Western Alaska’s convergent margins are composed of tectonostratigraphic terranes. On land, terrane assembly is recognized along boundaries or sutures between neighboring geologic elements with distinctly different origins. In marine areas where rock outcrops are covered by sediment, recognizing terrane sutures is problematic. A fault in seismic dip line 5 of the ALEUT project has been interpreted as a terrane suture. It is imaged intermittently down to the 30+-km-deep plate interface. Processing of ALEUT strike line 7 revealed the suture at ~18 km depths extending 300 km along the margin. Upper structures in line 5 are like the structures of adjacent seismic transects where imaging is only 8−10 km deep. They were previously not recognized as the upper reaches of terrane sutures and show structural details obscured at greater depths. The composite data are the basis for a simple tectonic model of terrane docking.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Global expansion in wind energy development is a notable achievement of the international community’s effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (Tadarida brasiliensis mexicana) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species’ probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-66490-3","usgsCitation":"Huang, T., Feng, X., Derbridge, J.J., Libby, K., Diffendorfer, J., Thogmartin, W.E., McCracken, G., Medellin, R., and Lopez-Hoffman, L., 2024, Potential for spatial coexistence of a transboundary migratory species and wind energy development: Scientific Reports, v. 14, 17050, 11 p., https://doi.org/10.1038/s41598-024-66490-3.","productDescription":"17050, 11 p.","ipdsId":"IP-117864","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":466978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-66490-3","text":"Publisher Index Page"},{"id":465068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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seafloor from archived unmanned submersible exploration dives","interactions":[],"lastModifiedDate":"2024-08-06T13:30:05.401425","indexId":"70256983","displayToPublicDate":"2024-07-24T08:23:11","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Photogrammetry of the deep seafloor from archived unmanned submersible exploration dives","docAbstract":"Large amounts of video images have been collected for decades by scientific and governmental organizations in deep (>1000 m) water using manned and unmanned submersibles and towed cameras. The collected images were analyzed individually or were mosaiced in small areas with great effort. Here, we provide a workflow for utilizing modern photogrammetry to construct virtual geological outcrops hundreds or thousands of meters in length from these archived video images. The photogrammetry further allows quantitative measurements of these outcrops, which were previously unavailable. Although photogrammetry had been carried out in recent years in the deep sea, it had been limited to small areas with pre-defined overlapping dive paths. Here, we propose a workflow for constructing virtual outcrops from archived exploration dives, which addresses the complicating factors posed by single non-linear and variable-speed vehicle paths. These factors include poor navigation, variable lighting, differential color attenuation due to variable distance from the seafloor, and variable camera orientation with respect to the vehicle. In particular, the lack of accurate navigation necessitates reliance on image quality and the establishment of pseudo-ground-control points to build the photogrammetry model. Our workflow offers an inexpensive method for analyzing deep-sea geological environments from existing video images, particularly when coupled with rock samples.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse12081250","usgsCitation":"Flores, C., and ten Brink, U.S., 2024, Photogrammetry of the deep seafloor from archived unmanned submersible exploration dives: Journal of Marine Science and Engineering, v. 12, no. 8, 1250, 19 p., https://doi.org/10.3390/jmse12081250.","productDescription":"1250, 19 p.","ipdsId":"IP-159627","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439255,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse12081250","text":"Publisher Index Page"},{"id":432271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.1,\n 19.2\n ],\n [\n -68.1,\n 18.3\n ],\n [\n -66.8,\n 18.3\n ],\n [\n -66.8,\n 19.2\n ],\n [\n -68.1,\n 19.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Flores, Claudia 0000-0003-0676-7061 cflores@usgs.gov","orcid":"https://orcid.org/0000-0003-0676-7061","contributorId":304396,"corporation":false,"usgs":true,"family":"Flores","given":"Claudia","email":"cflores@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":909080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"ten Brink, Uri S. 0000-0001-6858-3001","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":201741,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri","email":"","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":909081,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70275580,"text":"70275580 - 2024 - Strengths and opportunities in gopher tortoise population modeling: Reply to Loope et al.","interactions":[],"lastModifiedDate":"2026-05-04T16:29:38.173031","indexId":"70275580","displayToPublicDate":"2024-07-23T11:21:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Strengths and opportunities in gopher tortoise population modeling: Reply to Loope et al.","docAbstract":"We read the letter written by Loope et al. (2024) that evaluated the predictive population model for gopher tortoises from our previous paper (Folt et al., 2022), and, despite criticism raised, we contend that our effort remains a valuable contribution. In our previous paper, we performed an unprecedented and exhaustive review of gopher tortoise population demography, which synthesized a large volume of literature about the current state of knowledge on gopher tortoise population dynamics and factors that influence demographic rates, including anthropogenic threats (Folt et al., 2022). We then built a predictive population model that examined the relative influence of multiple anthropogenic stressors on tortoise populations into the future, while accounting for important geographic variation (Folt et al., 2022).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2024.e03093","usgsCitation":"Folt, B., Agha, M., Emanuel, J.A., Dziadzio, M., Cooke, J., Mena, L., Hinderliter, M., Hoffmann, S., Rankin, N., Tupy, J., and McGowan, C.P., 2024, Strengths and opportunities in gopher tortoise population modeling: Reply to Loope et al.: Global Ecology and Conservation, v. 54, e03093, 2 p., https://doi.org/10.1016/j.gecco.2024.e03093.","productDescription":"e03093, 2 p.","ipdsId":"IP-166659","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":504179,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2024.e03093","text":"Publisher Index Page"},{"id":503949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Folt, Brian","contributorId":267702,"corporation":false,"usgs":false,"family":"Folt","given":"Brian","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":960972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Agha, Mirza M. magha@usgs.gov","contributorId":4283,"corporation":false,"usgs":true,"family":"Agha","given":"Mirza M.","email":"magha@usgs.gov","affiliations":[],"preferred":true,"id":960973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Emanuel, Jo Anna","contributorId":337478,"corporation":false,"usgs":false,"family":"Emanuel","given":"Jo","email":"","middleInitial":"Anna","affiliations":[{"id":81021,"text":"Florida Ecological Services","active":true,"usgs":false}],"preferred":false,"id":960974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dziadzio, Michelina","contributorId":337480,"corporation":false,"usgs":false,"family":"Dziadzio","given":"Michelina","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":960975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooke, Jane","contributorId":337481,"corporation":false,"usgs":false,"family":"Cooke","given":"Jane","email":"","affiliations":[{"id":81021,"text":"Florida Ecological Services","active":true,"usgs":false}],"preferred":false,"id":960976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mena, Lourdes","contributorId":237782,"corporation":false,"usgs":false,"family":"Mena","given":"Lourdes","affiliations":[{"id":47612,"text":"USFWS, Nebraska Ecological Services Field Office","active":true,"usgs":false}],"preferred":false,"id":960977,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hinderliter, Matthew","contributorId":337483,"corporation":false,"usgs":false,"family":"Hinderliter","given":"Matthew","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":960978,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoffmann, Scott","contributorId":337616,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Scott","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":960979,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rankin, Nicole","contributorId":371080,"corporation":false,"usgs":false,"family":"Rankin","given":"Nicole","affiliations":[],"preferred":false,"id":960980,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tupy, John","contributorId":337486,"corporation":false,"usgs":false,"family":"Tupy","given":"John","affiliations":[{"id":81024,"text":"Mississippi Ecological Services Office","active":true,"usgs":false}],"preferred":false,"id":960981,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":960938,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70257458,"text":"70257458 - 2024 - Assessing the attractiveness of native wildflower species to bees (Hymenoptera: Anthophila) in the southeastern United States","interactions":[],"lastModifiedDate":"2024-09-10T15:19:29.744735","indexId":"70257458","displayToPublicDate":"2024-07-23T10:13:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the attractiveness of native wildflower species to bees (Hymenoptera: Anthophila) in the southeastern United States","docAbstract":"Decades of human activities and fire suppression have adversely affected longleaf pine (Pinus palustris) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long-term, large-scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer-scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long-term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13187","usgsCitation":"Hutchens, L., Kupfer, J.A., Gao, P., Sanchez, G.M., Meentemeyer, R.K., Terando, A., and Hiers, J.K., 2024, Projecting the long-term effects of large-scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome: Conservation Science and Practice, v. 6, e13187, 17 p., https://doi.org/10.1111/csp2.13187.","productDescription":"e13187, 17 p.","ipdsId":"IP-164797","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13187","text":"Publisher Index Page"},{"id":433552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6778324075661,\n 30.937698915049367\n ],\n [\n -82.87914407681765,\n 31.52377445868447\n ],\n [\n -85.48595743814668,\n 29.870880095396842\n ],\n [\n -84.9679549020707,\n 29.721163342172446\n ],\n [\n -83.73921448060787,\n 29.975699066098443\n ],\n [\n -83.09802043717049,\n 29.013903692421295\n ],\n [\n -82.21492940678012,\n 26.67873354422413\n ],\n [\n -81.88136124157192,\n 26.33869742309807\n ],\n [\n -80.97600764340528,\n 26.60948691174565\n ],\n [\n -80.80502196139402,\n 27.153838982412836\n ],\n [\n -80.23615831927407,\n 27.044945104314294\n ],\n [\n -81.6778324075661,\n 30.937698915049367\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchens, Lilian","contributorId":343967,"corporation":false,"usgs":false,"family":"Hutchens","given":"Lilian","email":"","affiliations":[],"preferred":false,"id":912487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kupfer, John A.","contributorId":339801,"corporation":false,"usgs":false,"family":"Kupfer","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":912488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gao, Peng","contributorId":224731,"corporation":false,"usgs":false,"family":"Gao","given":"Peng","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":912489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":303829,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina","email":"","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":true,"id":912490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":912491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":912492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. Kevin","contributorId":224733,"corporation":false,"usgs":false,"family":"Hiers","given":"J.","email":"","middleInitial":"Kevin","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":912493,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261445,"text":"70261445 - 2024 - A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","interactions":[],"lastModifiedDate":"2024-12-10T14:41:05.274772","indexId":"70261445","displayToPublicDate":"2024-07-23T08:36:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","docAbstract":"Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well-suited to dryland ecosystems, such as high sensitivity of plant water-use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine-scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi-mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process-based models, and loosely constrains time-varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross-wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub-yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem-scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007914","usgsCitation":"Reich, E., Samuels-Crow, K., Bradford, J., Litvak, M., Schlaepfer, D.R., and Ogle, K., 2024, A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007914, 18 p., https://doi.org/10.1029/2023JG007914.","productDescription":"e2023JG007914, 18 p.","ipdsId":"IP-166030","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":464941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Center","active":true,"usgs":true}],"preferred":true,"id":920585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ogle, K.","contributorId":347032,"corporation":false,"usgs":false,"family":"Ogle","given":"K.","affiliations":[{"id":83043,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA; Center of Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920586,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259218,"text":"70259218 - 2024 - Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","interactions":[],"lastModifiedDate":"2024-10-02T13:35:21.858349","indexId":"70259218","displayToPublicDate":"2024-07-23T08:15:54","publicationYear":"2024","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":"Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","docAbstract":"Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035982","usgsCitation":"Barnhart, T.B., Putman, A.L., Heldmyer, A.J., Rey, D., Hammond, J., Driscoll, J.M., and Sexstone, G., 2024, Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better: Water Resources Research, v. 60, no. 7, e2023WR035982, 21 p., https://doi.org/10.1029/2023WR035982.","productDescription":"e2023WR035982, 21 p.","ipdsId":"IP-154162","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":466979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035982","text":"Publisher Index Page"},{"id":462477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.35,\n 40\n ],\n [\n -107.5,\n 40\n ],\n [\n -107.5,\n 37.75\n ],\n [\n -105.35,\n 37.75\n ],\n [\n -105.35,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":219010,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore","email":"","middleInitial":"B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putman, Annie L. 0000-0002-9424-1707","orcid":"https://orcid.org/0000-0002-9424-1707","contributorId":225134,"corporation":false,"usgs":true,"family":"Putman","given":"Annie","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heldmyer, Aaron Joseph 0000-0001-8608-4927","orcid":"https://orcid.org/0000-0001-8608-4927","contributorId":302944,"corporation":false,"usgs":true,"family":"Heldmyer","given":"Aaron","email":"","middleInitial":"Joseph","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - 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2024 - Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","interactions":[],"lastModifiedDate":"2024-08-13T14:40:37.63952","indexId":"70256166","displayToPublicDate":"2024-07-23T07:28:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","docAbstract":"Several modelling and observational studies suggest deep water formation in the subpolar North Pacific as a possible alternative mode of thermohaline circulation that occurred in the warm Pliocene, a time when global atmospheric partial pressure of carbon dioxide was like the modern atmosphere (~400 ppm). We test this hypothesis by measuring the δ13C of the benthic foraminifer Cibicidoides wuellerstorfi collected from northernmost Pacific mid-Piacenzian Warm Period (3.264–3.025 Myr ago) sediments. The data reveal progressively more isotopically negative dissolved inorganic carbon along a northward Equator-to-pole transect, the opposite of the expected Pliocene Pacific meridional overturning circulation signal. C. wuellerstorfi δ13C is also often more positive at the deeper Ocean Drilling Program (ODP) site 887 compared with the shallower ODP site 883, suggesting ‘bottom-up’ ventilation of the deep Pacific Ocean. We then present alkenone sea surface temperature and export-productivity data from ODP site 883, which suggest that late Pliocene subarctic North Pacific carbonate sedimentation was, at least in part, probably due to higher coccolithophore export production, rather than North Pacific Deep Water formation as previously argued. Therefore, we suggest it is unlikely that North Pacific Deep Water formation occurred in the mid-Piacenzian Warm Period, although a shallower overturning cell cannot be ruled out.
Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.
In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.
Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.
The contiguous United States.
We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.
Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.
This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13906","usgsCitation":"Williams, D.A., Shadwell, K.S., Pearse, I., Prevey, J.S., Engelstad, P., Henderson, G., and Jarnevich, C.S., 2024, Predictor importance in habitat suitability models for invasive terrestrial plants: Diversity and Distributions, v. 30, no. 9, e13906, 13 p., https://doi.org/10.1111/ddi.13906.","productDescription":"e13906, 13 p.","ipdsId":"IP-160521","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13906","text":"Publisher Index Page"},{"id":432432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":909377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Henderson, Grace C. 0000-0001-9542-6888","orcid":"https://orcid.org/0000-0001-9542-6888","contributorId":328973,"corporation":false,"usgs":false,"family":"Henderson","given":"Grace","middleInitial":"C.","affiliations":[{"id":78543,"text":"Student contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":909378,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909379,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266476,"text":"70266476 - 2024 - Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","interactions":[],"lastModifiedDate":"2025-05-08T15:08:37.666349","indexId":"70266476","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5548,"text":"Spatial Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","docAbstract":"To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2024.100850","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2024, Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease: Spatial Statistics, v. 62, 100850, 15 p., https://doi.org/10.1016/j.spasta.2024.100850.","productDescription":"100850, 15 p.","ipdsId":"IP-155343","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.spasta.2024.100850","text":"Publisher Index Page"},{"id":485559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":354688,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":354689,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":936164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":354690,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":354692,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":936167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270064,"text":"70270064 - 2024 - Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","interactions":[],"lastModifiedDate":"2025-08-18T15:27:11.945136","indexId":"70270064","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22165,"text":"Reliability Engineering and System Safety (RESS)","active":true,"publicationSubtype":{"id":10}},"title":"Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","docAbstract":"Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.
Dams have negatively affected freshwater biodiversity throughout the world. These negative effects tend to be exacerbated for aquatic taxa with migratory life histories, and for taxa whose habitat is fundamentally altered by the formation of large reservoirs. Sauger (Sander candadensis; Percidae), large-bodied migratory fishes native to North America, have seen population declines over much of the species' range, and dams are often implicated for their role in blocking access to spawning habitat and otherwise negatively affecting river habitat. Furthermore, hybridization appears to be more frequent between sauger and walleye in the reservoirs formed by large dams. In this study, we examine the role of dams in altering sauger population connectivity and facilitating hybridization with introduced walleye in Wyoming's Wind River and Bighorn River systems. We collected genomic data from individuals sampled over a large spatial scale and replicated sampling throughout the spawning season, with the intent to capture potential variation in hybridization prevalence or genomic divergence between sauger with different life histories. The timing of sampling was not related to hybridization prevalence or population divergence, suggesting limited genetic differences between sauger spawning in different time and places. Overall, there was limited hybridization detected, however, hybridization was most prevalent in Boysen Reservoir (a large impounded section of the Wind River). Dams in the lower Wind River and upper Bighorn River were associated with population divergence between sauger upstream and downstream of the dams, and demographic models suggest that this divergence has occurred in concordance with the construction of the dam. Sauger upstream of the dams exhibited substantially lower estimates of genetic diversity, which implies that disrupted connectivity between Wind River and Bighorn River sauger populations may already be causing negative demographic effects. This research points towards the importance of considering the evolutionary consequences of dams on fish populations in addition to the threats they pose to population persistence.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11706","usgsCitation":"Rosenthal, W., Mandeville, E., Pilkerton, A., Gerrity, P.C., Skorupski, J., Walters, A.W., and Wagner, C., 2024, Influence of dams on sauger population structure and hybridization with introduced walleye, v. 14, no. 7, e11706, 16 p., https://doi.org/10.1002/ece3.11706.","productDescription":"e11706, 16 p.","ipdsId":"IP-145629","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11706","text":"Publisher Index Page"},{"id":485447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bighorn River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.2,\n 45\n ],\n [\n -109.2,\n 42.8\n ],\n [\n -107.6,\n 42.8\n ],\n [\n -107.6,\n 45\n ],\n [\n -109.2,\n 45\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenthal, William C.","contributorId":244630,"corporation":false,"usgs":false,"family":"Rosenthal","given":"William C.","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":935772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":935773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilkerton, Ashleigh","contributorId":346434,"corporation":false,"usgs":false,"family":"Pilkerton","given":"Ashleigh","affiliations":[{"id":63974,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":935774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":935775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skorupski, Joseph A.","contributorId":354495,"corporation":false,"usgs":false,"family":"Skorupski","given":"Joseph A.","affiliations":[],"preferred":false,"id":935776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Catherine E.","contributorId":337377,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine E.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":935778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266352,"text":"70266352 - 2024 - Stability concepts in ecology","interactions":[],"lastModifiedDate":"2025-05-06T13:29:07.014175","indexId":"70266352","displayToPublicDate":"2024-07-20T08:26:10","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Stability concepts in ecology","docAbstract":"The term stability, as applied to ecological systems, whether populations, communities, or ecosystems, means the tendency either to stay either close to some initial state, or to stay within certain bounds, or to persist in the face of environmental disturbances or changes. Here, a historical overview of stability concepts in ecology is outlined and measures of stability are discussed and described mathematically, including local stability, engineering resilience, resistance, persistence, and structural stability. Examples of instabilities caused by both pulse and press disturbances are given.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences-Encyclopedia of ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-443-21964-1.00008-2","usgsCitation":"DeAngelis, D.L., and Xu, L., 2024, Stability concepts in ecology, chap. of Reference module in earth systems and environmental sciences-Encyclopedia of ecology, HTML Document, https://doi.org/10.1016/B978-0-443-21964-1.00008-2.","productDescription":"HTML Document","ipdsId":"IP-162704","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":485438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":935750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":935751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256109,"text":"70256109 - 2024 - Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","interactions":[],"lastModifiedDate":"2024-07-22T11:47:51.294971","indexId":"70256109","displayToPublicDate":"2024-07-20T06:43:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","docAbstract":"In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km2 scales under conditions without debris flows.
Vesicularity of individual pyroclasts from airfall tephra deposits is an important parameter that is commonly measured at basaltic volcanoes. Conventional methods used to determine pyroclast vesicularity on a large number of clasts has the potential to be time consuming, particularly when rapid analysis is required. Here we propose dynamic image analysis on two-dimensional (2D) projection shapes of crushed pyroclasts from tephra deposits as a new method to estimate vesicularity. This method relies on the influence of vesicles and uses grain morphology as a proxy for vesicle size and abundance. Pyroclasts from a variety of basaltic tephra deposits from the volcanoes of Mauna Loa and Kīlauea were analyzed. Vesicularities between 52–98% were measured via nitrogen-gas pycnometry. The same pyroclasts were then crushed and sieved, and their grain shapes measured using dynamic image analysis on a CAMSIZER®. This yields values for the mean sphericity, elongation, compactness, and Krumbein roundness of the grains. Our data show that grains become increasingly irregular with increasing vesicularity, with the degree of correlation between shape parameters and vesicularity depending on the size of measured grains. Shape irregularities in small grains (60–250 µm) are mostly area-based, with elongation being the best vesicularity indicator, whereas shape irregularities in large grains (250–700 µm) are mostly perimeter-based, with Krumbein roundness as the best vesicularity indicator. Using mean shape parameter values with all grain sizes included, grain elongation is the most well-correlated shape parameter with vesicularity, with the best fitted model explaining 76% of variation in the observations. Microscope images of thin sections of intact pyroclasts, as well as from crushed pyroclasts, were analyzed using CSDCorrections 1.6 software in ImageJ to find local vesicularity, vesicle size, grain size, grain elongation, and vesicle spatial distribution by stereological conversion. Observed correlation between grain shape and vesicularity can be explained by the local effect of vesicles on the shape of the solid structure in between those vesicles. Grain shape depends not only on vesicularity, but also on vesicle to grain size ratio and the spatial distribution of vesicles. The influence of vesicles on grain shape is best captured by grains with the size of the solid structure in between vesicles, which generally increases with decreasing vesicularity. Dynamic image analysis is a useful tool to quickly gauge vesicularity, which could be used in near-real-time during an eruption response. However, this method is best suited for highly vesicular (> 80%) basaltic pyroclasts from tephra deposits with few microlites and phenocrysts. Further research on crushing techniques, optimum grain size for shape measurements, and Krumbein roundness measurements for the grain size range of 250–700 µm might enable application of this method to lower vesicularity pyroclasts.
Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.
To explain the drivers of historical water use in the public water systems (PWSs) that serve populations in Providence, Rhode Island, and surrounding areas, and to forecast future water use, a machine-learning model (cubist regression) was developed by the U.S. Geological Survey in cooperation with Providence Water to model daily per capita rates of domestic, commercial, and industrial water use. The PWSs in this area form a connected network that sources water from the Scituate Reservoir in Rhode Island. The cubist regression model was trained and tested on daily per capita rates for three categories of water use (domestic, commercial, and industrial) that were developed from quarterly water sales data and U.S. Census Bureau population estimates within each PWS service area from January 2005 through December 2021. The model was then used to make forecasts of future water use under varying scenarios of climate change, population growth, and economic growth for the years 2030 and 2040.
The resulting daily per capita rates, which were modeled from the historical data, had an r2 value of 0.94 and root mean square error of 6.7 gallons per capita daily. Results of the model were used to estimate total water use (the product of daily per capita rates and population) for all public water systems over the historical study period. Daily per capita rates in the study area decreased from 2005 to 2021, while population increased during that same period. “Category of water use” was the variable with the greatest explanatory power for modeling daily per capita rates. Overall, both daily per capita rates and total water use were projected to decrease in 2030 and 2040, in comparison to historical values from 2005 to 2021. Daily per capita rates and total water use were forecasted to decrease as economic growth rates increase. Daily per capita rates were expected to decrease as population growth rates increase; however, total water use was less sensitive to population growth rates than daily per capita rates. Effects of climate change were minimal over the 2030 and 2040 forecasting horizon for the scenarios tested.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245052","collaboration":"Prepared in cooperation with Providence Water","usgsCitation":"Chamberlin, C.A., 2024, A predictive analysis of water use for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2024–5052, 36 p., https://doi.org/10.3133/sir20245052.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152679","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499474,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117188.htm","linkFileType":{"id":5,"text":"html"}},{"id":431062,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94XIQ7W","text":"USGS data release","linkHelpText":"Model archive, input data, modeled estimates of water use 2005-2021, and forecasts of water use in 2030 and 2040 in Providence, Rhode Island"},{"id":431061,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5052 XML"},{"id":431060,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5052/images/"},{"id":431059,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245052/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5052 HTML"},{"id":431058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.pdf","text":"Report","size":"4.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5052 PDF"},{"id":431057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5052/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.6320030327788,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 41.56235835697041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1617","usgsCitation":"Udell, B.J., Straw, B., Loeb, S.C., Irvine, K., Thogmartin, W.E., Lausen, C., Reichard, J.D., Coleman, J.T., Cryan, P.M., Frick, W.F., and Reichert, B., 2024, Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends: Ecological Monographs, v. 94, no. 4, e1617, 25 p.; Data Release, https://doi.org/10.1002/ecm.1617.","productDescription":"e1617, 25 p.; Data Release","ipdsId":"IP-153066","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":434925,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3W0EZ","text":"USGS data release","linkHelpText":"Ecosystems-nabat-FPabund: software for fitting false-positive N-mixture models using NABat mobile acoustic data (version 1.0.0)"},{"id":439265,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecm.1617","text":"Publisher Index Page"},{"id":433498,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Udell, Bradley James 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Straw, Bethany R. 0000-0001-9086-4600","orcid":"https://orcid.org/0000-0001-9086-4600","contributorId":271020,"corporation":false,"usgs":true,"family":"Straw","given":"Bethany","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loeb, Susan C. 0000-0002-9264-3614","orcid":"https://orcid.org/0000-0002-9264-3614","contributorId":337070,"corporation":false,"usgs":false,"family":"Loeb","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":912387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":221555,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":912389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lausen, Cori","contributorId":343919,"corporation":false,"usgs":false,"family":"Lausen","given":"Cori","affiliations":[{"id":36893,"text":"Wildlife Conservation Society Canada","active":true,"usgs":false}],"preferred":false,"id":912390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reichard, Jonathan D. 0000-0002-4792-2868","orcid":"https://orcid.org/0000-0002-4792-2868","contributorId":337073,"corporation":false,"usgs":false,"family":"Reichard","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":912391,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coleman, Jeremy T.H. 0000-0002-2762-947X","orcid":"https://orcid.org/0000-0002-2762-947X","contributorId":239956,"corporation":false,"usgs":false,"family":"Coleman","given":"Jeremy","email":"","middleInitial":"T.H.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":912392,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912393,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Frick, Winifred F. 0000-0002-9469-1839","orcid":"https://orcid.org/0000-0002-9469-1839","contributorId":337076,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred","email":"","middleInitial":"F.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":912394,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912395,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70257688,"text":"70257688 - 2024 - The effects of flow extremes on native and non-native stream fishes in Puerto Rico","interactions":[],"lastModifiedDate":"2024-08-23T14:23:44.291409","indexId":"70257688","displayToPublicDate":"2024-07-19T09:20:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of flow extremes on native and non-native stream fishes in Puerto Rico","docAbstract":"Tidal freshwater forests are usually located at or above the level of mean high water. Some Louisiana coastal forests are below mean high water, especially bald cypress (Taxodium distichum (L.) Rich.) forests because flooding has increased due to the combined effects of global sea level rise and local subsidence. In addition, constructed channels from the coast inland act as conduits for saltwater. As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding.
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