There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
","language":"English","publisher":"Wiley","doi":"10.1016/j.ecolind.2020.106172","usgsCitation":"Rappaport, D.I., Royle, J.A., and Morton, D.C., 2020, Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes: Ecological Indicators, v. 113, 106172, 9 p., https://doi.org/10.1016/j.ecolind.2020.106172.","productDescription":"106172, 9 p.","ipdsId":"IP-113750","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106172","text":"Publisher Index Page"},{"id":375463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Mato Grosso","city":"Feliz Natal, Nova Ubirita","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -59.64477539062499,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -13.667338259654947\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rappaport, Danielle I.","contributorId":225138,"corporation":false,"usgs":false,"family":"Rappaport","given":"Danielle","email":"","middleInitial":"I.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":790497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":790498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morton, Douglas C.","contributorId":225139,"corporation":false,"usgs":false,"family":"Morton","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":790499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208700,"text":"70208700 - 2020 - Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms","interactions":[],"lastModifiedDate":"2020-03-11T15:59:43","indexId":"70208700","displayToPublicDate":"2020-02-15T08:52:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms","docAbstract":"Recent experimental studies have detected the presence of anoxic microzones in hyporheic sediments. These microzones are small‐scale anoxic pores, embedded within oxygen‐rich porous media and can act as anaerobic reaction sites producing reduction compounds such as nitrous oxide, a potent greenhouse gas. Microbes are a key control on nutrient transformation in hyporheic sediment, but their associated biomass growth is also capable of altering hydraulic flux, leading to potential bioclogging. Here, we developed one of the first computational modeling approaches that combined hydraulics and microbial conditions to explore the continuous evolution of microzones in stream sediments. The model assessed stream and sediment conditions with different hydraulic flux (0.1–1.0 m/day Darcy flux), nutrient concentrations (O2 = 8 mg/L, OrgC = 20 mg/L, NO−3 = 1.5–3 mg/L, and NH3 = 0.5–1 mg/L), and biomass scenarios (with and without). The model domain is a pore network model with random sized pore‐throat radii creating heterogeneous and anisotropic flow that is representative of a natural streambed composed of medium sand with a hydraulic conductivity of 0.8 m/day. Results from 30 day simulations indicate that hyporheic microzone formation will occur and microzone distributions are not simply controlled by residence time alone, rather by the complex interactions of hydraulic flux, nutrient concentrations, and biomass, with bioclogging having strong feedbacks on both hydraulics and nutrients. Under all conditions with biomass growth, anoxic microzones were unstable, perishing a few days after formation, because bioclogging primarily occurs near the influent (downwelling) area of the hyporheic zone. In turn, this bioclogging shifts transport conditions from advection‐dominated to diffusion‐dominated transport, removing all oxic regions in the hyporheic zone. Overall, results from the modeling show that anoxic microzones are likely to form under many hyporheic zone conditions, and be dynamic through space and time as they are dependent on both hydraulic flux and nutrient transport.
","language":"English","publisher":"Wiley","doi":"10.1029/2019WR025971","usgsCitation":"Chowdhury, S.R., Zarnetske, J., Phanikumar, M., Briggs, M.A., Day-Lewis, F.D., and Singha, K., 2020, Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms: Water Resources Research, v. 56, no. 3, e2019WR025971, 15 p., https://doi.org/10.1029/2019WR025971.","productDescription":"e2019WR025971, 15 p.","ipdsId":"IP-113836","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Chowdhury, S. R.","contributorId":222748,"corporation":false,"usgs":false,"family":"Chowdhury","given":"S.","email":"","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnetske, J.","contributorId":222749,"corporation":false,"usgs":false,"family":"Zarnetske","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phanikumar, M.S.","contributorId":222750,"corporation":false,"usgs":false,"family":"Phanikumar","given":"M.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":783078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":783079,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209565,"text":"70209565 - 2020 - Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","interactions":[],"lastModifiedDate":"2020-04-14T11:20:35.897788","indexId":"70209565","displayToPublicDate":"2020-02-15T06:14:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","docAbstract":"The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040644","collaboration":"","usgsCitation":"Du, L., McCarty, G.W., Zhang, X., Lang, M.W., Vanderhoof, M.K., Lin, X., Huang, C., Lee, S., and Zou, Z., 2020, Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model: Remote Sensing, v. 12, no. 4, 644, 19 p., https://doi.org/10.3390/rs12040644.","productDescription":"644, 19 p.","ipdsId":"IP-114826","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040644","text":"Publisher Index Page"},{"id":373937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.26434326171875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 38.46649284538942\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Du, Ling","contributorId":224056,"corporation":false,"usgs":false,"family":"Du","given":"Ling","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":786898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarty, Greg W.","contributorId":131149,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Xinhow","contributorId":177143,"corporation":false,"usgs":false,"family":"Zhang","given":"Xinhow","email":"","affiliations":[],"preferred":false,"id":786900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan W.","contributorId":131150,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":7264,"text":"USDA Forest Service, Northern Research Station, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":786902,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lin, Xian-Dan","contributorId":171991,"corporation":false,"usgs":false,"family":"Lin","given":"Xian-Dan","email":"","affiliations":[],"preferred":false,"id":786903,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":786904,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":786905,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zou, Zhenhua","contributorId":224062,"corporation":false,"usgs":false,"family":"Zou","given":"Zhenhua","email":"","affiliations":[],"preferred":false,"id":786946,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228549,"text":"70228549 - 2020 - The economics of territory selection","interactions":[],"lastModifiedDate":"2022-02-14T22:38:36.179198","indexId":"70228549","displayToPublicDate":"2020-02-14T16:34:07","publicationYear":"2020","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":"The economics of territory selection","docAbstract":"Territorial behavior is a fundamental and conspicuous behavior within numerous species, but the mechanisms driving territory selection remain uncertain. Theory and empirical precedent indicate that many animals select territories economically to satisfy resource requirements for survival and reproduction, based on benefits of food resources and costs of competition and travel. Costs of competition may vary by competitive ability, and costs of predation risk may also drive territory selection. Habitat structure, resource requirements, conspecific density, and predator distribution and abundance are likely to further influence territorial behavior. We developed a mechanistic, spatially-explicit, individual-based model to better understand how and why animals select particular territories. The model was based on optimal selection of individual patches for inclusion in a territory according to their net value, i.e., benefits (food resources) minus costs (travel, competition, predation risk). Simulations produced predictions for what may be observed empirically if such optimization drives placement and characteristics of territories. Simulations consisted of sequential, iterative selection of territories by simulated animals that interacted to defend and maintain territories. Results explain why certain patterns in space use are commonly observed, and when and why these patterns will differ from the norm. For example, more clumped or abundant food resources are predicted to result, on average, in smaller territories with more overlap. Strongly different resource requirements for individuals or groups in a population will directly affect space use and are predicted to cause different responses under identical conditions. Territories are predicted to decrease in size with increasing population density, which can enable a population’s density of territories to change at faster rates than their spatial distribution. Due to competition, less competitive territory-holders are generally predicted to have larger territories in order to accumulate sufficient resources, which could produce an ideal despotic distribution of territories. Interestingly, territory size and overlap are predicted to show a parabolic response to increases in predator densities, and territories are predicted to be larger where predators are more clumped in distribution. Our model’s predictions are consistent with many empirical observations, providing support for optimal patch selection as a mechanism for the economical territories of animals commonly observed in nature.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2020.109329","usgsCitation":"Mitchell, M.S., and Sells, S., 2020, The economics of territory selection: Ecological Modelling, v. 338, 109329, 15 p., https://doi.org/10.1016/j.ecolmodel.2020.109329.","productDescription":"109329, 15 p.","ipdsId":"IP-117338","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"338","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sells, Sarah N.","contributorId":276102,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah N.","affiliations":[{"id":50219,"text":"um","active":true,"usgs":false}],"preferred":false,"id":834548,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208051,"text":"sir20205006 - 2020 - Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:56:36.421159","indexId":"sir20205006","displayToPublicDate":"2020-02-14T15:35:24","publicationYear":"2020","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":"2020-5006","displayTitle":"Potential Groundwater Recharge Rates for Two Subsurface-Drained Agricultural Fields, Southeastern Minnesota, 2016–18","title":"Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","docAbstract":"Subsurface drainage is used to efficiently drain saturated soils to support productive agriculture in poorly drained terrains. Although subsurface drainage alters the water balance for agricultural fields, its effect on groundwater resources and groundwater recharge is poorly understood. In Minnesota, subsurface drainage has begun to increase in southeastern Minnesota, even though this part of the State is underlain by permeable karstic bedrock aquifers with only a thin layer of glacial sediments separating these aquifers from land surface.
To gain a better understanding of groundwater recharge effects from subsurface drainage, the U.S. Geological Survey (USGS), in cooperation with the Legislative-Citizen Commission on Minnesota Resources, led a 2-year hydrologic study to investigate this connection for two agricultural fields in southeastern Minnesota with subsurface drainage. A total of three monitoring plots were used between the two agricultural fields: two monitoring plots that included an actively drained area with peripheral, undrained areas, and a third monitoring plot without any subsurface drainage. Multiple piezometer transects were set up across the three monitoring plots to characterize the unsaturated zone and shallow water-table flow using pressure transducers and soil moisture probes. From these piezometers, groundwater recharge rates were derived using two different methods: the RISE Water-Table Fluctuation (WTF) method and the DRAINMOD model. In addition to these two methods, the USGS Soil-Water-Balance (SWB) model was used to estimate potential recharge rates for three different monitoring plots.
In addition to deriving groundwater recharge rates, the hydrologic budget was analyzed to interpret the water-table surface elevation and soil volumetric water content time series. At one of the two drained plots, the transects exhibited varying water-table surface elevation patterns. Frequent backflow from the adjacent ditch caused subsurface drainage flow to slow down or stop drainage through the main collector drain and cause pipe pressurization, so the closest transect appeared to be mostly controlled by the drain pressurization, whereas the farthest transect was more efficiently drained. Both of the drained monitoring plots had an elevation gradient parallel to the pattern tiles, sloping downward towards the collector drain that aggregated the parallel lines into a single drain. Because the transects were set at different gradients in the field, some of the water-table surface elevation differences were also attributed to lateral flow towards the lowest parts of the field.
Three methods were used to derive potential groundwater recharge rates: the RISE WTF method, the USGS SWB model, and DRAINMOD-derived deep seepage rates. Potential groundwater recharge rates, using the RISE WTF method, across all piezometers were 1.55 and 1.94 inches per year, respectively, for water years 2017 and 2018. More differentiation of potential recharge rates between different piezometer types occurred for water year 2018. Although the difference was slightly more than 1 inch between the drained and nondrained piezometers for water year 2018, this difference was statistically significant based on a t-test with a p-value of 0.036 (α=0.05). When looking at recharge based on distance from the drain, the subsurface drain did not affect potential recharge, although other factors such as variability in screen depths, well construction, and specific yield variability cannot be eliminated. The SWB model was also used to estimate potential recharge rates for water years 2017–18, with rates between 2.44 and 5.92 inches per year for the two drained sites, generally higher than the RISE WTF estimates. DRAINMOD-derived potential recharge rates were generally the highest of the three methods, with potential recharge rates varying from 2.07 to 9.49 inches per year.
Overall, there was a lack of agreement between the three methods. These results were not remarkable, considering the fundamental differences in the methodology for each method. However, all methods did show a fundamental difference between piezometers within the drained area and piezometers outside the drained area, including the third undrained monitoring plot. The drained areas show a lower overall potential groundwater recharge compared to the nondrained areas for all three estimates. The difference for the 2018 recharge estimates was slightly higher than 1 inch for the RISE WTF method, the difference was almost double for the nine sites for the DRAINMOD model, and the difference between the drain and undrained plots was even more significant for the SWB model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205006","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., and Berg, A.M., 2020, Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5006, 57 p., https://doi.org/10.3133/sir20205006.","productDescription":"Report: ix, 54 p.; 5 Appendixes; 3 Data Releases; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112919","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":372354,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendixes.xlsx","text":"Appendix 1 and 2","size":"3.55 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5006 Appendixes"},{"id":372353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006.pdf","text":"Report","size":"4.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5006"},{"id":372352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5006/coverthb.jpg"},{"id":372355,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.1.csv","text":"Appendix 1.1","size":"1.55 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.1"},{"id":372356,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.2.csv","text":"Appendix 1.2","size":"1.66 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.2"},{"id":372357,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.1.csv","text":"Appendix 2.1","size":"13.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.1"},{"id":372358,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.2.csv","text":"Appendix 2.2","size":"13.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.2"},{"id":372359,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987N30U","text":"USGS data release","linkHelpText":"DRAINMOD simulations for two agricultural drainage sites in western Fillmore County, southeastern Minnesota"},{"id":372360,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90N4AWG","text":"USGS data release","linkHelpText":"Soil-Water Balance model datasets used to estimate recharge for southeastern Minnesota, 2014–2018"},{"id":372361,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LMOPP","text":"USGS data release","linkHelpText":"Potential groundwater recharge estimates based on a groundwater rise analysis technique for two agricultural sites in southeastern Minnesota, 2016–2018"},{"id":372362,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS dataset","linkHelpText":"– USGS groundwater data for Minnesota in USGS water data for the Nation"},{"id":399628,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109687.htm"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4167,\n 43.595\n ],\n [\n -92.45,\n 43.595\n ],\n [\n -92.45,\n 43.5444\n ],\n [\n -92.4167,\n 43.5444\n ],\n [\n -92.4167,\n 43.595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
This study investigated collaborative groundwater‐flow modeling and scenario analysis in the Little Plover River basin, Wisconsin, USA where an unconfined aquifer supplies groundwater for agricultural irrigation, industrial processing, municipal water supply, and stream baseflow. We recruited stakeholders with diverse interests to identify, prioritize, and evaluate scenarios defined as management changes to the landscape. Using a groundwater flow model, we simulated the top 10 stakeholder‐ranked scenarios under historically informed dry, average, and wet weather conditions and evaluated the ability of scenarios to meet government‐defined stream flow performance measures. Results show that multiple changes to the landscape are necessary to maintain optimum stream flow, particularly during dry years. Yet, when landscape changes from three scenarios—transferring water from the local waste water treatment plant to basin headwaters, moving municipal wells further from the river and downstream, and converting 240 acre (97 ha) of irrigated land to unirrigated land—were simulated in combination, the probability of meeting or exceeding optimum flows rose to 75, 65, and 34% at upper, mid, and lower stream gages, respectively, in dry climate conditions. Discussions with stakeholders reveal that the collaborative model and scenario analysis process resulted in social learning that built upon the existing complex and dynamic institutional landscape. The approach provided a forum for solution‐based discussions, and the model served as an important mediation tool for the development and evaluation of community‐defined scenarios in a high conflict environment. Today, stakeholders continue to work collaboratively to overcome challenges and implement voluntary solutions in the basin.
Hellbenders Cryptobranchus alleganiensis are critically imperiled amphibians throughout the eastern USA. Rock-lifting is widely used to monitor hellbenders but can severely disturb habitat. We asked whether artificial shelter occupancy (the proportion of occupied shelters in an array) would function as a proxy for hellbender abundance and thereby serve as a viable alternative to rock-lifting. We hypothesized that shelter occupancy would vary spatially in response to hellbender density, natural shelter density, or both, and would vary temporally with hellbender seasonal activity patterns and time since shelter deployment. We established shelter arrays (n = 30 shelters each) in 6 stream reaches and monitored them monthly for up to 2 yr. We used Bayesian mixed logistic regression and model ranking criteria to assess support for hypotheses concerning drivers of shelter occupancy. In all reaches, shelter occupancy was highest from June-August each year and was higher in Year 2 relative to Year 1. Our best-supported model indicated that the extent of boulder and bedrock (hereafter, natural shelter) in a reach mediated the relationship between hellbender abundance and shelter occupancy. More explicitly, shelter occupancy was positively correlated with abundance when natural shelter covered <20% of a reach, but uncorrelated with abundance when natural shelter was more abundant. While shelter occupancy should not be used to infer variation in hellbender relative abundance when substrate composition varies among reaches, we showed that artificial shelters can function as valuable monitoring tools when reaches meet certain criteria, though regular shelter maintenance is critical.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01014","usgsCitation":"Bodinof Jachowski, C.M., Ross, B., and Hopkins, W., 2020, Evaluating artificial shelter arrays as a minimally invasive monitoring tool for the hellbender Cryptobranchus alleganiensis: Endangered Species Research, v. 41, p. 167-181, https://doi.org/10.3354/esr01014.","productDescription":"15 p.","startPage":"167","endPage":"181","ipdsId":"IP-107334","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01014","text":"Publisher Index Page"},{"id":395435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"upper Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.50732421875,\n 36.56260003738545\n ],\n [\n -80.540771484375,\n 36.56260003738545\n ],\n [\n -80.540771484375,\n 37.26530995561875\n ],\n [\n -82.50732421875,\n 37.26530995561875\n ],\n [\n -82.50732421875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bodinof Jachowski, C. M.","contributorId":274670,"corporation":false,"usgs":false,"family":"Bodinof Jachowski","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":833211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkins, W.A.","contributorId":274671,"corporation":false,"usgs":false,"family":"Hopkins","given":"W.A.","email":"","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":833213,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208518,"text":"70208518 - 2020 - Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry","interactions":[],"lastModifiedDate":"2020-02-14T06:20:44","indexId":"70208518","displayToPublicDate":"2020-02-13T08:04:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry","docAbstract":"Avian influenza (AI) affects wild aquatic birds and poses hazards to human health, food security, and wildlife conservation globally. Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. The study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks.","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-59077-1","usgsCitation":"Humphreys, J.M., Ramey, A., Douglas, D., Mullinax, J.M., Soos, C., Link, P.T., Walther, P., and Prosser, D.J., 2020, Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry: Scientific Reports, v. 10, https://doi.org/10.1038/s41598-020-59077-1.","productDescription":"2595, 16 p.","startPage":"16","ipdsId":"IP-110829","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457734,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59077-1","text":"Publisher Index Page"},{"id":437114,"rank":0,"type":{"id":30,"text":"Data 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0000-0002-3601-8400","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":222481,"corporation":false,"usgs":true,"family":"Ramey","given":"Andy","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":782257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":782258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":782263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soos, Catherine","contributorId":177909,"corporation":false,"usgs":false,"family":"Soos","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":782260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Link, Paul T.","contributorId":53611,"corporation":false,"usgs":false,"family":"Link","given":"Paul","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":782261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walther, Patrick","contributorId":213915,"corporation":false,"usgs":false,"family":"Walther","given":"Patrick","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":782262,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":782255,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208489,"text":"ofr20191136 - 2020 - The surface trace tool — Modeling complex planar interactions using ArcGIS","interactions":[],"lastModifiedDate":"2022-04-21T19:38:18.369387","indexId":"ofr20191136","displayToPublicDate":"2020-02-12T15:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1136","displayTitle":"The Surface Trace Tool — Modeling Complex Planar Interactions Using ArcGIS","title":"The surface trace tool — Modeling complex planar interactions using ArcGIS","docAbstract":"The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Many pathogens interact and evolve in communities where more than one host species is present, yet our understanding of host–pathogen specialization is mostly informed by laboratory studies with single species. Managing diseases in the wild, however, requires understanding how host–pathogen specialization affects hosts in diverse communities. Juvenile salmonid mortality in hatcheries caused by infectious hematopoietic necrosis virus (IHNV) has important implications for salmonid conservation programs. Here, we evaluate evidence for IHNV specialization on three salmonid hosts and assess how this influences intra‐ and interspecific transmission in hatchery‐reared salmonids. We expect that while more generalist viral lineages should pose an equal risk of infection across host types, viral specialization will increase intraspecific transmission. We used Bayesian models and data from 24 hatcheries in the Columbia River Basin to reconstruct the exposure history of hatcheries with two IHNV lineages, MD and UC, allowing us to estimate the probability of juvenile infection with these lineages in three salmonid host types. Our results show that lineage MD is specialized on steelhead trout and perhaps rainbow trout (both Oncorhynchus mykiss), whereas lineage UC displayed a generalist phenotype across steelhead trout, rainbow trout, and Chinook salmon. Furthermore, our results suggest the presence of specialist–generalist trade‐offs because, while lineage UC had moderate probabilities of infection across host types, lineage MD had a small probability of infection in its nonadapted host type, Chinook salmon. Thus, in addition to quantifying probabilities of infection of socially and economically important salmonid hosts with different IHNV lineages, our results provide insights into the trade‐offs that viral lineages incur in multihost communities. Our results suggest that knowledge of the specialist/generalist strategies of circulating viral lineages could be useful in salmonid conservation programs to control disease.
We develop a semiautomated method for estimating with second seismic moments the directivity, rupture area, duration, and centroid velocity of earthquakes. The method is applied to 41 southern California earthquakes with magnitude in the range 3.5–5.2 and provides stable results for 28 events. Apparent source time functions (ASTFs) of P and S phases are derived using deconvolution with three stacked empirical Green's functions (seGf). The use of seGf suppresses nongeneric source effects, improves the focal mechanism correspondence to the analyzed earthquakes, and typically allows inclusion of 5 to 15 more ASTFs compared with analysis using a single eGf. Most analyzed earthquakes in the Trifurcation area of the San Jacinto Fault have directivities toward the northwest, while events around Cajon Pass and San Gabriel Mountain tend to propagate toward the southeast. These results are generally consistent with predictions for dynamic rupture on bimaterial interfaces associated with the imaged velocity contrasts in the area. The second moment inversions also provide constraints on the upper and lower bounds of rupture areas in our data set. Stress drops and uncertainties are estimated for elliptical ruptures using the derived characteristic rupture length and width. The semiautomated second moment method with seGfs can be used for routine application to moderate earthquakes in locations with good station coverage.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018566","usgsCitation":"Meng, H., McGuire, J., and Ben-Zion, Y., 2020, Semiautomated estimates of directivity and related source properties of small to moderate southern California earthquakes using second seismic moments: Journal of Geophysical Research, v. 125, no. 4, e2019JB018566, 21 p., https://doi.org/10.1029/2019JB018566.","productDescription":"e2019JB018566, 21 p.","ipdsId":"IP-110976","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":377209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.20214843749999,\n 32.519026027827515\n ],\n [\n -115.059814453125,\n 32.713355353177555\n ],\n [\n -115.169677734375,\n 35.34425514918409\n ],\n [\n -116.65283203124999,\n 35.96911507577482\n ],\n [\n -120.25634765624999,\n 34.77771580360469\n ],\n [\n -120.03662109374999,\n 34.31621838080741\n ],\n [\n -117.20214843749999,\n 32.519026027827515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Meng, Haoran","contributorId":237785,"corporation":false,"usgs":false,"family":"Meng","given":"Haoran","email":"","affiliations":[{"id":47614,"text":"University of Southern California; Florida State University","active":true,"usgs":false}],"preferred":false,"id":795292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ben-Zion, Yehuda","contributorId":195741,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","email":"","affiliations":[{"id":16177,"text":"University of Southern California, Los Angeles, Ca.","active":true,"usgs":false}],"preferred":false,"id":795294,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211360,"text":"70211360 - 2020 - Bridging the research-management gap: Landscape ecology in practice on public lands in the western United States","interactions":[],"lastModifiedDate":"2020-07-29T13:39:56.528748","indexId":"70211360","displayToPublicDate":"2020-02-11T12:09:24","publicationYear":"2020","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":"Bridging the research-management gap: Landscape ecology in practice on public lands in the western United States","docAbstract":"The field of landscape ecology has grown and matured in recent decades, but incorporating landscape science into land management decisions remains challenging. Many lands in the western United States are federally owned and managed for multiple uses, including recreation, conservation, and energy development. We argue for stronger integration of landscape science into the management of these public lands. We open by outlining the relevance of landscape science for public land planning, management, and environmental effects analysis, including pertinent laws and policies. We identify challenges to integrating landscape science into public land management, including the multijurisdictional nature and complicated spatial pattern of public lands, the capacity of agencies to identify and fill landscape science needs, and public perceptions about the meaning of landscape approaches to management. We provide several recent examples related to landscape monitoring, restoration, reclamation, and conservation in which landscape science products were developed specifically to support decision-making. We close by highlighting three actions - elevating the importance of science-management partnerships dedicated to coproducing actionable landscape science products, identifying where landscape science could foster efficiencies in the land-use planning process, and developing scenario-based landscape models for shrublands - that could improve landscape science support for public land planners and managers.","language":"English","publisher":"Springer","doi":"10.1007/s10980-020-00970-5","usgsCitation":"Carter, S.K., Pilliod, D.S., Haby, T.S., Prentice, K.L., Aldridge, C., Anderson, P.J., Bowen, Z.H., Bradford, J., Cushman, S.A., DeVivo, J.C., Duniway, M.C., Hathaway, R.S., Nelson, L., Schultz, C.A., Schuster, R., Trammell, E.J., and Weltzin, J., 2020, Bridging the research-management gap: Landscape ecology in practice on public lands in the western United States: Landscape Ecology, v. 35, p. 545-560, https://doi.org/10.1007/s10980-020-00970-5.","productDescription":"16 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0000-0003-2204-9967","orcid":"https://orcid.org/0000-0003-2204-9967","contributorId":138831,"corporation":false,"usgs":false,"family":"Haby","given":"Travis","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":794025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prentice, Karen L.","contributorId":184046,"corporation":false,"usgs":false,"family":"Prentice","given":"Karen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":794026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":213471,"corporation":false,"usgs":false,"family":"Aldridge","given":"Cameron L.","affiliations":[{"id":6621,"text":"Colorado State 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Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. In 2018, the U.S. National Phenology Network (USA-NPN) released a suite of ‘Pheno Forecast’ map products relevant to science and management. The Pheno Forecasts include real-time maps and short-term forecasts of insect pest activity at management-relevant spatial and temporal resolutions and are based on accumulated temperature thresholds associated with critical life-cycle stages of economically important pests. Pheno Forecasts indicate, for a specified day, the status of the insect’s target life-cycle stage in real time across the contiguous United States. The maps are available for 12 pest species including the invasive emerald ash borer (Agrilus planipennis Fairmaire [Coleoptera: Buprestidae]), hemlock woolly adelgid (Adelges tsugae Annand), and gypsy moth (Lymantria dispar Linnaeus [Lepidoptera: Erebidae]). Preliminary validation based on in-situ observations for hemlock woolly adelgid egg and nymph stages in 2018 indicated the maps to be ≥93% accurate depending on phenophase. Since their release in early 2018, these maps have been adopted by tree care specialists and foresters across the United States. Using a consultative mode of engagement, USA-NPN staff have continuously sought input and critique of the maps and delivery from end users. Based on feedback received, maps have been expanded and modified to include additional species, improved descriptions of the phenophase event of interest, and e-mail-based notifications to support management decisions.
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Talbot","contributorId":222418,"corporation":false,"usgs":false,"family":"Trotter","given":"R.","email":"","middleInitial":"Talbot","affiliations":[{"id":40541,"text":"US Department of Agriculture Forest Service, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":782112,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Whitmore, Mark","contributorId":222419,"corporation":false,"usgs":false,"family":"Whitmore","given":"Mark","email":"","affiliations":[{"id":40542,"text":"Cornell University, Natural Resources","active":true,"usgs":false}],"preferred":false,"id":782113,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70228490,"text":"70228490 - 2020 - Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment","interactions":[],"lastModifiedDate":"2022-02-11T16:09:26.967537","indexId":"70228490","displayToPublicDate":"2020-02-11T09:10:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment","docAbstract":"The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.
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J.","contributorId":67040,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":834433,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Smith, F.","contributorId":275953,"corporation":false,"usgs":false,"family":"Smith","given":"F.","affiliations":[{"id":6686,"text":"College of William and Mary","active":true,"usgs":false}],"preferred":false,"id":834434,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tolliver, J.","contributorId":275957,"corporation":false,"usgs":false,"family":"Tolliver","given":"J.","email":"","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":834435,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Watts, Bryan D","contributorId":243507,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":834436,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70216986,"text":"70216986 - 2020 - Basin amplification effects in the Puget Lowland, Washington from strong motion recordings and 3D simulations","interactions":[],"lastModifiedDate":"2020-12-22T13:30:24.816134","indexId":"70216986","displayToPublicDate":"2020-02-11T07:26:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Basin amplification effects in the Puget Lowland, Washington from strong motion recordings and 3D simulations","docAbstract":"Sedimentary basins in the Puget Sound region, Washington State, increase ground‐motion intensity and duration of shaking during local earthquakes. We analyze Pacific Northwest Seismic Network and U.S. Geological Survey strong‐motion recordings of five local earthquakes (M 3.9–6.8), including the 2001 Nisqually earthquake, to characterize sedimentary basin effects within the Seattle and Tacoma basins. We observe basin‐edge generated surface waves at sites within the Seattle basin for most ray paths that cross the Seattle fault zone. We also note previously undocumented basin‐edge surface waves in the Tacoma basin during one of the local earthquakes. To place quantitative constraints on basin amplification, we determine amplification factors by computing the spectral ratios of inside‐basin sites to outside‐basin sites at 1, 2, 3, and 5 s periods. Ground shaking is amplified in the Seattle basin for all the earthquakes analyzed and for a subset of events in the Tacoma basin. We find that the largest amplification factors in the Seattle basin are produced by a shallow earthquake located to the southwest of the basin. Our observation suggests that future shallow crustal and megathrust earthquakes rupturing west of the Puget Lowland will produce greater amplification within the Seattle basin than has been seen for intraslab events. We also perform ground‐motion simulations using a finite‐difference method to validate a 3D Cascadia velocity model (CVM) by comparing properties of observed and synthetic waveforms up to a frequency of 1 Hz. Basin‐edge effects are well reproduced in the Seattle basin, but are less well resolved in the Tacoma basin. Continued study of basin effects in the Tacoma basin would improve the CVM.
Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.
Rockfalls and rockslides are often dominant geomorphic processes in steep bedrock landscapes, but documenting their occurrence can be challenging, requiring frequent monitoring and well resolved spatial data. Repeat application of remote sensing methods such as Terrestrial Laser Scanning (TLS) and Structure-from-Motion (SfM) photogrammetry can detect even very small rockfalls, but typically these acquisitions span only years and may not record rockfall activity representative of longer-term rates of cliff erosion. Inventory databases can extend rockfall records, but are commonly incomplete and prone to observation bias. We employed TLS and SfM on two adjacent cliffs (El Capitan and Middle Brother) in Yosemite Valley, integrating semi-annual data collections from 2010 to 2017 with “historical” (archival) SfM models derived from oblique photographs taken in 1976. Comparing the 1976 SfM models against more recent data allows for more accurate and precise rockfall detection and volume measurement over a 40-year period. Change detection indicates that 235 rockfalls occurred from the two cliffs, more than twice as many events as are recorded in Yosemite's inventory database. Although individual rockfall volumes reported in the inventory database vary from those measured by SfM-TLS, reported cumulative volumes are similar to measured volumes, likely because the large-volume events that account for most of the cumulative volume tend to be widely observed and well-documented. Volume-frequency relationships indicate that the cliffs erode predominantly by less frequent, larger-volume rockfalls, at rates of 0.9 to 1.7 mm/yr. Our study demonstrates how integrated SfM and TLS measurements, especially utilizing SfM models derived from historical imagery, allow detection and quantification of rockfalls spanning several decades, complementing and improving inventory databases, informing rockfall hazard assessment, and providing longer-term rates of cliff erosion.