Along with storms producing significant episodic change, long-term beach erosion has exposed numerous pine tree stumps in the shoreface. The stumps are remnants of past maritime forests and reflect the consistent landward retreat of the island.
Island change has prompted the State of Alabama to evaluate restoration alternatives to increase island resilience and sustainability by protecting and preserving the natural habitat, and by understanding the processes that influence shoreline change. Under a grant from the National Fish and Wildlife Foundation, restoration alternatives are being developed that will allow the State to make decisions on engineering and ecological restoration designs based on scientific analysis of likely outcomes and tradeoffs between impacts to stakeholder interests. Science-based assessment of the coastal zone requires accurate and up-to-date baseline data to provide a valid image of present conditions and to support modeling of coastal processes. Bathymetric elevation measurements are essential to this requirement. In August 2015, the U.S. Army Corps of Engineers and the U.S. Geological Survey conducted single beam and multibeam bathymetric surveys around Dauphin Island using a variety of shallow draft vessels and equipment. More than 95 square kilometers of seafloor was imaged. The data were integrated into a seamless digital elevation model to provide a high-resolution bathymetric map of the seafloor extending 9.5 kilometers seaward from the island’s eastern end and approximately 2 km along the rest of the island on the gulf and sound sides. Water depths range from 0.3 to 15.0 meters (m), with depths greater than 10.0 m constrained to the Mobile ship channel on the extreme eastern flank of the coverage.
To measure seafloor change, two periods of historic hydrographic survey data were acquired from the National Oceanic and Atmospheric Administration National Centers for Environmental Information data archive. The two timeframes (1987–1988 and 2005–2007) were selected for their completeness of spatial coverage and because they encompass a period of significant storm impacts to the island. These timeframes were compared to each other and with the 2015 dataset to monitor elevation gain (sediment accretion) and elevation loss (sediment erosion) over time. Sediment dynamics is by far the most significant driver of nearshore elevation change in this area. The Mississippi-Alabama inner shelf is a passive margin, and other influences on elevation change (for example, tectonic adjustment, Holocene subsidence, and eustatic sea-level rise) are neither significant nor variable enough over this time period to have an imprint.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171112","usgsCitation":"Flocks, J.G., DeWitt, N.T., and Stalk, C.A., 2018, Analysis of seafloor change around Dauphin Island, Alabama, 1987–2015 (ver. 1.1, February 2018):St. Petersburg Science Center
U.S. Geological Survey
600 4th Street, South
St Petersburg, FL 33701
In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Some upstream stakeholders have expressed concerns about the dynamics of sedimentation and flood flows in the transition zone between major rivers and Grand Lake O’ the Cherokees. To relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission, the hydraulic models for these rivers require high-resolution bathymetric data along the river channels. In support of the Federal Energy Regulatory Commission relicensing process, the U.S. Geological Survey, in cooperation with the Grand River Dam Authority, performed bathymetric surveys of (1) the Neosho River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, (2) the Spring River from the Oklahoma border to the U.S. Highway 60 bridge at Twin Bridges State Park, and (3) the Elk River from Noel, Missouri, to the Oklahoma State Highway 10 bridge near Grove, Oklahoma. The Neosho River and Spring River bathymetric surveys were performed from October 26 to December 14, 2016; the Elk River bathymetric survey was performed from February 27 to March 21, 2017. Only areas inundated during those periods were surveyed.
The bathymetric surveys covered a total distance of about 76 river miles and a total area of about 5 square miles. Greater than 1.4 million bathymetric-survey data points were used in the computation and interpolation of bathymetric-survey digital elevation models and derived contours at 1-foot (ft) intervals. The minimum bathymetric-survey elevation of the Neosho River was 709.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 34.22 ft. The minimum bathymetric-survey elevation of the Spring River was 714.18 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 29.22 ft. The minimum bathymetric-survey elevation of the Elk River was 715.62 ft above North American Vertical Datum of 1988, which corresponds to a maximum depth of 27.78 ft.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175101","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., Ashworth, C.E., and Smith, S.J., 2017, Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17 (ver. 1.1, October 2017): U.S. Geological Survey Scientific Investigations Report 2017–5101, 59 p., https://doi.org/10.3133/sir20175101.","productDescription":"Report: 59 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087073","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":346091,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71N7ZMS","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17"},{"id":348038,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5101/coverthb2.jpg"},{"id":348039,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5101/sir20175101_ver1.1.pdf","text":"Report","size":"75.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5101"},{"id":348040,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5101/versionHist.txt","text":"Version History","size":"1.68 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Neosho River, Spring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1,\n 36.8\n ],\n [\n -94.5667,\n 36.8\n ],\n [\n -94.5667,\n 37.0333\n ],\n [\n -95.1,\n 37.0333\n ],\n [\n -95.1,\n 36.8\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.7,\n 36.6667\n ],\n [\n -94.4667,\n 36.6667\n ],\n [\n -94.4667,\n 36.5333\n ],\n [\n -94.7,\n 36.5333\n ],\n [\n -94.7,\n 36.6667\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 26, 2017; Version 1.1: October 25, 2017","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 13 bridges at 8 highway crossings of the Missouri and Mississippi Rivers in the greater St. Louis, Missouri, area from May 23 to 27, 2016. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches ranging from 1,640 to 1,970 feet longitudinally and extending laterally across the active channel from bank to bank during low to moderate flood flow conditions. These bathymetric surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were observed at most surveyed piers. The observed scour holes at the surveyed bridges were examined with respect to shape and depth.
The frontal slope values determined for scour holes observed in the current (2016) study generally are similar to recommended values in the literature and to values determined for scour holes in previous bathymetric surveys. Several of the structures had piers that were skewed to primary approach flow, as indicated by the scour hole being longer on the side of the pier with impinging flow, and some amount of deposition on the leeward side, as typically has been observed at piers skewed to approach flow; however, at most skewed piers in the current (2016) study, the scour hole was deeper on the leeward side of the pier. At most of these piers, the angled approach flow was the result of a deflection or contraction of flow caused by a spur dike near the pier, which may affect flow differently than for a simple skew. At structure A6500 (site 33), the wide face of the pier footing and seal course would behave as a complex foundation, for which scour is computed differently.
Previous bathymetric surveys exist for all the sites examined in this study. A previous survey in October 2010 at most of the sites had similar flow conditions and similar results to the 2016 surveys. A survey during flood conditions in August 2011 at the sites on the Missouri River and in May 2009 at structures A4936 and A1850 (site 35) on the Mississippi River did not always indicate more substantial scour during flood conditions. At structure A6500 (site 33) on the Mississippi River, a previous survey in 2009 was part of a habitat assessment before construction of the bridge and provides unique insight into the effects of the construction of that bridge on the channel in this reach. Substantial scour was observed near the right pier, and the riprap blanket surrounding the left pier seems to limit scour near that pier. Multiple additional surveys have been completed at structures A4936 and A1850 (site 35) on the Mississippi River, and the results of these surveys also are presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175076","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2017, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, May 23–27, 2016: U.S. Geological Survey Scientific Investigations Report 2017–5076, 102 p., https://doi.org/10.3133/sir20175076.","productDescription":"Report: x, 102 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086447","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":346078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS Data Release","linkHelpText":"Bathymetry and Velocity Data from Surveys at Highway Bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":346076,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5076/coverthb.jpg"},{"id":346077,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5076/sir20175076.pdf","text":"Report","size":"17.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5076"}],"country":"United States","state":"Missouri","city":"St. Louis","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.69419860839844,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.42293213401053\n ],\n [\n -90.06935119628906,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.92843409820933\n ],\n [\n -90.69419860839844,\n 38.42293213401053\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Linear-based data-worth analysis is an efficient and straightforward method for identifying the most important data for model forecasts.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12561","usgsCitation":"Leaf, A.T., 2017, Using models to identify the best data: An example from northern Wisconsin: Groundwater, v. 55, no. 5, p. 641-645, https://doi.org/10.1111/gwat.12561.","productDescription":"5 p.","startPage":"641","endPage":"645","ipdsId":"IP-087331","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":346051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.00387573242188,\n 46.29761098988109\n ],\n [\n -90.4119873046875,\n 46.29761098988109\n ],\n [\n -90.4119873046875,\n 46.68995749641134\n ],\n [\n -91.00387573242188,\n 46.68995749641134\n ],\n [\n -91.00387573242188,\n 46.29761098988109\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-17","publicationStatus":"PW","scienceBaseUri":"59ca15ace4b017cf314041ba","contributors":{"authors":[{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711043,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}