Many plants rely on animals for seed dispersal, but are all individuals equally effective at dispersing seeds? If not, then the loss of certain individual dispersers from populations could have cascade effects on ecosystems. Despite the importance of seed dispersal for forest ecosystems, variation among individual dispersers and whether land‐use change interferes with this process remains untested. Through a large‐scale field experiment conducted on small mammal seed dispersers, we show that an individual's personality affects its choice of seeds, as well as how distant and where seeds are cached. We also show that anthropogenic habitat modifications shift the distribution of personalities within a population, by increasing the proportion of bold, active, and anxious individuals and in‐turn affecting the potential survival and dispersal of seeds. We demonstrate that preserving diverse personality types within a population is critical for maintaining the key ecosystem function of seed dispersal.
Ecological distance-based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13369","usgsCitation":"Gupta, A., Dilkina, B., Morin, D., Fuller, A.K., Royle, A., Sutherland, C., and Gomes, C., 2019, Reserve design to optimize functional connectivity and animal density: Conservation Biology, v. 35, no. 5, p. 1023-1034, https://doi.org/10.1111/cobi.13369.","productDescription":"12 p.","startPage":"1023","endPage":"1034","ipdsId":"IP-102003","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.13369","text":"Publisher Index Page"},{"id":388399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Gupta, Amrita 0000-0003-2643-5865","orcid":"https://orcid.org/0000-0003-2643-5865","contributorId":264600,"corporation":false,"usgs":false,"family":"Gupta","given":"Amrita","email":"","affiliations":[{"id":54512,"text":"Georgia Institute of Techniology","active":true,"usgs":false}],"preferred":false,"id":821726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dilkina, Bistra 0000-0002-6784-473X","orcid":"https://orcid.org/0000-0002-6784-473X","contributorId":264601,"corporation":false,"usgs":false,"family":"Dilkina","given":"Bistra","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":821727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morin, Dana","contributorId":264602,"corporation":false,"usgs":false,"family":"Morin","given":"Dana","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":821728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":821725,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":821729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sutherland, Chris","contributorId":264603,"corporation":false,"usgs":false,"family":"Sutherland","given":"Chris","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":821730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomes, Carla","contributorId":264604,"corporation":false,"usgs":false,"family":"Gomes","given":"Carla","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":821731,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203909,"text":"70203909 - 2019 - Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada","interactions":[],"lastModifiedDate":"2019-10-28T09:55:47","indexId":"70203909","displayToPublicDate":"2019-06-17T15:37:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada","docAbstract":"Conservation of montane meadows is a high priority for land and water managers given their critical role in buffering the effects of climate variability and their vulnerability to increasing temperatures and evaporative demands. Recent advances in cloud computing have provided new opportunities to examine ecological responses to climate variability over the past few decades, and at large spatial scales. In this study we characterized the sensitivities (magnitude and direction of the slope) of meadow vegetation responses to interannual variations in climate. We calculated sensitivity as the regression slope between a 35-year (1985-2016) time series of Landsat-derived vegetation indices characterizing late-season vegetation vigor and water balance variables from the Basin Characterization Model. We identified April 1 snowpack as the climate variable the majority of meadows were most sensitive to. We assessed how vegetation sensitivities to snowpack varied with hydrogeomorphic context (e.g., climate, geology, soils, watershed geometry and land cover) across the Sierra Nevada mountain range using factor analysis to reduce the dimensionality of the hydrogeomorphic data, and multiple linear regression to model sensitivity responses. We found that meadow sensitivities to snowpack varied with long-term average meadow climate, indicators of watershed subsurface water storage capacity, and indicators of meadow vegetation composition. Alpine and sub-alpine meadows with high average annual precipitation, but limited catchment subsurface storage exhibited the largest sensitivities. Our results provide a novel regional perspective on spatial patterns of meadow sensitivities to climate variability and the landscape-scale hydrogeomorphic factors that influence late-season water availability in meadow ecosystems in the Sierra Nevada.","language":"English","publisher":"Wiley","doi":"10.1002/eco.2128","usgsCitation":"Albano, C.M., McClure, M.L., Gross, S.E., Kitlasten, W., Soulard, C., Charles Morton, and Huntington, J., 2019, Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada: Ecohydrology, v. 12, no. 7, e2128, 20 p., https://doi.org/10.1002/eco.2128.","productDescription":"e2128, 20 p.","ipdsId":"IP-103662","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":467526,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2128","text":"Publisher Index Page"},{"id":364849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364848,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/abs/10.1002/eco.2128"}],"country":"United States","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -119.5,\n 37.5\n ],\n [\n -119.5,\n 40\n ],\n [\n -122,\n 40\n ],\n [\n -122,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Albano, Christine M.","contributorId":182427,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":764705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClure, Meredith L.","contributorId":216395,"corporation":false,"usgs":false,"family":"McClure","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":764706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gross, Shana E.","contributorId":216396,"corporation":false,"usgs":false,"family":"Gross","given":"Shana","email":"","middleInitial":"E.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":764707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kitlasten, Wesley","contributorId":216397,"corporation":false,"usgs":true,"family":"Kitlasten","given":"Wesley","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soulard, Christopher 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":216394,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":764704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Charles Morton","contributorId":169457,"corporation":false,"usgs":false,"family":"Charles Morton","affiliations":[{"id":25514,"text":"UNR Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":764709,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Justin","contributorId":169456,"corporation":false,"usgs":false,"family":"Huntington","given":"Justin","email":"","affiliations":[{"id":25514,"text":"UNR Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":764710,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203906,"text":"70203906 - 2019 - Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance","interactions":[],"lastModifiedDate":"2019-08-15T12:28:34","indexId":"70203906","displayToPublicDate":"2019-06-17T12:26:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance","docAbstract":"The apparent failure of ecosystems to recover from increasingly widespread disturbance is a global concern. Despite growing focus on factors inhibiting resilience and restoration, we still know very little about how demographic and population processes influence recovery. Using inverse and forward demographic modelling of 531 post‐fire sagebrush populations across the western US, we show that demographic processes during recovery from seeds do not initially lead to population growth but rather to years of population decline, low density, and risk of extirpation after disturbance and restoration, even at sites with potential to support long‐term, stable populations. Changes in population structure, and resulting transient population dynamics, lead to a > 50% decline in population growth rate after disturbance and significant reductions in population density. Our results indicate that demographic processes influence the recovery of ecosystems from disturbance and that demographic analyses can be used by resource managers to anticipate ecological transformation risk.
An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator
","language":"English","publisher":"MDPI","doi":"10.3390/app9122452","usgsCitation":"Kim, M., 2019, Airborne waveform lidar simulator using the radiative transfer of a laser pulse: Applied Sciences, v. 9, no. 12, 2452; 16 p., https://doi.org/10.3390/app9122452.","productDescription":"2452; 16 p.","ipdsId":"IP-108063","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":460357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/app9122452","text":"Publisher Index Page"},{"id":364894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":764745,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70203935,"text":"70203935 - 2019 - Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert","interactions":[],"lastModifiedDate":"2019-06-24T15:50:40","indexId":"70203935","displayToPublicDate":"2019-06-13T15:47:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert","docAbstract":"Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution.","language":"English","publisher":"MDPI","doi":"10.3390/s19122669","usgsCitation":"Nowicki, S.A., Inman, R.D., Esque, T., Nussear, K., and Edwards, C., 2019, Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert: Sensors, v. 19, no. 12, 2669; 17 p., https://doi.org/10.3390/s19122669.","productDescription":"2669; 17 p.","ipdsId":"IP-093332","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467532,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s19122669","text":"Publisher Index Page"},{"id":364965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"ARizone, California, Nevada, Utah","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 34\n ],\n [\n -113,\n 34\n ],\n [\n -113,\n 37\n ],\n [\n -119,\n 37\n ],\n [\n -119,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"12","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nowicki, Scott A","contributorId":216483,"corporation":false,"usgs":false,"family":"Nowicki","given":"Scott","email":"","middleInitial":"A","affiliations":[{"id":13339,"text":"University of New Mexico, Albuquerque","active":true,"usgs":false}],"preferred":false,"id":764840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"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":true,"id":764841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":764842,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nussear, Kenneth","contributorId":194538,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":764843,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":764844,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202483,"text":"sir20195011 - 2019 - Hydrologic and hydraulic analyses of selected streams in Richland County, Ohio","interactions":[],"lastModifiedDate":"2019-06-18T08:53:48","indexId":"sir20195011","displayToPublicDate":"2019-06-13T15:11:21","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5011","displayTitle":"Hydrologic and Hydraulic Analyses of Selected Streams in Richland County, Ohio","title":"Hydrologic and hydraulic analyses of selected streams in Richland County, Ohio","docAbstract":"Hydrologic and hydraulic analyses were done for selected reaches of Clear Fork Mohican River and Cedar Fork in Richland County, Ohio. To update and expand a portion of the Federal Emergency Management Agency detailed Flood Insurance Study, the U.S. Geological Survey (USGS) and the Muskingum Watershed Conservancy District initiated a cooperative study. The study comprised an 18.6-mile reach of the Clear Fork Mohican River and a 5.9-mile reach of Cedar Fork.
Historical streamflow data from the streamgage Clear Fork Mohican River at Bellville, Ohio (USGS station number 03131982) and regional regression equations were used to estimate instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities. The 1-percent plus flood elevation is defined by the Federal Emergency Management Agency as a flood elevation derived by using streamflows that include the average predictive error for the regression equation streamflow calculation for the Flood Risk project. This error is then added to the 1-percent annual exceedance probability flood streamflow to calculate the new 1-percent plus streamflow.
The annual exceedance probability streamflows were then used in a Hydrologic Engineering Center-River Analysis System step-backwater model to determine water-surface elevation profiles and flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probability floods and a regulatory floodway along a selected reach of each stream. The Clear Fork Mohican River model was calibrated to 16 flood events by using the current stage-streamflow relation at the streamgage Clear Fork Mohican River at Bellville, Ohio (USGS station number 03131982) and a submersible pressure transducer. Flood-inundation boundaries for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway were mapped for each stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195011","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District and Richland County","usgsCitation":"Ostheimer, C.J., 2019, Hydrologic and hydraulic analyses of selected streams in Richland County, Ohio: U.S. Geological Survey Scientific Investigations Report 2019–5011, 18 p., https://doi.org/10.3133/sir20195011.","productDescription":"Report: iv, 18 p., Data Release","numberOfPages":"26","ipdsId":"IP-100978","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":364655,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NMXM5B","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic models of the Clear Fork Mohican River and Cedar Fork in Richland County, Ohio"},{"id":364654,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5011/sir20195011.pdf","text":"Report","size":"2.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5011"},{"id":364653,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5011/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Richland county","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.66525268554688,\n 40.54850462620186\n ],\n [\n -82.33943939208984,\n 40.54850462620186\n ],\n [\n -82.33943939208984,\n 40.77352187640244\n ],\n [\n -82.66525268554688,\n 40.77352187640244\n ],\n [\n -82.66525268554688,\n 40.54850462620186\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Suite 100
Columbus, OH 43229–1737
The current research builds upon a previously published study that demonstrated the combination of Raman spectroscopy coupled with multivariate analysis (MVA) for the prediction of thermal maturity in coal by evaluating the efficacy of this method for the prediction of thermal maturity in shale. MVA techniques eliminate analyst bias in peak-fitting methods by using the full Raman spectrum, and then extricating the important spectral regions for distinguishing samples and building accurate, robust models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.58–4.59%) for 53 geographically diverse shale chip samples, and 43 shale powder samples. Separate PLS models were built using Raman spectra from shale chips or powders. The calibration sets were validated using approximately one-third of the samples to rigorously assess the predictive accuracy of the models. The root mean standard error of prediction was 0.24 for the shale chip model, and 0.28 for the shale powder model. The coefficients of determination (R2) for the cross-validated data sets were identical (0.90, chips; 0.90, powders), revealing a strong linearity despite the geographic and age diversity of the samples. This study demonstrates the validity of using PLS models for the prediction of shale VRo from Raman spectra. The MVA method described herein presents a Raman alternative to the VRo industry benchmark for assessing thermal maturity in shale that is not imperiled by the shortcomings and subjectivity of peak-fitting methods.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2019.05.156","usgsCitation":"Lupoi, J.S., Hackley, P.C., Birsic, E., Fritz, L.P., Solotky, L., Weislogel, A., and Schlaegle, S., 2019, Quantitative evaluation of vitrinite reflectance in shale using Raman spectroscopy and multivariate analysis: Fuel, v. 254, 115573, 9 p., https://doi.org/10.1016/j.fuel.2019.05.156.","productDescription":"115573, 9 p.","ipdsId":"IP-106000","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467534,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fuel.2019.05.156","text":"Publisher Index Page"},{"id":384585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": 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University","active":true,"usgs":false}],"preferred":false,"id":812664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Solotky, Logan","contributorId":243155,"corporation":false,"usgs":false,"family":"Solotky","given":"Logan","email":"","affiliations":[{"id":48649,"text":"RJ Lee Group Inc.","active":true,"usgs":false}],"preferred":false,"id":812665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weislogel, Amy","contributorId":243156,"corporation":false,"usgs":false,"family":"Weislogel","given":"Amy","email":"","affiliations":[{"id":48650,"text":"West Virginia University,Department of Geology and Geography","active":true,"usgs":false}],"preferred":false,"id":812666,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schlaegle, Steve","contributorId":243157,"corporation":false,"usgs":false,"family":"Schlaegle","given":"Steve","email":"","affiliations":[{"id":48649,"text":"RJ Lee Group Inc.","active":true,"usgs":false}],"preferred":false,"id":812667,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223299,"text":"70223299 - 2019 - A general model of temporary aquatic habitat use: Water phenology as a life history filter","interactions":[],"lastModifiedDate":"2021-08-20T13:33:52.286606","indexId":"70223299","displayToPublicDate":"2019-06-13T08:28:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1652,"text":"Fish and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"A general model of temporary aquatic habitat use: Water phenology as a life history filter","docAbstract":"Temporary aquatic habitats are not widely appreciated fish habitat. However, fish navigate the transient waters of intertidal zones, floodplains, intermittent and ephemeral streams, lake margins, seasonally frozen lakes and streams, and anthropogenic aquatic habitats across the globe to access important resources. The selective pressures imposed by water impermanence (i.e., freezing, drying, tidal fluctuations), however, operate similarly across taxa and ecosystems. These similarities are formalized into a conceptual model relating habitat use to surface water phenology. Whereas all necessary life history functions (spawning, foraging, refuge, and dispersal) can be accomplished in temporary habitats, the timing, duration, and predictability of water act as a “life history filter” to which habitats can be used and for what purpose. Habitats wet from minutes to months may all be important—albeit in different ways, for different species. If life history needs co-occur with accessibility, temporary habitats can contribute substantially to individual fitness, overall production and important metapopulation processes. This heuristic is intended to promote research, recognition and conservation of these frequently overlooked habitats that can be disproportionately important relative to their size or brevity of existence. There is a pressing need to quantify how use of temporary aquatic habitats translates to individual fitness benefits, population size and temporal stability, and ecosystem-level consequences. Temporary aquatic habitats are being impacted at an alarming rate by anthropogenic activities altering their existence, phenology, and connectivity. It is timely that scientists, managers and policymakers consider the role these habitats play in global fish production.
","language":"English","publisher":"Wiley","doi":"10.1111/faf.12386","usgsCitation":"Heim, K., Falke, J.A., McMahon, T., Wipfli, M.S., and Calle, L., 2019, A general model of temporary aquatic habitat use: Water phenology as a life history filter: Fish and Fisheries, v. 20, no. 4, p. 802-816, https://doi.org/10.1111/faf.12386.","productDescription":"15 p.","startPage":"802","endPage":"816","ipdsId":"IP-101686","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":388228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Heim, Kurt C.","contributorId":264533,"corporation":false,"usgs":false,"family":"Heim","given":"Kurt C.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":821645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":821644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McMahon, Thomas E.","contributorId":264534,"corporation":false,"usgs":false,"family":"McMahon","given":"Thomas E.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":821646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":821643,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calle, Leonardo","contributorId":264535,"corporation":false,"usgs":false,"family":"Calle","given":"Leonardo","email":"","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":821647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198478,"text":"70198478 - 2019 - Evaluating mechanisms of plant‐mediated effects on herbivore persistence and occupancy across an ecoregion","interactions":[],"lastModifiedDate":"2020-05-27T15:24:46.382467","indexId":"70198478","displayToPublicDate":"2019-06-12T10:18:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating mechanisms of plant‐mediated effects on herbivore persistence and occupancy across an ecoregion","docAbstract":"Contemporary climate change is rapidly creating one of the greatest challenges for management and conservation during the 21st century. Mountain ecosystems, which have a high degree of spatial heterogeneity and contain numerous habitat specialists, have been identified as particularly vulnerable. We used data from multiple years across sites spanning a >40 million ha ecoregion to test hypotheses regarding how community‐level characteristics of vegetation may affect a mammalian generalist herbivore, the American pika (Ochotona princeps ). We examined patterns of pika persistence across sites in the hydrographic Great Basin, and occupancy within a subset of these sites. We used mixed‐effects logistic regression models to compare evidence in support of competing explanations for each pattern within an information–theoretic framework (using Akaike's information criterion). Models reflected four hypothesized classes of mechanisms related to nutritional ecology, ecosystem function, indirect indication of climatic effects, and (synergistic) combinations of these three classes. At the site level, models reflecting synergistic effects received the most support. At the within‐site level, support appeared to be split equally among hypotheses containing predictors related to either nutritional ecology or indirect climate effects. Well‐supported predictors included cover of invasive plant species, cover of more‐xeric plant species, species evenness, and proportion of graminoid species. Our results both (1) identify important aspects of vegetation communities that may influence herbivore distribution in mountainous areas across a large, diverse geographic region, and (2) contribute to an improved understanding of how mountain ecosystems may be affected by ongoing climate change, more broadly.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2764","usgsCitation":"Wilkening, J.L., Cole, E.J., and Beever, E., 2019, Evaluating mechanisms of plant‐mediated effects on herbivore persistence and occupancy across an ecoregion: Ecosphere, v. 10, no. 6, e02764, 19 p., https://doi.org/10.1002/ecs2.2764.","productDescription":"e02764, 19 p.","ipdsId":"IP-094589","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467537,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2764","text":"Publisher Index Page"},{"id":375082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah, Wyoming","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.01513671875,\n 36.82687474287728\n ],\n [\n -112.67578124999999,\n 37.70120736474139\n ],\n [\n -112.06054687499999,\n 40.26276066437183\n ],\n [\n -112.32421875,\n 41.983994270935625\n ],\n [\n -113.04931640625,\n 43.18114705939968\n ],\n [\n -113.37890625,\n 43.50075243569041\n ],\n [\n -114.54345703125,\n 42.293564192170095\n ],\n [\n -115.57617187499999,\n 41.705728515237524\n ],\n [\n -116.3232421875,\n 41.49212083968776\n ],\n [\n -117.59765625,\n 41.27780646738183\n ],\n [\n -117.90527343750001,\n 42.08191667830631\n ],\n [\n -118.36669921875,\n 43.6599240747891\n ],\n [\n -119.02587890624999,\n 44.134913443750726\n ],\n [\n -120.25634765624999,\n 43.91372326852401\n ],\n [\n -121.17919921875001,\n 43.56447158721811\n ],\n [\n -121.06933593749999,\n 41.393294288784865\n ],\n [\n -120.7177734375,\n 40.29628651711716\n ],\n [\n -120.38818359375,\n 39.26628442213066\n ],\n [\n -118.01513671875,\n 36.82687474287728\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkening, Jennifer L. 0000-0001-8748-4578","orcid":"https://orcid.org/0000-0001-8748-4578","contributorId":127685,"corporation":false,"usgs":false,"family":"Wilkening","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7111,"text":"U. 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Ecology & Evol.Biol., PhD Student","active":true,"usgs":false}],"preferred":false,"id":741602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, Evan J.","contributorId":206741,"corporation":false,"usgs":false,"family":"Cole","given":"Evan","email":"","middleInitial":"J.","affiliations":[{"id":37388,"text":"Dept. of Environmental Science, University of San Francisco","active":true,"usgs":false}],"preferred":false,"id":741603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":147685,"corporation":false,"usgs":true,"family":"Beever","given":"Erik A.","email":"ebeever@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":true,"id":741601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205244,"text":"70205244 - 2019 - Refinement of eDNA as an early monitoring tool at the landscape-level: Study design considerations","interactions":[],"lastModifiedDate":"2019-09-10T10:05:40","indexId":"70205244","displayToPublicDate":"2019-06-12T09:56:33","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Refinement of eDNA as an early monitoring tool at the landscape-level: Study design considerations","docAbstract":"Natural resource managers use data on the spatial range of species to guide management decisions. These data come from survey or monitoring efforts that use a wide variety of tools. Environmental DNA (eDNA) is a surveillance tool that uses genetic markers for detecting species and holds potential as a tool for large-scale monitoring programs. Two challenges of eDNA-based studies are uncertainties created by imperfect capture of eDNA in collection samples (e.g., water field samples) and imperfect detection of eDNA using molecular methods (e.g., quantitative PCR). Occurrence models can be used to address these challenges, thus we use an occurrence model to address two objectives: First, determine how many samples were required to detect species using eDNA; Second, examine when and where to take samples. We collected water samples from three different habitat types in the Upper Mississippi River when both Bighead Carp and Silver Carp were known to be present based on telemetry detections. Each habitat type (backwater, tributary, and impoundment) was sampled during April, May and November. Detections of eDNA for both species varied across sites and months, but were generally low, 0 - 19.3% of samples were positive for eDNA. Overall, we found that eDNA-based sampling holds promise to be a powerful monitoring tool for resource managers, however, limitations of eDNA-based sampling include different biological and ecological characteristics of target species such as seasonal habitat usage patterns as well as aspects of different physical environments that impact the implementation of these methods such as water temperature.","language":"English","publisher":"Wiley","doi":"10.1002/eap.1951","usgsCitation":"Mize, E.L., Erickson, R.A., Merkes, C.M., Berndt, N., Bockrath, K., Credico, J., Grueneis, N., Merry, J., Mosel, K., Tuttle-Lau, M., Von Ruden, K., Amberg, J., Baerwaldt, K., Finney, S., and Monroe, E., 2019, Refinement of eDNA as an early monitoring tool at the landscape-level: Study design considerations: Ecological Applications, v. 29, no. 6, e01951, https://doi.org/10.1002/eap.1951.","productDescription":"e01951","ipdsId":"IP-099527","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences 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