The Iġnik Sikumi Gas Hydrate Exchange Field Experiment was conducted by ConocoPhillips in partnership with the U.S. Department of Energy, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey within the Prudhoe Bay Unit on the Alaska North Slope during 2011 and 2012. The primary goals of the program were to (1) determine the feasibility of gas injection into hydrate-bearing sand reservoirs and (2) observe reservoir response upon subsequent flowback in order to assess the potential for CO2 exchange for CH4 in naturally occurring gas hydrate reservoirs. Initial modeling determined that no feasible means of injection of pure CO2 was likely, given the presence of free water in the reservoir. Laboratory and numerical modeling studies indicated that the injection of a mixture of CO2 and N2 offered the best potential for gas injection and exchange. The test featured the following primary operational phases: (1) injection of a gaseous phase mixture of CO2, N2, and chemical tracers; (2) flowback conducted at downhole pressures above the stability threshold for native CH4 hydrate; and (3) an extended (30-days) flowback at pressures near, and then below, the stability threshold of native CH4 hydrate. The test findings indicate that the formation of a range of mixed-gas hydrates resulted in a net exchange of CO2 for CH4 in the reservoir, although the complexity of the subsurface environment renders the nature, extent, and efficiency of the exchange reaction uncertain. The next steps in the evaluation of exchange technology should feature multiple well applications; however, such field test programs will require extensive preparatory experimental and numerical modeling studies and will likely be a secondary priority to further field testing of production through depressurization. Additional insights gained from the field program include the following: (1) gas hydrate destabilization is self-limiting, dispelling any notion of the potential for uncontrolled destabilization; (2) gas hydrate test wells must be carefully designed to enable rapid remediation of wellbore blockages that will occur during any cessation in operations; (3) sand production during hydrate production likely can be managed through standard engineering controls; and (4) reservoir heat exchange during depressurization was more favorable than expected—mitigating concerns for near-wellbore freezing and enabling consideration of more aggressive pressure reduction.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.energyfuels.6b01909","usgsCitation":"Boswell, R., Schoderbek, D., Collett, T.S., Ohtsuki, S., White, M., and Anderson, B.J., 2017, The Iġnik Sikumi Field Experiment, Alaska North Slope: Design, operations, and implications for CO2−CH4 exchange in gas hydrate reservoirs: Energy & Fuels, v. 31, no. 1, p. 140-153, https://doi.org/10.1021/acs.energyfuels.6b01909.","productDescription":"14 p.","startPage":"140","endPage":"153","ipdsId":"IP-074604","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-14","publicationStatus":"PW","scienceBaseUri":"58772077e4b0315b4c11fe24","contributors":{"authors":[{"text":"Boswell, Ray","contributorId":12307,"corporation":false,"usgs":true,"family":"Boswell","given":"Ray","affiliations":[],"preferred":false,"id":658205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoderbek, David","contributorId":178207,"corporation":false,"usgs":false,"family":"Schoderbek","given":"David","email":"","affiliations":[],"preferred":false,"id":658206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":658204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ohtsuki, Satoshi","contributorId":150141,"corporation":false,"usgs":false,"family":"Ohtsuki","given":"Satoshi","email":"","affiliations":[{"id":17917,"text":"Japan Oil, Gas and Metals National Corporation","active":true,"usgs":false}],"preferred":false,"id":658208,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Mark","contributorId":150142,"corporation":false,"usgs":false,"family":"White","given":"Mark","email":"","affiliations":[{"id":6727,"text":"Pacific Northwest National Laboratory, Richland, WA","active":true,"usgs":false}],"preferred":false,"id":658209,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Brian J.","contributorId":147120,"corporation":false,"usgs":false,"family":"Anderson","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":658207,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70179615,"text":"70179615 - 2017 - The Bayesian group lasso for confounded spatial data","interactions":[],"lastModifiedDate":"2017-02-15T14:44:29","indexId":"70179615","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2151,"text":"Journal of Agricultural, Biological, and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"The Bayesian group lasso for confounded spatial data","docAbstract":"Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-016-0274-1","usgsCitation":"Hefley, T.J., Hooten, M., Hanks, E.M., Russell, R.E., and Walsh, D.P., 2017, The Bayesian group lasso for confounded spatial data: Journal of Agricultural, Biological, and Environmental Statistics, v. 22, no. 1, p. 42-59, https://doi.org/10.1007/s13253-016-0274-1.","productDescription":"18 p.","startPage":"42","endPage":"59","ipdsId":"IP-071980","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":333019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-05","publicationStatus":"PW","scienceBaseUri":"58760114e4b04eac8e0746d5","contributors":{"authors":[{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":657904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":657903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":178093,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":657905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179655,"text":"70179655 - 2017 - Long-term flow-through column experiments and their relevance to natural granitoid weathering rates","interactions":[],"lastModifiedDate":"2017-02-24T10:45:06","indexId":"70179655","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Long-term flow-through column experiments and their relevance to natural granitoid weathering rates","docAbstract":"Four pairs of fresh and partly-weathered granitoids, obtained from well-characterized watersheds—Merced River, CA, USA; Panola, GA, USA; Loch Vale, CO, USA, and Rio Icacos, Puerto Rico—were reacted in columns under ambient laboratory conditions for 13.8 yrs, the longest running experimental weathering study to date. Low total column mass losses (<1 wt. %), correlated with the absence of pitting or surface roughening of primary silicate grains. BET surface area (SBET) increased, primarily due to Fe-oxyhydroxide precipitation. Surface areas returned to within factors of 2 to 3 of their original values after dithionite extraction. Miscible displacement experiments indicated homogeneous plug flow with negligible immobile water, commonly cited for column experiments. Fresh granitoid effluent solute concentrations initially declined rapidly, followed by much slower decreases over the next decade. Weathered granitoid effluent concentrations increased modestly over the same time period, indicating losses of natural Fe-oxide and/or clay coatings and the increased exposure of primary mineral surfaces. Corresponding (fresh and weathered) elemental effluent concentrations trended toward convergence during the last decade of reaction. NETPATH/PHREEQC code simulations indicated non-stoichiometric dissolution involving Ca release from disseminated calcite and excess K release from interlayer biotite. Effluent 87Sr/85Sr ratios reflected a progressive weathering sequence beginning and ending with 87Sr/85Sr values of plagioclase with an additional calcite input and a radiogenic biotite excursion proportional to the granitoid ages.
Effluents became thermodynamically saturated with goethite and gibbsite, slightly under-saturated with kaolinite and strongly under-saturated with plagioclase, consistent with kinetically-limited weathering in which solutes such as Na varied with column flow rates. Effluent Na concentrations showed no clear trend with time during the last decade of reaction (fresh granitoids) or increased slowly with time (weathered granitoids). Analysis of cumulative Na release indicated that plagioclase dissolution achieved steady state in 3 of the 4 fresh granitoids during the last decade of reaction. Surface-area normalized plagioclase dissolution rates exhibited a narrow range (0.95 to 1.26 10-13 moles m-2 s-1), in spite of significant stoichiometric differences (An0.21 to An0.50). Rates were an order of magnitude slower than previously reported in shorter duration experiments but generally 2 to 3 orders of magnitude faster than corresponding natural analogs. CrunchFlow simulations indicated that more than a hundredfold decrease in column flow rates would be required to produce near-saturation reaction affinities that would start to slow plagioclase weathering to real-world levels. Extending simulations to approximate long term weathering in naturally weathered profiles required additional decreases in the intrinsic plagioclase dissolution and kaolinite precipitation rates and relatively large decreases in the fluid flow rate, implying that exposure to reactive mineral surfaces is significantly limited in the natural environment compared to column experiments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2016.11.042","usgsCitation":"White, A.F., Schulz, M., Lawrence, C.R., Vivit, D.V., and Stonestrom, D.A., 2017, Long-term flow-through column experiments and their relevance to natural granitoid weathering rates: Geochimica et Cosmochimica Acta, v. 202, p. 190-214, https://doi.org/10.1016/j.gca.2016.11.042.","productDescription":"25 p.","startPage":"190","endPage":"214","ipdsId":"IP-073779","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2016.11.042","text":"Publisher Index Page"},{"id":333021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"202","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58760113e4b04eac8e0746d1","chorus":{"doi":"10.1016/j.gca.2016.11.042","url":"http://dx.doi.org/10.1016/j.gca.2016.11.042","publisher":"Elsevier BV","authors":"White Art F., Schulz Marjorie S., Lawrence Corey R., Vivit Davison V., Stonestrom David A.","journalName":"Geochimica et Cosmochimica Acta","publicationDate":"4/2017"},"contributors":{"authors":[{"text":"White, Arthur F. afwhite@usgs.gov","contributorId":3718,"corporation":false,"usgs":true,"family":"White","given":"Arthur","email":"afwhite@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":658091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":658090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Corey R. clawrence@usgs.gov","contributorId":167122,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"clawrence@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":658092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vivit, Davison V.","contributorId":178166,"corporation":false,"usgs":false,"family":"Vivit","given":"Davison","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":658094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stonestrom, David A. 0000-0001-7883-3385 dastones@usgs.gov","orcid":"https://orcid.org/0000-0001-7883-3385","contributorId":2280,"corporation":false,"usgs":true,"family":"Stonestrom","given":"David","email":"dastones@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":658093,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179652,"text":"70179652 - 2017 - Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach","interactions":[],"lastModifiedDate":"2017-01-10T10:34:49","indexId":"70179652","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach","docAbstract":"The western population of the yellow-billed cuckoo (Coccyzus americanus) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat requirements and will be useful for management and conservation activities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.12.010","usgsCitation":"Johnson, M.J., Hatten, J.R., Holmes, J.A., and Shafroth, P.B., 2017, Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach: Ecological Modelling, v. 347, p. 50-62, https://doi.org/10.1016/j.ecolmodel.2016.12.010.","productDescription":"13 p.","startPage":"50","endPage":"62","ipdsId":"IP-075673","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":470144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2016.12.010","text":"Publisher Index Page"},{"id":333009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"347","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58760114e4b04eac8e0746d3","contributors":{"authors":[{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":167197,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":658079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":658078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Jennifer A.","contributorId":178159,"corporation":false,"usgs":false,"family":"Holmes","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":658080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":658081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70181026,"text":"70181026 - 2017 - Uranium delivery and uptake in a montane wetland, north-central Colorado, USA","interactions":[],"lastModifiedDate":"2017-02-15T11:32:06","indexId":"70181026","displayToPublicDate":"2017-01-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Uranium delivery and uptake in a montane wetland, north-central Colorado, USA","docAbstract":"Comprehensive sampling of peat, underlying lakebed sediments, and coexisting waters of a naturally uraniferous montane wetland are combined with hydrologic measurements to define the important controls on uranium (U) supply and uptake. The major source of U to the wetland is groundwater flowing through locally fractured and faulted granite gneiss of Proterozoic age. Dissolved U concentrations in four springs and one seep ranged from 20 to 83 ppb (μg/l). Maximum U concentrations are ∼300 ppm (mg/kg) in lakebed sediments and >3000 ppm in peat. Uranium in lakebed sediments is primarily stratabound in the more organic-rich layers, but samples of similar organic content display variable U concentrations. Post-depositional modifications include variable additions of U delivered by groundwater. Uranium distribution in peat is heterogeneous and primarily controlled by proximity to groundwater-fed springs and seeps that act as local point sources of U, and by proximity to groundwater directed along the peat/lakebeds contact. Uranium is initially sorbed on various organic components of peat as oxidized U(VI) present in groundwater. Selective extractions indicate that the majority of sorbed U remains as the oxidized species despite reducing conditions that should favor formation of U(IV). Possible explanations are kinetic hindrances related to strong complex formation between uranyl and humic substances, inhibition of anaerobic bacterial activity by low supply of dissolved iron and sulfate, and by cold temperatures.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.01.001","usgsCitation":"Schumann, R.R., Zielinski, R.A., Otton, J.K., Pantea, M.P., and Orem, W.H., 2017, Uranium delivery and uptake in a montane wetland, north-central Colorado, USA: Applied Geochemistry, v. 78, no. 3, p. 363-379, https://doi.org/10.1016/j.apgeochem.2017.01.001.","productDescription":"17 p.","startPage":"363","endPage":"379","ipdsId":"IP-074221","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470147,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.01.001","text":"Publisher Index Page"},{"id":335164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335496,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70Z71DQ","text":"Stratigraphic, geochemical, and hydrologic data for the Boston Peak wetland, Larimer County, CO, USA"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 38.805470223177466\n ],\n [\n -107.5341796875,\n 41.0130657870063\n ],\n [\n -103.4912109375,\n 41.0130657870063\n ],\n [\n -103.4912109375,\n 38.805470223177466\n ],\n [\n -107.5341796875,\n 38.805470223177466\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffedfe4b099f50d3e0434","contributors":{"authors":[{"text":"Schumann, R. 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Carolina\",\"nation\":\"USA \"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.
","language":"English","publisher":"Informa UK Limited","doi":"10.1080/10402381.2016.1263694","usgsCitation":"Harris, T.D., and Graham, J., 2017, Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset: Lake and Reservoir Management, no. 33, 17 p., https://doi.org/10.1080/10402381.2016.1263694.","productDescription":"17 p.","ipdsId":"IP-078030","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":335169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.94174194335936,\n 37.666429212090605\n ],\n [\n -97.94174194335936,\n 37.845037026243425\n ],\n [\n -97.72270202636717,\n 37.845037026243425\n ],\n [\n -97.72270202636717,\n 37.666429212090605\n ],\n [\n -97.94174194335936,\n 37.666429212090605\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"33","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-06","publicationStatus":"PW","scienceBaseUri":"589ffedfe4b099f50d3e0436","contributors":{"authors":[{"text":"Harris, Ted D.","contributorId":149758,"corporation":false,"usgs":false,"family":"Harris","given":"Ted","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":663305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":663304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179601,"text":"70179601 - 2017 - Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading","interactions":[],"lastModifiedDate":"2017-01-05T10:53:43","indexId":"70179601","displayToPublicDate":"2017-01-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading","docAbstract":"A freshwater “mussel mortality threshold” was explored as a function of porewater ammonium (NH4+) concentration, mussel biomass, and total nitrogen (N) utilizing a numerical model calibrated with data from mesocosms with and without mussels. A mortality threshold of 2 mg-N L−1 porewater NH4+ was selected based on a study that estimated 100% mortality of juvenile Lampsilis mussels exposed to 1.9 mg-N L−1NH4+ in equilibrium with 0.18 mg-N L−1 NH3. At the highest simulated mussel biomass (560 g m−2) and the lowest simulated influent water “food” concentration (0.1 mg-N L−1), the porewater NH4+ concentration after a 2,160 h timespan without mussels was 0.5 mg-N L−1 compared to 2.25 mg-N L−1 with mussels. Continuing these simulations while varying mussel biomass and N content yielded a mortality threshold contour that was essentially linear which contradicted the non-linear and non-monotonic relationship suggested by Strayer (2014). Our model suggests that mussels spatially focus nutrients from the overlying water to the sediments as evidenced by elevated porewater NH4+ in mesocosms with mussels. However, our previous work and the model utilized here show elevated concentrations of nitrite and nitrate in overlying waters as an indirect consequence of mussel activity. Even when the simulated overlying water food availability was quite low, the mortality threshold was reached at a mussel biomass of about 480 g m−2. At a food concentration of 10 mg-N L−1, the mortality threshold was reached at a biomass of about 250 g m−2. Our model suggests the mortality threshold for juvenile Lampsilis species could be exceeded at low mussel biomass if exposed for even a short time to the highly elevated total N loadings endemic to the agricultural Midwest.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.2838","usgsCitation":"Bril, J.S., Langenfeld, K., Just, C.L., Spak, S.N., and Newton, T., 2017, Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading: PeerJ, v. 5, e2838; 17 p., https://doi.org/10.7717/peerj.2838.","productDescription":"e2838; 17 p.","ipdsId":"IP-072131","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":470149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.2838","text":"Publisher Index Page"},{"id":332924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"586f69a2e4b01a71ba0bc8fb","contributors":{"authors":[{"text":"Bril, Jeremy S.","contributorId":178035,"corporation":false,"usgs":false,"family":"Bril","given":"Jeremy","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":657826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenfeld, Kathryn","contributorId":178036,"corporation":false,"usgs":false,"family":"Langenfeld","given":"Kathryn","email":"","affiliations":[],"preferred":false,"id":657827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Just, Craig L.","contributorId":178037,"corporation":false,"usgs":false,"family":"Just","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spak, Scott N.","contributorId":178038,"corporation":false,"usgs":false,"family":"Spak","given":"Scott","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":657829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":657825,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179508,"text":"70179508 - 2017 - Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","interactions":[],"lastModifiedDate":"2023-06-21T15:04:52.97662","indexId":"70179508","displayToPublicDate":"2017-01-04T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","title":"Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","docAbstract":"Since the introduction of West Nile virus (WNV) into North America in 1999 a number of passerine bird species have been found to play a role in the amplification of the virus. Arbovirus surveillance, observational studies and experimental studies have implicated passerine birds (songbirds, e.g., crows, American robins, house sparrows, and house finches) as significant reservoirs of WNV in North America, yet we lack a tractable passerine animal model for controlled studies of the virus. The zebra finch (Taeniopygia guttata) serves as a model system across a diversity of fields, and here we develop the zebra finch a songbird model for WNV. Like many natural hosts of WNV, we found that zebra finches developed sufficient viremia to serve as a competent host, yet in general resisted mortality from infection. In the Australian zebra finch (AZF) T. g. castanotis, we detected WNV in the majority of sampled tissues by 4 days post injection (dpi). However, WNV was not detected in tissues of sacrificed birds at 14 dpi, shortly after the development of detectable anti-WNV antibodies in the majority of birds indicating successful viral clearance. We compared susceptibility between the two zebra finch subspecies AZF and Timor zebra finch (TZF) T. g. guttata. Compared to AZF, WNV RNA was detected in a larger proportion of challenged TZF and molecular detection of virus in the serum of TZF was significantly higher than in AZF. Given the observed moderate host competence and disease susceptibility, we suggest that zebra finches are appropriate as models for the study of WNV and although underutilized in this respect, may be ideal models for the study of the many diseases carried and transmitted by songbirds.
","language":"English","publisher":"PLOS One","doi":"10.1371/journal.pone.0167876","usgsCitation":"Hofmeister, E.K., Lund, M., Shearn-Bochsler, V.I., and Balakrishnan, C.N., 2017, Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus: PLoS ONE, v. 12, no. 1, e0167876; 17 p.; Data Release, https://doi.org/10.1371/journal.pone.0167876.","productDescription":"e0167876; 17 p.; Data Release","ipdsId":"IP-075765","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":470150,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0167876","text":"Publisher Index Page"},{"id":332816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418291,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7707ZM3"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"586e181ee4b0f5ce109fcad3","contributors":{"authors":[{"text":"Hofmeister, Erik K. 0000-0002-6360-3912 ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-6360-3912","contributorId":3230,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Melissa 0000-0003-4577-2015 mlund@usgs.gov","orcid":"https://orcid.org/0000-0003-4577-2015","contributorId":177923,"corporation":false,"usgs":true,"family":"Lund","given":"Melissa","email":"mlund@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Balakrishnan, Christopher N.","contributorId":177924,"corporation":false,"usgs":false,"family":"Balakrishnan","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":657508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179447,"text":"70179447 - 2017 - Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques","interactions":[],"lastModifiedDate":"2017-05-18T11:00:47","indexId":"70179447","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques","docAbstract":"Most techniques for estimating settling velocities of natural particles have been developed for siliciclastic sediments. Therefore, to understand how these techniques apply to bioclastic environments, measured settling velocities of bioclastic sedimentary deposits sampled from a nearshore fringing reef in Western Australia were compared with settling velocities calculated using results from several common grain-size analysis techniques (sieve, laser diffraction and image analysis) and established models. The effects of sediment density and shape were also examined using a range of density values and three different models of settling velocity. Sediment density was found to have a significant effect on calculated settling velocity, causing a range in normalized root-mean-square error of up to 28%, depending upon settling velocity model and grain-size method. Accounting for particle shape reduced errors in predicted settling velocity by 3% to 6% and removed any velocity-dependent bias, which is particularly important for the fastest settling fractions. When shape was accounted for and measured density was used, normalized root-mean-square errors were 4%, 10% and 18% for laser diffraction, sieve and image analysis, respectively. The results of this study show that established models of settling velocity that account for particle shape can be used to estimate settling velocity of irregularly shaped, sand-sized bioclastic sediments from sieve, laser diffraction, or image analysis-derived measures of grain size with a limited amount of error. Collectively, these findings will allow for grain-size data measured with different methods to be accurately converted to settling velocity for comparison. This will facilitate greater understanding of the hydraulic properties of bioclastic sediment which can help to increase our general knowledge of sediment dynamics in these environments.
","language":"English","publisher":"International Association of Sedimentologists","publisherLocation":"Oxford, United Kingdom","doi":"10.1111/sed.12338","usgsCitation":"Cuttler, M.V., Lowe, R.J., Falter, J.L., and Buscombe, D.D., 2017, Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques: Sedimentology, v. 64, no. 4, p. 987-1004, https://doi.org/10.1111/sed.12338.","productDescription":"18 p.","startPage":"987","endPage":"1004","ipdsId":"IP-073934","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470155,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://admin.research-repository.uwa.edu.au/en/publications/fe5b1cde-8ee4-4296-ba73-13808106388b","text":"External Repository"},{"id":332804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"64","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-29","publicationStatus":"PW","scienceBaseUri":"586cc68ce4b0f5ce109fa939","contributors":{"authors":[{"text":"Cuttler, Michael V. W.","contributorId":177844,"corporation":false,"usgs":false,"family":"Cuttler","given":"Michael","email":"","middleInitial":"V. W.","affiliations":[],"preferred":false,"id":657256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Ryan J.","contributorId":152265,"corporation":false,"usgs":false,"family":"Lowe","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":657257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falter, James L.","contributorId":177846,"corporation":false,"usgs":false,"family":"Falter","given":"James","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":657255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179394,"text":"70179394 - 2017 - Pinyon and juniper encroachment into sagebrush ecosystems impacts distribution and survival of greater sage-grouse","interactions":[],"lastModifiedDate":"2017-01-03T11:40:21","indexId":"70179394","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Pinyon and juniper encroachment into sagebrush ecosystems impacts distribution and survival of greater sage-grouse","docAbstract":"In sagebrush (Artemisia spp.) ecosystems, encroachment of pinyon (Pinus spp.) and juniper (Juniperus spp.; hereafter, “pinyon-juniper”) trees has increased dramatically since European settlement. Understanding the impacts of this encroachment on behavioral decisions, distributions, and population dynamics of greater sage-grouse (Centrocercus urophasianus) and other sagebrush obligate species could help benefit sagebrush ecosystem management actions. We employed a novel two-stage Bayesian model that linked avoidance across different levels of pinyon-juniper cover to sage-grouse survival. Our analysis relied on extensive telemetry data collected across 6 yr and seven subpopulations within the Bi-State Distinct Population Segment (DPS), on the border of Nevada and California. The first model stage indicated avoidance behavior for all canopy cover classes on average, but individual grouse exhibited a high degree of heterogeneity in avoidance behavior of the lowest cover class (e.g., scattered isolated trees). The second stage modeled survival as a function of estimated avoidance parameters and indicated increased survival rates for individuals that exhibited avoidance of the lowest cover class. A post hoc frailty analysis revealed the greatest increase in hazard (i.e., mortality risk) occurred in areas with scattered isolated trees consisting of relatively high primary plant productivity. Collectively, these results provide clear evidence that local sage-grouse distributions and demographic rates are influenced by pinyon-juniper, especially in habitats with higher primary productivity but relatively low and seemingly benign tree cover. Such areas may function as ecological traps that convey attractive resources but adversely affect population vital rates. To increase sage-grouse survival, our model predictions support reducing actual pinyon-juniper cover as low as 1.5%, which is lower than the published target of 4.0%. These results may represent effects of pinyon-juniper cover in areas with similar ecological conditions to those of the Bi-State DPS, where populations occur at relatively high elevations and pinyon-juniper is abundant and widespread.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2016.09.001","usgsCitation":"Coates, P.S., Prochazka, B.G., Ricca, M.A., Gustafson, K.B., Ziegler, P.T., and Casazza, M.L., 2017, Pinyon and juniper encroachment into sagebrush ecosystems impacts distribution and survival of greater sage-grouse: Rangeland Ecology and Management, v. 70, no. 1, p. 25-38, https://doi.org/10.1016/j.rama.2016.09.001.","productDescription":"14 p.","startPage":"25","endPage":"38","ipdsId":"IP-074789","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2016.09.001","text":"Publisher Index Page"},{"id":332739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc68fe4b0f5ce109fa93f","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657064,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ziegler, Pilar T.","contributorId":175033,"corporation":false,"usgs":false,"family":"Ziegler","given":"Pilar","email":"","middleInitial":"T.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":657066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657067,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70179446,"text":"70179446 - 2017 - Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar","interactions":[],"lastModifiedDate":"2017-01-03T11:42:07","indexId":"70179446","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar","docAbstract":"In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.12.003","usgsCitation":"Buscombe, D.D., 2017, Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar: Environmental Modelling and Software, p. 1-18, https://doi.org/10.1016/j.envsoft.2016.12.003.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-073207","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470156,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://eartharxiv.org/gfxa6/","text":"External Repository"},{"id":332729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"89","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc68ce4b0f5ce109fa93b","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":657254,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70179438,"text":"70179438 - 2017 - Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013","interactions":[],"lastModifiedDate":"2017-02-15T15:39:56","indexId":"70179438","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013","docAbstract":"Recent studies have found insignificant or decreasing trends in time-series dissolved organic carbon (DOC) datasets, questioning the assumption that long-term DOC concentrations in surface waters are increasing in response to anthropogenic forcing, including climate change, land use, and atmospheric acid deposition. We used the weighted regressions on time, discharge, and season (WRTDS) model to estimate annual flow-normalized concentrations and fluxes to determine if changes in DOC quantity and quality signal anthropogenic forcing at 10 locations in the Mississippi River Basin. Despite increases in agriculture and urban development throughout the basin, net increases in DOC concentration and flux were significant at only 3 of 10 sites from 1997 to 2013 and ranged between −3.5% to +18% and −0.1 to 19%, respectively. Positive shifts in DOC quality, characterized by increasing specific ultraviolet absorbance at 254 nm, ranged between +8% and +45%, but only occurred at one of the sites with significant DOC quantity increases. Basinwide reductions in atmospheric sulfate deposition did not result in large increases in DOC either, likely because of the high buffering capacity of the soil. Hydroclimatic factors including annual discharge, precipitation, and temperature did not significantly change during the 17-year timespan of this study, which contrasts with results from previous studies showing significant increases in precipitation and discharge over a century time scale. Our study also contrasts with those from smaller catchments, which have shown stronger DOC responses to climate, land use, and acidic deposition. This temporal and spatial analysis indicated that there was a potential change in DOC sources in the Mississippi River Basin between 1997 and 2013. However, the overall magnitude of DOC trends was not large, and the pattern in quantity and quality increases for the 10 study sites was not consistent throughout the basin.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11072","usgsCitation":"Stackpoole, S.M., Stets, E., Clow, D.W., Burns, D.A., Aiken, G.R., Aulenbach, B.T., Creed, I., Hirsch, R.M., Laudon, H., Pellerin, B., and Striegl, R.G., 2017, Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013: Hydrological Processes, v. 31, no. 4, p. 902-915, https://doi.org/10.1002/hyp.11072.","productDescription":"14 p.","startPage":"902","endPage":"915","ipdsId":"IP-066770","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":470153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11072","text":"Publisher Index Page"},{"id":332738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-11","publicationStatus":"PW","scienceBaseUri":"586cc68ee4b0f5ce109fa93d","contributors":{"authors":[{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922 sstackpoole@usgs.gov","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":3784,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"sstackpoole@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":657186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. estets@usgs.gov","contributorId":174182,"corporation":false,"usgs":true,"family":"Stets","given":"Edward G.","email":"estets@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":657187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":657192,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Laudon, Hjalmar","contributorId":46812,"corporation":false,"usgs":true,"family":"Laudon","given":"Hjalmar","affiliations":[],"preferred":false,"id":657193,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657194,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - 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Typical structure of young redwood stands impedes the recovery of old-growth conditions, such as dominance of redwood (Sequoia sempervirens (D. Don) Endl.), distinct canopy layers and diverse understory vegetation. Young forests are commonly comprised of dense, even-aged Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and redwood stump sprouts, with simple canopy structure and little understory development. Moreover, many of these young stands are believed to be vulnerable to disturbance in the form of drought, disease and fire. Silvicultural practices are increasingly being employed by conservation agencies to restore degraded forests throughout the coast redwood range; however, prescribed fire treatments are less common and potentially under-utilized as a restoration tool. We present an early synthesis from three separate management-scale prescribed fire projects at Redwood National Park spanning 1to 7 years post-treatment. Low intensity prescribed fire had minimal effect on overstory structure, with some mortality observed in trees smaller than 30 cm diameter. Moderate to high intensity fire may be required to reduce densities of larger Douglas-fir, the primary competitor of redwood in the Park’s second growth forests. Fine woody surface fuels fully recovered by 7 years post-burn, while recruitment of larger surface fuels was quite variable. Managers of coastal redwood ecosystems will benefit by having a variety of tools at their disposal for forest restoration and management.
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2017 - No substitute for survival: Perturbation analyses using a Golden Eagle population model reveal limits to managing for take","interactions":[],"lastModifiedDate":"2019-02-21T16:36:40","indexId":"70202317","displayToPublicDate":"2017-01-01T16:36:24","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"No substitute for survival: Perturbation analyses using a Golden Eagle population model reveal limits to managing for take","docAbstract":"Conserving populations of long-lived birds of prey, characterized by a slow life-history (e.g., high survival and low reproductive output), requires a thorough understanding of how variation in their vital rates differentially affects population growth. Stochastic population modeling provides a framework for exploring variation in complex life histories to better understand how environmental and demographic variation within individual vital rates affects population dynamics. Specifically, we used life-stage simulation analysis (LSA) to identify those life-history characteristics that most affect population growth and are amenable to management actions. The Golden Eagle (Aquila chrysaetos) is a wide-ranging raptor of conservation concern, which has been adopted as a focal species for conservation planning. Golden Eagle population trends in western North America currently appear stable. Yet an expanding human footprint that may increase mortality stimulated our investigation into the ability of populations to sustain reduced survival. We fit mixed-effects models to published estimates of vital rates to estimate the mean and process variation of productivity (young fledged per pair) and survival for use in a LSA framework. As expected, breeding adult survival had the greatest relative effect on population growth, though productivity explained the most variation in growth. Based on perturbation analyses, we demonstrate that even minor reductions in breeding adult survival (<4.5%) caused otherwise stable populations to decline. Despite its importance, precise estimates of spatial and temporal variation in breeding adult survival are poorly documented. Importantly, we found that the ability for increases in reproductive output to compensate for decreased survival was very limited. To maintain stable populations, declines in survival >4% required increases in productivity that generally exceed the evolutionary potential for Golden Eagles. Our findings support the current U.S. Fish and Wildlife conservation strategy which mitigates eagle “take” via efforts to reduce mortality elsewhere.
","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-16-32.1","usgsCitation":"Tack, J., Noon, B.R., Bowen, Z.H., Strybos, L., and Fedy, B., 2017, No substitute for survival: Perturbation analyses using a Golden Eagle population model reveal limits to managing for take: Journal of Raptor Research, v. 51, no. 3, p. 258-272, https://doi.org/10.3356/JRR-16-32.1.","productDescription":"15 p.","startPage":"258","endPage":"272","ipdsId":"IP-080056","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470159,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-16-32.1","text":"Publisher Index Page"},{"id":361436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":145460,"corporation":false,"usgs":true,"family":"Tack","given":"Jason D.","email":"jtack@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":757802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noon, Barry R.","contributorId":198981,"corporation":false,"usgs":false,"family":"Noon","given":"Barry","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":757803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":757801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strybos, Lauren","contributorId":213476,"corporation":false,"usgs":false,"family":"Strybos","given":"Lauren","email":"","affiliations":[],"preferred":false,"id":757804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":757805,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202831,"text":"70202831 - 2017 - Unifying population and landscape ecology with spatial capture-recapture","interactions":[],"lastModifiedDate":"2019-03-27T14:26:42","indexId":"70202831","displayToPublicDate":"2017-01-01T15:26:20","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Unifying population and landscape ecology with spatial capture-recapture","docAbstract":"Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio‐temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture–recapture (SCR) represents an individual‐based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non‐invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1111/ecog.03170","usgsCitation":"Royle, J.A., Fuller, A.K., and Sutherland, C., 2017, Unifying population and landscape ecology with spatial capture-recapture: Ecography, v. 41, no. 3, p. 444-456, https://doi.org/10.1111/ecog.03170.","productDescription":"13 p.","startPage":"444","endPage":"456","numberOfPages":"13","ipdsId":"IP-081473","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.03170","text":"Publisher Index Page"},{"id":362501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2017-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":760180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":760181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutherland, Christopher","contributorId":214549,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":760182,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201880,"text":"70201880 - 2017 - Geophysical expression of buried range-front embayment structure: Great Sand Dunes National Park, Rio Grande rift, Colorado","interactions":[],"lastModifiedDate":"2019-01-31T15:21:20","indexId":"70201880","displayToPublicDate":"2017-01-01T15:21:14","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical expression of buried range-front embayment structure: Great Sand Dunes National Park, Rio Grande rift, Colorado","docAbstract":"Great Sand Dunes National Park and Preserve (GRSA, Colorado) lies along the eastern margin of the San Luis Basin and the tectonically active Sangre de Cristo fault system that are part of the northern Rio Grande rift. GRSA lies within a prominent embayment in the range front where two separate sections of the Sangre de Cristo fault system intersect. Fault scarps are observed along both intersecting fault zones within older basin-fill alluvium, but have been obscured by the actively migrating dunefield. The dune sand is also strongly magnetic, locally limiting the usefulness of aeromagnetic methods for mapping concealed structure. This study uses airborne geophysical methods, primarily airborne gravity gradient data, along with constraints from geologic mapping and limited subsurface data and groundwater modeling, to interpret the subsurface basin geometry and range-front structure of the embayment. Using forward modeling of the gravity gradient data and locations of faults inferred from gravity gradient and aeromagnetic lineaments, several previously unrecognized tectonic elements are interpreted adjacent to the range front. Some of the largest rift-related fault offsets are demonstrated to be basinward of the normal fault zones mapped at the surface along the range front of the Sangre de Cristo Mountains, along faults concealed under the dunefield and subparallel to the two fault sections. A fault-bounded structural bench, likely composed of Proterozoic rocks, underlies most of the high dunefield at depths of 500 m to 1 km. The bench is truncated on its southwest margin by a northwest-trending, southwest-dipping normal fault. A northeast-trending, northwest-dipping normal fault with ∼600 m of estimated relief lies under the southern margin of the dunefield and bounds a structurally higher bench of Proterozoic rocks concealed at <400 m depth near the range front. The northwest- and northeast-trending geophysical lineaments generally correspond well with the trends of faults mapped at the surface, and with both pre- and syn-rift structures in the Sangre de Cristo Mountains. Aeromagnetic anomalies are explained by variations in the magnetization of pre-rift rocks, and the strongly magnetic dune sand.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01439.1","usgsCitation":"Drenth, B.J., Grauch, V.J., Ruleman, C.A., and Schenk, J.A., 2017, Geophysical expression of buried range-front embayment structure: Great Sand Dunes National Park, Rio Grande rift, Colorado: Geosphere, v. 13, no. 3, p. 974-990, https://doi.org/10.1130/GES01439.1.","productDescription":"17 p.","startPage":"974","endPage":"990","ipdsId":"IP-080301","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":470161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01439.1","text":"Publisher Index Page"},{"id":360890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Great Sand Dunes National Park, Rio Grande rift","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64453124999999,\n 37.661809012124635\n ],\n [\n -105.48763275146483,\n 37.661809012124635\n ],\n [\n -105.48763275146483,\n 37.83636090929915\n ],\n [\n -105.64453124999999,\n 37.83636090929915\n ],\n [\n -105.64453124999999,\n 37.661809012124635\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":755752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grauch, V. J. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":152256,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":755753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruleman, Chester A. 0000-0002-1503-4591 cruleman@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-4591","contributorId":1264,"corporation":false,"usgs":true,"family":"Ruleman","given":"Chester","email":"cruleman@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":755755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schenk, Judith A","contributorId":212229,"corporation":false,"usgs":false,"family":"Schenk","given":"Judith","email":"","middleInitial":"A","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":755754,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202107,"text":"70202107 - 2017 - Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.","interactions":[],"lastModifiedDate":"2019-02-11T14:15:20","indexId":"70202107","displayToPublicDate":"2017-01-01T14:15:14","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.","docAbstract":"The national network of weather surveillance radars (NEXRAD) detects birds in flight, and has proven to be a useful remote-sensing tool for ornithological study. We used data collected during Fall 2008 to 2014 by 16 NEXRAD and four terminal Doppler weather radars (TDWR) in the northeastern U.S. to map and study the spatial distribution of landbirds shortly after they leave daytime stopover sites to embark on nocturnal migratory flights. Given observed variability in the precise timing of migratory exodus, we developed a new method to sample the onset of migration at the point of maximum rate of increase in bird densities aloft to consistently sample exodus across radars and days.
The mean linear trend in aggregate stopover densities of migrants indicated a 4% decline per year from the 2008 baseline density (29% decline over the seven years). Regionally, coastal Virginia and Maine had the steepest declines. The steepest increases in migrant densities across years occurred within the Delmarva Peninsula and in coastal Connecticut.
We used NEXRAD observations to develop models to predict potentially important stopover sites throughout USFWS Region 5. Observed NEXRAD data were positively correlated to observations from TDWR and NASA’s S-Band Dual-Polarimetric Radar (NPOL), though not strongly. Predicted densities increased with increasing hardwood cover across multiple scales and with vegetation productivity. Contrastingly, predicted densities decreased with increasing agricultural, emergent marsh and coniferous land cover, but did not change with fraction of urban cover. Stopover density increased closer to bright areas and the Atlantic coast. Moreover, interactive effects indicated that migrants were more concentrated in forested areas that were both brightly lit and near the Atlantic coast. Large areas of predicted regionally important stopover sites were located along the coastlines of Maine, Long Island Sound, New Jersey, the lower Delmarva Peninsula, within the Adirondack Mountains, Catskill Mountains, and eastern Virginia.
We also created maps of classified stopover use during bimonthly periods and at multiple-scales. Migrant densities peaked along the Adirondack Mountains early in September, and along the Atlantic coast in late September with the passage of Neotropical migrants. Stopover densities peaked in the most northern extent of Maine and New England States in late October with the departure of temperate migrants.
Ground surveys conducted at 48 forested sites within the Delmarva Peninsula and Tidewater Virginia during Fall 2013 and 2014 revealed that nocturnal migrant densities pooled across species and for 14 individual species, after accounting for temporal phenology in their passage timing, were related to factors operating at multiple scales including food resources (primarily arthropod abundance in understory) and understory shrub density at a patch scale, and latitude and proximity to the Atlantic coast at a regional scale.
We integrated field survey and radar data to estimate relative stopover duration and to identify stopover functional types among 45 sites that included data from a past study near the Gulf of Mexico. We identified four functional types spanning the gradient of short rest stops to refueling stops with variable duration of stopover in relation to food abundance. The Mid-Atlantic sites were dominated by rest stops near coastal areas and lacked quick refueling stops due to low overall food abundance. The maps and ecological understanding produced can help inform conservation planning to protect and enhance stopover sites for migratory landbirds in the future.
","language":"English","usgsCitation":"Buler, J.J., McLaren, J., Schreckengost, T., Smolinsky, J.A., Walters, E., Arnold, J.A., and Dawson, D.K., 2017, Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S., viii, 112 p.","productDescription":"viii, 112 p.","ipdsId":"IP-081122","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":361147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361129,"type":{"id":15,"text":"Index Page"},"url":"https://lccnetwork.org/resource/final-report-validation-nexrad-data-and-models-bird-migration-stopover-sites-northeast-us"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buler, Jeffrey J.","contributorId":194648,"corporation":false,"usgs":false,"family":"Buler","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":756915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McLaren, James","contributorId":213085,"corporation":false,"usgs":false,"family":"McLaren","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":756916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schreckengost, Timothy","contributorId":213086,"corporation":false,"usgs":false,"family":"Schreckengost","given":"Timothy","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":756917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smolinsky, Jaclyn A.","contributorId":202723,"corporation":false,"usgs":false,"family":"Smolinsky","given":"Jaclyn","email":"","middleInitial":"A.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":756918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walters, Eric","contributorId":213087,"corporation":false,"usgs":false,"family":"Walters","given":"Eric","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":756919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arnold, J. Andrew","contributorId":213088,"corporation":false,"usgs":false,"family":"Arnold","given":"J.","email":"","middleInitial":"Andrew","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":756920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dawson, Deanna K. 0000-0001-7531-212X ddawson@usgs.gov","orcid":"https://orcid.org/0000-0001-7531-212X","contributorId":202720,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":756914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198895,"text":"70198895 - 2017 - A pressure-limited model to estimate CO2 injection and storage capacity of saline formations: Investigating the effects of formation properties, model variables and presence of hydrocarbon reservoirs","interactions":[],"lastModifiedDate":"2021-10-26T15:54:26.608187","indexId":"70198895","displayToPublicDate":"2017-01-01T12:18:47","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A pressure-limited model to estimate CO2 injection and storage capacity of saline formations: Investigating the effects of formation properties, model variables and presence of hydrocarbon reservoirs","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IEAGHG Modelling and Risk Management Network Meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Energy Agency Greenhouse Gas R&D Programme","usgsCitation":"Jahediesfanjani, H., Warwick, P., and Anderson, S.T., 2017, A pressure-limited model to estimate CO2 injection and storage capacity of saline formations: Investigating the effects of formation properties, model variables and presence of hydrocarbon reservoirs, in IEAGHG Modelling and Risk Management Network Meeting, 17 p.","productDescription":"17 p.","ipdsId":"IP-098650","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":361074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jahediesfanjani, Hossein 0000-0001-6281-5166","orcid":"https://orcid.org/0000-0001-6281-5166","contributorId":201000,"corporation":false,"usgs":false,"family":"Jahediesfanjani","given":"Hossein","affiliations":[],"preferred":false,"id":743313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":743312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":743314,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201659,"text":"70201659 - 2017 - Climate change and collapsing thermal niches of Mexican endemic reptiles","interactions":[],"lastModifiedDate":"2018-12-21T09:36:43","indexId":"70201659","displayToPublicDate":"2017-01-01T11:43:46","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Climate change and collapsing thermal niches of Mexican endemic reptiles","docAbstract":"Recent climate change should result in expansion of species to northern or high elevation range margins, and contraction at southern and low elevation margins due to extinction. Climate models predict dramatic extinctions and distributional shifts in the next century, but there are few ground-truths of these dire forecasts leading to uncertainty in predicting extinctions due to climate change. Previously, we reported on recent extinctions of Mexican Sceloporus lizards by comparing recent surveys to historical distributional records for 48 species at 200 sites. We also ground-truthed extinctions on five continents across 8 lizard families by comparing observed and predicted extinctions from an eco-physiological species distribution model and obtained a high R 2 of 0.72 (1, 2). Here, we derive more detailed predictions for 15 terrestrial reptile families and 142 species for the Mexican and California Biogeographic provinces using all known museum occurrence records, and detailed measures on eco-physiology. We adopt the eco-physiological model of extinction developed earlier but use a species-specific model. We predict massive and rapid extinctions of 22% of the reptile populations in Mexico within the next 50 years. We also predict that 3 of 15 reptile families, all three endemic to the Mexican and Californian biogeographic provinces, will go extinct by 2070, the hallmark of the beginnings of a mass extinction event. However, extinctions may be attenuated by forest cover and by presence of montane environments in contemporary ranges. We describe impacts of altitude on three species (Gopherus morafkai, G. evgoodei, and Gambelia sila) to illustrate regional management strategies (AZ-Mexico, Sinoloa, CA) for reserves in tandem with global strategies of CO2 limits that might limit climate impacts. By carefully selecting new montane preserves adjacent to desert and tropical forest habitats, and by implementing global controls on atmospheric CO2 emissions, extinctions may be reduced to less than 11% of species and only a single reptile family.
","language":"English","publisher":"University of California Mexico Initiative","usgsCitation":"Sinervo, B., Miles, D.B., Lara Resendiz, R.A., Lovich, J.E., Ennen, J.R., Muller, J., Cooper, R.D., Rosen, P.C., Stewart, J.A., Santos, J.C., Sites, J.W., Gibbons, P., Goode, E., Hillard, L.S., Welton, L., Agha, M., Caetano, G., Vaughn, M., Melendez Torres, C., Gadsden, H., Castenada Gaytan, G., Galina-Tessaro, P., Valle Jimenez, F.I., Valdez-Villavicencio, J.H., Martinez Mendez, N., Woolrich Pina, G., Luja Molina, V., Diaz de la Vega Perez, A., Arenas Moreno, D.M., Dominguez Guerrero, S., Fierro, N., Butterfield, S., Westpha, M., Huey, R.B., Mautz, W., Sánchez-Cordero, V., and Mendez de la Cruz, F.R., 2017, Climate change and collapsing thermal niches of Mexican endemic reptiles, 21 p.","productDescription":"21 p.","ipdsId":"IP-090219","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":360623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360587,"type":{"id":15,"text":"Index Page"},"url":"https://escholarship.org/uc/item/4xk077hp"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1cb860e4b0708288c83836","contributors":{"authors":[{"text":"Sinervo, Barry","contributorId":139508,"corporation":false,"usgs":false,"family":"Sinervo","given":"Barry","email":"","affiliations":[{"id":12781,"text":"Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA. lizardrps@gmail.com","active":true,"usgs":false}],"preferred":false,"id":754754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miles, Donald B.","contributorId":211745,"corporation":false,"usgs":false,"family":"Miles","given":"Donald","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":754770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lara Resendiz, Rafael A.","contributorId":211744,"corporation":false,"usgs":false,"family":"Lara Resendiz","given":"Rafael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":754769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":754771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ennen, Joshua R.","contributorId":83858,"corporation":false,"usgs":true,"family":"Ennen","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":754772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muller, Johannes","contributorId":211746,"corporation":false,"usgs":false,"family":"Muller","given":"Johannes","email":"","affiliations":[],"preferred":false,"id":754773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Robert D.","contributorId":211747,"corporation":false,"usgs":false,"family":"Cooper","given":"Robert","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":754774,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosen, Philip C.","contributorId":70311,"corporation":false,"usgs":true,"family":"Rosen","given":"Philip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":754775,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stewart, Joseph A. 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William","contributorId":211766,"corporation":false,"usgs":false,"family":"Mautz","given":"William","affiliations":[],"preferred":false,"id":754802,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Sánchez-Cordero, Víctor","contributorId":15544,"corporation":false,"usgs":true,"family":"Sánchez-Cordero","given":"Víctor","affiliations":[],"preferred":false,"id":754803,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Mendez de la Cruz, Fausto R.","contributorId":211767,"corporation":false,"usgs":false,"family":"Mendez de la Cruz","given":"Fausto","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":754804,"contributorType":{"id":1,"text":"Authors"},"rank":37}]}} ,{"id":70199144,"text":"70199144 - 2017 - Adaptive harvest management for the Svalbard population of pink-footed geese (Anser brachyrhynchus)","interactions":[],"lastModifiedDate":"2018-09-11T10:57:33","indexId":"70199144","displayToPublicDate":"2017-01-01T10:57:27","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":5740,"text":"EGMP Technical Report","active":true,"publicationSubtype":{"id":9}},"seriesNumber":"5","displayTitle":"Adaptive harvest management for the Svalbard population of pink-footed geese (Anser brachyrhynchus)","title":"Adaptive harvest management for the Svalbard population of pink-footed geese (Anser brachyrhynchus)","docAbstract":"This document describes progress to date on the development of an adaptive harvest-management strategy for maintaining the Svalbard population of pink-footed geese (Anser brachyrhynchus) near their target level (60,000) by providing for sustainable harvests in Norway and Denmark. Specifically, this report provides an assessment of the most recent monitoring information and its implications for the harvest management strategy.
The development of an adaptive harvest management (AHM) strategy requires specification of four elements: (a) a set of alternative population models, which bound the uncertainty about effects of harvest and other relevant environmental factors; (b) a set of probabilities (or weights) describing the relative credibility of the alternative models, which are updated each year based on a comparison of model predictions and monitoring information; (c) a set of alternative harvest quotas from which to choose; and (d) a management objective function, by which alternative harvest strategies can be evaluated and a mathematically optimal strategy identified.
By combining varying hypotheses about survival and reproduction, a suite of nine models were developed. Those models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent, and the extent to which spring temperatures influence survival and reproduction. Five of the models incorporate density-dependent mechanisms that would maintain the population near a carrying capacity (i.e., in the absence of harvest) of 65,000 – 129,000 depending on the specific model. The remaining four models are density independent and predict an exponentially growing population even with moderate levels of harvest.
The most current set of monitoring information was used to update model weights for the period 1991 – 2016. Current model weights suggest little evidence for density-dependent survival and reproduction. These results suggest that the pink-footed goose population may have recently experienced a release from density-dependent mechanisms, corresponding to the period of most rapid growth in population size. There is equivocal evidence for the effect of May temperature days in Svalbard (number of days with temperatures above freezing) on survival and reproduction.
Beginning with the 2016 hunting season, harvest quotas are chosen on an annual basis rather than every three years because of the potential to better meet management objectives. The optimal harvest strategy, however, remains “knife-edged,” meaning that small changes in resource status can precipitate large changes in the annual harvest quota. This potential outcome is likely to be of concern to hunters, and we are investigating ways in which large swings in harvest quotas might be dampened. Based on updated model probabilities, the recent observations of record-high population size (88,000), the aboveaverage proportion of the population comprised of one-year-old birds (0.196), and temperature days in Svalbard (4), the suggested harvest quota for the 2017 hunting season is 36,000. Last year the quota was 25,000, yet a harvest of only 16,143 was realized. We are increasingly concerned that with the return of average spring temperatures in Svalbard, the population will continue to grow beyond managers’ ability to control it, as is the case with many goose populations in Europe and North America.
","language":"English","publisher":"AEWA European Goose Management Platform","usgsCitation":"2017, Adaptive harvest management for the Svalbard population of pink-footed geese (Anser brachyrhynchus): EGMP Technical Report 5, 20 p.","productDescription":"20 p.","ipdsId":"IP-087735","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":357224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357099,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://egmp.aewa.info/sites/default/files/download/population_status_reports/EGMP_005_Pink-footed%20Goose%20AHM%20Report%202017.pdf"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a4b4e4b0702d0e84308d","contributors":{"compilers":[{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":744732,"contributorType":{"id":3,"text":"Compilers"},"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":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":744733,"contributorType":{"id":3,"text":"Compilers"},"rank":2}]}} ,{"id":70195834,"text":"70195834 - 2017 - A global analysis of traits predicting species sensitivity to habitat fragmentation","interactions":[],"lastModifiedDate":"2018-03-06T11:55:14","indexId":"70195834","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"A global analysis of traits predicting species sensitivity to habitat fragmentation","docAbstract":"Aim
Elucidating patterns in species responses to habitat fragmentation is an important focus of ecology and conservation, but studies are often geographically restricted, taxonomically narrow or use indirect measures of species vulnerability. We investigated predictors of species presence after fragmentation using data from studies around the world that included all four terrestrial vertebrate classes, thus allowing direct inter-taxonomic comparison.
Location
World-wide.
Methods
We used generalized linear mixed-effect models in an information theoretic framework to assess the factors that explained species presence in remnant habitat patches (3342 patches; 1559 species, mostly birds; and 65,695 records of patch-specific presence–absence). We developed a novel metric of fragmentation sensitivity, defined as the maximum rate of change in probability of presence with changing patch size (‘Peak Change’), to distinguish between general rarity on the landscape and sensitivity to fragmentation per se.
Results
Size of remnant habitat patches was the most important driver of species presence. Across all classes, habitat specialists, carnivores and larger species had a lower probability of presence, and those effects were substantially modified by interactions. Sensitivity to fragmentation (measured by Peak Change) was influenced primarily by habitat type and specialization, but also by fecundity, life span and body mass. Reptiles were more sensitive than other classes. Grassland species had a lower probability of presence, though sample size was relatively small, but forest and shrubland species were more sensitive.
Main conclusions
Habitat relationships were more important than life-history characteristics in predicting the effects of fragmentation. Habitat specialization increased sensitivity to fragmentation and interacted with class and habitat type; forest specialists and habitat-specific reptiles were particularly sensitive to fragmentation. Our results suggest that when conservationists are faced with disturbances that could fragment habitat they should pay particular attention to specialists, particularly reptiles. Further, our results highlight that the probability of presence in fragmented landscapes and true sensitivity to fragmentation are predicted by different factors.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.12509","usgsCitation":"Keinath, D., Doak, D.F., Hodges, K.E., Prugh, L.R., Fagan, W., Sekercioglu, C., Buchart, S.H., and Kauffman, M., 2017, A global analysis of traits predicting species sensitivity to habitat fragmentation: Global Ecology and Biogeography, v. 26, no. 1, p. 115-127, https://doi.org/10.1111/geb.12509.","productDescription":"13 p.","startPage":"115","endPage":"127","ipdsId":"IP-065257","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.12509","text":"Publisher Index Page"},{"id":352265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-23","publicationStatus":"PW","scienceBaseUri":"5afee8ebe4b0da30c1bfc4d6","contributors":{"authors":[{"text":"Keinath, Douglas","contributorId":12747,"corporation":false,"usgs":true,"family":"Keinath","given":"Douglas","affiliations":[],"preferred":false,"id":730340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doak, Daniel F.","contributorId":46811,"corporation":false,"usgs":true,"family":"Doak","given":"Daniel","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":730341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodges, Karen E.","contributorId":202978,"corporation":false,"usgs":false,"family":"Hodges","given":"Karen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":730342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prugh, Laura R. 0000-0001-9045-3107","orcid":"https://orcid.org/0000-0001-9045-3107","contributorId":196572,"corporation":false,"usgs":false,"family":"Prugh","given":"Laura","email":"","middleInitial":"R.","affiliations":[{"id":13194,"text":"School of Environmental and Forest Sciences, University of Washington","active":true,"usgs":false}],"preferred":false,"id":730343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":730344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sekercioglu, Cagan H.","contributorId":202979,"corporation":false,"usgs":false,"family":"Sekercioglu","given":"Cagan H.","affiliations":[],"preferred":false,"id":730345,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buchart, Stuart H. M.","contributorId":202980,"corporation":false,"usgs":false,"family":"Buchart","given":"Stuart","email":"","middleInitial":"H. M.","affiliations":[],"preferred":false,"id":730346,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":730218,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193237,"text":"70193237 - 2017 - Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes","interactions":[],"lastModifiedDate":"2017-11-22T17:05:17","indexId":"70193237","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes","docAbstract":"Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (Aquila chrysaetos). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.
","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-16-77.1","usgsCitation":"Wiens, J.D., Schumaker, N.H., Inman, R.D., Esque, T., Longshore, K.M., and Nussear, K.E., 2017, Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes: Journal of Raptor Research, v. 51, no. 3, p. 234-257, https://doi.org/10.3356/JRR-16-77.1.","productDescription":"24 p.","startPage":"234","endPage":"257","ipdsId":"IP-079327","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":470164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-16-77.1","text":"Publisher Index Page"},{"id":347904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"51","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98bbae4b0531197afa004","contributors":{"authors":[{"text":"Wiens, J. David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":468,"corporation":false,"usgs":true,"family":"Wiens","given":"J.","email":"jwiens@usgs.gov","middleInitial":"David","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":718668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schumaker, Nathan H.","contributorId":199151,"corporation":false,"usgs":false,"family":"Schumaker","given":"Nathan","email":"","middleInitial":"H.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":718669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Inman, Richard D. rdinman@usgs.gov","contributorId":3316,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":718670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd C. tesque@usgs.gov","contributorId":127766,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":718671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":718672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":718673,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194036,"text":"70194036 - 2017 - Influence of the Eastern California Shear Zone on deposition of the Mio-Pliocene Bouse Formation: Insights from the Cibola area, Arizona","interactions":[],"lastModifiedDate":"2017-12-11T15:08:44","indexId":"70194036","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Influence of the Eastern California Shear Zone on deposition of the Mio-Pliocene Bouse Formation: Insights from the Cibola area, Arizona","docAbstract":"The Eastern California Shear Zone (ECSZ) is a wide zone of late Cenozoic strike-slip faults and related diffuse deformation that currently accommodates ~20–25% of relative Pacific–North America plate motion in the lower Colorado River region (Fig. 1A; Dokka and Travis, 1990; Miller et al., 2001; Guest et al., 2007; Mahan et al., 2009). The ECSZ is kinematically linked southward to dextral faults in the northern Gulf of California (Bennett et al., 2016a), and it may have initiated ca. 8 Ma when major strike-slip faults developed in the northern Gulf and Salton Trough region (Bennett et al., 2016b; Darin et al., 2016; Woodburne, 2017). Thus deformation related to the ECSZ occurred in the lower Colorado River region during deposition of the Bouse Formation, which is commonly bracketed between 6.0 and 4.8 Ma (House et al., 2008; Sarna-Wojcicki et al., 2011; Spencer et al., 2013) and may be as old as 6–7 Ma in the south (McDougall and Miranda Martínez, 2014, 2016). Post-4.5 Ma broad sagging is recognized along the lower Colorado River (Howard et al., 2015), but the possibility that faults of the ECSZ influenced local to regional subsidence patterns during deposition of the Bouse Formation has received little attention to date (e.g., Homan, 2014; O’Connell et al., 2016).
The Bouse Formation is a widespread sequence of late Miocene to early Pliocene deposits exposed discontinuously along the lower Colorado River corridor (Fig. 1A). In the southern Blythe basin it consists of three regionally correlative members: (1) Basal Carbonate, consisting of supratidal and intertidal mud-flat marls, intertidal and shallow subtidal bioclastic grainstone and conglomerate, and subtidal marl; (2) Siliciclastic member, consisting of Colorado River-derived green claystone, red mudstone and siltstone, and cross-bedded river channel sandstone; and (3) Upper Bioclastic member fossiliferous sandy calcarenite, coarse pebbly grainstone, and calcareous-matrix conglomerate (Homan, 2014; Dorsey et al., 2016; O’Connell et al., 2016, 2017). The southern Bouse Formation has been interpreted as recording deposition in either a lake (Spencer and Patchett, 1997; Spencer et al., 2008, 2013; Bright et al., 2016) or shallow marine setting (Buising, 1990; McDougall, 2008; McDougall and Miranda Martínez, 2014; O’Connell et al., 2017).
In this paper we summarize key results from five field seasons of detailed stratigraphic analysis south of Cibola, Ariz. ( . 1). The data reveal systematic stratal thinning and thickening, pinch-outs, and wedging patterns in the Bouse Formation that we conclude were produced by syn-depositional tilting in response to growth of normal faults near the eastern margin of the basin. Similar stratal patterns in other nearby areas suggest widespread structural controls on deposition of the Bouse Formation. A palinspastic reconstruction of the lower Colorado River region at 5 Ma, modified from Bennett et al. (2016), provides insight to regional fault geometries in the ECSZ that may have controlled syn-depositional tilting and subsidence in Bouse depocenters shortly prior to and during initiation of the Colorado River.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2017 Desert Symposium Field Guide and Proceedings - ECSZ does it: Revisiting the eastern California Shear Zone","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"California State University Desert Studies Center","usgsCitation":"Dorsey, R.J., O’Connell, B., Homan, M.B., and Bennett, S.E., 2017, Influence of the Eastern California Shear Zone on deposition of the Mio-Pliocene Bouse Formation: Insights from the Cibola area, Arizona, in 2017 Desert Symposium Field Guide and Proceedings - ECSZ does it: Revisiting the eastern California Shear Zone, p. 150-157.","productDescription":"8 p.","startPage":"150","endPage":"157","ipdsId":"IP-084485","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":349924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349922,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23be7","contributors":{"authors":[{"text":"Dorsey, Rebecca J.","contributorId":167712,"corporation":false,"usgs":false,"family":"Dorsey","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":24813,"text":"University of Oregan","active":true,"usgs":false}],"preferred":false,"id":721953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Connell, Brennan","contributorId":200336,"corporation":false,"usgs":false,"family":"O’Connell","given":"Brennan","email":"","affiliations":[],"preferred":false,"id":721954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homan, Mindy B.","contributorId":200337,"corporation":false,"usgs":false,"family":"Homan","given":"Mindy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":721955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":721952,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193154,"text":"70193154 - 2017 - A network model framework for prioritizing wetland conservation in the Great Plains","interactions":[],"lastModifiedDate":"2017-11-20T16:32:30","indexId":"70193154","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A network model framework for prioritizing wetland conservation in the Great Plains","docAbstract":"Context
Playa wetlands are the primary habitat for numerous wetland-dependent species in the Southern Great Plains of North America. Plant and wildlife populations that inhabit these wetlands are reciprocally linked through the dispersal of individuals, propagules and ultimately genes among local populations.
Objective
To develop and implement a framework using network models for conceptualizing, representing and analyzing potential biological flows among 48,981 spatially discrete playa wetlands in the Southern Great Plains.
Methods
We examined changes in connectivity patterns and assessed the relative importance of wetlands to maintaining these patterns by targeting wetlands for removal based on network centrality metrics weighted by estimates of habitat quality and probability of inundation.
Results
We identified several distinct, broad-scale sub networks and phase transitions among playa wetlands in the Southern Plains. In particular, for organisms that can disperse >2 km a dense and expansive wetland sub network emerges in the Southern High Plains. This network was characterized by localized, densely connected wetland clusters at link distances (h) >2 km but <5 km and was most sensitive to changes in wetland availability (p) and configuration when h = 4 km, and p = 0.2–0.4. It transitioned to a single, large connected wetland system at broader spatial scales even when the proportion of inundated wetland was relatively low (p = 0.2).
Conclusions
Our findings suggest that redundancy in the potential for broad and fine-scale movements insulates this system from damage and facilitates system-wide connectivity among populations with different dispersal capacities.