Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s43017-023-00450-9","usgsCitation":"Shen, C., Appling, A.P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng, Y., Harman, C., Clark, M., Farthing, M., Feng, D., Kumar, P., Aboelyazeed, D., Rahmani, F., Song, Y., Beck, H.E., Bindas, T., Dwivedi, D., Fang, K., Hoge, M., Rackauckas, C., Mohanty, B., , R., Xu, C., and Lawson, K., 2023, Differentiable modelling to unify machine learning and physical models for geosciences: Nature Reviews Earth & Environment, v. 4, p. 552-567, https://doi.org/10.1038/s43017-023-00450-9.","productDescription":"16 p.","startPage":"552","endPage":"567","ipdsId":"IP-147269","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467103,"rank":2,"type":{"id":41,"text":"Open Access External Repository 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PA","active":true,"usgs":false}],"preferred":false,"id":878595,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70246733,"text":"70246733 - 2023 - Ring fault creep drives volcano-tectonic seismicity during caldera collapse of Kīlauea in 2018","interactions":[],"lastModifiedDate":"2023-07-18T11:57:32.785185","indexId":"70246733","displayToPublicDate":"2023-07-11T06:53:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Ring fault creep drives volcano-tectonic seismicity during caldera collapse of Kīlauea in 2018","docAbstract":"Basaltic caldera collapses are episodic, producing very-long-period (VLP) earthquakes up to Mw 5.4, with prolific inter-collapse (between collapses) volcano-tectonic (VT) seismicity. During the 2018 caldera collapse of Kīlauea Volcano, VT seismicity ceased following each collapse, and then accelerated to a quasi-steady rate prior to the next collapse, marking a temporal pattern distinct from typical foreshock/aftershock sequences. There is currently no consensus on the mechanism(s) that generates the VT seismicity. Here we demonstrate that inter-collapse ring fault creep, induced by chamber depressurization, was the main driver of VT seismicity at Kīlauea in 2018. This is evidenced by: 1) the correlation between cumulative number of VT events and GNSS-derived ring fault creep; 2) agreement between repeating earthquake and GNSS derived creep rates; and 3) consistency between the time dependence of mechanically modeled, creep-driven seismicity and observations. We further show that, ring fault creep can be explained by velocity strengthening friction alone or in conjunction with viscous shear zone rheology. The simultaneous occurrence of creep and seismicity highlights the spatially heterogeneous velocity weakening/strengthening friction on the ring fault. If the VT seismicity-creep correlation can be replicated at other basaltic volcanoes, it would demonstrate that VT seismicity can be used as a proxy for ring fault creep in the absence of GNSS measurements on subsiding caldera block(s).
Salinity control efforts in the Colorado River Basin have focused on mobilization of salts from irrigated land, but nonirrigated rangelands are also a source of salinity. In particular, lands where soils have formed from the Late Cretaceous Mancos Shale under arid and semiarid climates contain considerable quantities of salt, mainly in the subsurface. Hundreds of thousands of contour furrows and check dams (gully plugs) were constructed by the Bureau of Land Management (BLM) and Bureau of Reclamation in the late 1950s and 1960s to reduce runoff, sedimentation, and salt mobilization from ephemeral stream channels on rangelands. Sediment-retention ponds associated with check dams are dry most of the year, except immediately following substantial rain events. Generally, no maintenance has been performed on these structures, some have degraded over time, and their current and past influence on salinity is poorly understood. To assess the influence of check dams and their associated ponds on salt retention and mobilization, the U.S. Geological Survey, in cooperation with the BLM, conducted a study of such ponds within the Gunnison Gorge National Conservation Area (GGNCA) near Delta, Colorado.
This report includes conceptual models of how sediment-retention ponds function relative to salinity, and a collection of environmental data to evaluate the conceptual models. An inventory of 69 ponds indicated that 38 percent no longer had water holding capacity, and another 20 percent could hold 1 foot or less of water. Check-dam degradation was the main cause, but sediment infill of ponds contributed as well. Water content of soil profiles collected beneath ponds and immediately downstream from check dams indicated little penetration of water below 60 centimeters for most ponds and little evidence for lateral movement of water beneath check dams. Patterns of salt content in the soil profiles indicated no accumulation of salts at the pond surface from evaporating waters and little evidence for salt redistribution in the form of salt bulges or salt depletion curves at intermediate depths. Based on the conceptual models presented and interpretations of data collected by this study, it appears that the sediment-retention ponds in the GGNCA have neither mobilized nor retained substantial quantities of salt during their lifetimes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235071","collaboration":"Prepared in cooperation with Bureau of Land Management","programNote":"Water Availability and Use Science Program","usgsCitation":"Richards, R.J., Bern, C.R., and Moreno, V., 2023, Assessment of salinity retention or mobilization by sediment-retention ponds near Delta, Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2023–5071, 21 p., https://doi.org/10.3133/sir20235071.","productDescription":"Report: v, 21 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134766","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":418430,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WZNJL6","text":"USGS data release","linkHelpText":"Data from the assessment of sediment-retention ponds near Delta, Colorado, 2019"},{"id":418428,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5071/coverthb.jpg"},{"id":418429,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5071/sir20235071.pdf","text":"Report","size":"4.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5071"},{"id":500951,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114965.htm","linkFileType":{"id":5,"text":"html"}},{"id":418866,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5071"},{"id":418837,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5071/sir20235071.xml"},{"id":418836,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5071/images"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.55,\n 38.4\n ],\n [\n -107.55,\n 38.36\n ],\n [\n -107.53,\n 38.36\n ],\n [\n -107.53,\n 38.4\n ],\n [\n -107.55,\n 38.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25048, Mail Stop 415
Denver, Colorado 80225
Remote cameras have become a widespread data-collection tool for terrestrial mammals, but classifying images can be labor intensive and limit the usefulness of cameras for broad-scale population monitoring. Machine learning algorithms for automated image classification can expedite data processing, but image misclassifications may influence inferences. Here, we used camera data for three sympatric species with disparate body sizes and life histories – black-tailed jackrabbits (Lepus californicus), kit foxes (Vulpes macrotis), and pronghorns (Antilocapra americana) – as a model system to evaluate the influence of competing image classification approaches on estimates of occupancy and inferences about space use. We classified images with: (i) single review (manual), (ii) double review (manual by two observers), (iii) an automated-manual review (machine learning to cull empty images and single review of remaining images), (iv) a pretrained machine-learning algorithm that classifies images to species (base model), (v) the base model accepting only classifications with ≥95% confidence, (vi) the base model trained with regional images (trained model), and (vii) the trained model accepting only classifications with ≥95% confidence. We compared species-specific results from alternative approaches to results from double review, which reduces the potential for misclassifications and was assumed to be the best approximation of truth. Despite high classification success, species-level misclassification rates for the base and trained models were sufficiently high to produce erroneous occupancy estimates and inferences related to space use across species. Increasing the confidence thresholds for image classification to 95% did not consistently improve performance. Classifying images as empty (or not) offered a reasonable approach to reduce effort (by 97.7%) and facilitated a semi-automated workflow that produced reliable estimates and inferences. Thus, camera-based monitoring combined with machine learning algorithms for image classification could facilitate monitoring with limited manual image classification.
","language":"English","publisher":"Wiley","doi":"10.1002/rse2.356","usgsCitation":"Lonsinger, R.C., Dart, M.M., Larsen, R., and Knight, R.N., 2023, Efficacy of machine learning image classification for automated occupancy-based monitoring: Remote Sensing in Ecology and Conservation, v. 10, no. 1, p. 56-71, https://doi.org/10.1002/rse2.356.","productDescription":"16 p.","startPage":"56","endPage":"71","ipdsId":"IP-150309","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":442810,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.356","text":"Publisher Index Page"},{"id":432056,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Dugway Proving Ground, Lund, Mojave Desert, Beaver Dam Wash, Colorado Plateau Great Basin 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\"}}]}","volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dart, Marlin M.","contributorId":340675,"corporation":false,"usgs":false,"family":"Dart","given":"Marlin","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Randy T.","contributorId":340676,"corporation":false,"usgs":false,"family":"Larsen","given":"Randy T.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":907446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Robert N.","contributorId":340677,"corporation":false,"usgs":false,"family":"Knight","given":"Robert","email":"","middleInitial":"N.","affiliations":[{"id":81648,"text":"US Army Dugway Proving Ground","active":true,"usgs":false}],"preferred":false,"id":907447,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246549,"text":"sir20235075 - 2023 - Potential effects of projected pumping scenarios on future water-table elevations near Kirtland Air Force Base in Albuquerque, New Mexico","interactions":[],"lastModifiedDate":"2026-03-12T20:44:43.104195","indexId":"sir20235075","displayToPublicDate":"2023-07-10T12:38:28","publicationYear":"2023","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":"2023-5075","displayTitle":"Potential Effects of Projected Pumping Scenarios on Future Water-Table Elevations Near Kirtland Air Force Base in Albuquerque, New Mexico","title":"Potential effects of projected pumping scenarios on future water-table elevations near Kirtland Air Force Base in Albuquerque, New Mexico","docAbstract":"The U.S. Geological Survey, in cooperation with the Air Force Civil Engineer Center, simulated different groundwater pumping scenarios from 2016 to 2050 to determine the potential future changes in groundwater levels in areas around the Kirtland Air Force Base Bulk Fuels Facility and an ethylene dibromide (EDB) plume. Projections of water supply and demand created by the Albuquerque Bernalillo County Water Utility Authority were used to develop the future groundwater pumping scenarios used as inputs for a refined local-scale model within the updated Middle Rio Grande Basin regional model.
The simulated water-table elevations in model cells that contain the EDB plume in the medium demand and medium supply scenario rose 29 feet (ft) until 2035, then remained within 10 ft of that elevation through 2050, whereas the water-table elevations in the high demand and low supply scenario rose about 26 ft until 2035 and then decreased by more than 10 ft. Simulated water-table elevations in the low demand and high supply scenario continued to rise throughout most of the future simulation period and peaked at about 44 ft over the 2016 water-table elevation. All of the scenarios ended the future simulation period with higher simulated water-table elevations than at the beginning of the future simulation period. Simulations that represented the potentially highest and lowest volume of groundwater pumping near the EDB plume by adjusting the spatial distribution of pumping had similar simulated water-table elevations as the nonadjusted scenarios, with maximum water-table elevation changes that only differed by about 2 ft from the nonadjusted scenarios. Consideration should be taken when using these model results to inform decisions because the model results are subject to uncertainty from many different sources, including uncertainty in the future pumping scenarios as well as the model itself because of the simplification of the hydrogeologic system.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Coordinated wildlife disease surveillance (WDS) can help professionals across disciplines effectively safeguard human, animal, and environmental health. The aims of this study were to understand how WDS in Thailand is utilized, valued, and can be improved within a One Health framework. An online questionnaire was distributed to 183 professionals (55.7% response rate) across Thailand working in wildlife, marine animal, livestock, domestic animal, zoo animal, environmental, and public health sectors. Twelve semi-structured interviews with key professionals were then performed. Three-quarters of survey respondents reported using WDS data and information. Sectors agreed upon ranking disease control (76.5% of respondents) as the most beneficial outcome of WDS, while fostering new ideas through collaboration was valued by few participants (2.0%). Accessing data collected by ones own sector was identified as the most challenging (50%) yet least difficult to improve (88.3%). Having legal authority to conduct WDS was the second most frequently identified challenge. Interviewees explained that legal documentation required for crossinstitutional collaborations posed a barrier to efficient communication and use of human resources. Survey respondents identified allocation of human resources (75.5%), adequate budget (71.6%), and having a clear communication system between sectors (71.6%) as highest priority areas for improvement to WDS in Thailand. Authorization from administrative officials and support from local community members were identified as challenges during in-person interviews. Future outreach should be directed towards these groups. As 42.9% of marine health professionals had difficulty knowing whom to contact in other sectors and 28.4% of survey respondents indicated that communication with marine health professionals was not applicable to their work, connecting the marine sector with other sectors may be prioritized. This study identifies priorities for addressing current challenges in the establishment of a general WDS system and information management system in Thailand while presenting a model for such evaluation in other regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.onehlt.2023.100600","usgsCitation":"George, S.E., Smink, M., Sangkachai, N., Wiratsudakul, A., Sakcamduang, W., Suwanpakdee, S., and Sleeman, J.M., 2023, Stakeholder attitudes and perspectives on wildlife disease surveillance as a component of a One Health approach in Thailand: One Health Newsletter, v. 17, 100600, 10 p., https://doi.org/10.1016/j.onehlt.2023.100600.","productDescription":"100600, 10 p.","ipdsId":"IP-154964","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":442811,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.onehlt.2023.100600","text":"Publisher Index Page"},{"id":419412,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Thailand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[102.58493,12.18659],[101.68716,12.64574],[100.83181,12.62708],[100.97847,13.41272],[100.0978,13.40686],[100.01873,12.307],[99.47892,10.84637],[99.15377,9.96306],[99.2224,9.23926],[99.87383,9.20786],[100.27965,8.29515],[100.45927,7.42957],[101.01733,6.85687],[101.62308,6.74062],[102.14119,6.22164],[101.81428,5.81081],[101.15422,5.69138],[101.07552,6.20487],[100.2596,6.64282],[100.08576,6.46449],[99.69069,6.84821],[99.51964,7.34345],[98.98825,7.90799],[98.50379,8.38231],[98.33966,7.79451],[98.15001,8.35001],[98.25915,8.97392],[98.55355,9.93296],[99.03812,10.96055],[99.58729,11.89276],[99.19635,12.80475],[99.21201,13.26929],[99.09776,13.8275],[98.43082,14.62203],[98.19207,15.1237],[98.53738,15.3085],[98.90335,16.17782],[98.49376,16.83784],[97.85912,17.56795],[97.3759,18.44544],[97.79778,18.62708],[98.25372,19.7082],[98.95968,19.75298],[99.54331,20.1866],[100.11599,20.41785],[100.54888,20.10924],[100.60629,19.50834],[101.28201,19.46258],[101.03593,18.40893],[101.05955,17.5125],[102.11359,18.1091],[102.413,17.93278],[102.99871,17.96169],[103.20019,18.30963],[103.95648,18.24095],[104.71695,17.42886],[104.77932,16.44186],[105.58904,15.57032],[105.54434,14.72393],[105.21878,14.27321],[104.28142,14.41674],[102.98842,14.22572],[102.3481,13.39425],[102.58493,12.18659]]]},\"properties\":{\"name\":\"Thailand\"}}]}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"George, Serena Elise","contributorId":317781,"corporation":false,"usgs":false,"family":"George","given":"Serena","email":"","middleInitial":"Elise","affiliations":[{"id":69152,"text":"University of Wisconsin-Madison, School of Veterinary Medicine","active":true,"usgs":false}],"preferred":false,"id":879297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smink, Moniek","contributorId":317782,"corporation":false,"usgs":false,"family":"Smink","given":"Moniek","email":"","affiliations":[{"id":69153,"text":"University of Wisconsin-Madison, Department of Computer Sciences,","active":true,"usgs":false}],"preferred":false,"id":879298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sangkachai, Nareerat","contributorId":317783,"corporation":false,"usgs":false,"family":"Sangkachai","given":"Nareerat","email":"","affiliations":[{"id":69154,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science & The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals","active":true,"usgs":false}],"preferred":false,"id":879299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiratsudakul, Anuwat","contributorId":317784,"corporation":false,"usgs":false,"family":"Wiratsudakul","given":"Anuwat","email":"","affiliations":[{"id":69155,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science, The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals & Department of Clinical Sciences and Public Health, Faculty of Veterinary Science","active":true,"usgs":false}],"preferred":false,"id":879300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sakcamduang, Walasinee","contributorId":317785,"corporation":false,"usgs":false,"family":"Sakcamduang","given":"Walasinee","email":"","affiliations":[{"id":69156,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science & Department of Clinical Sciences and Public Health, Faculty of Veterinary Science","active":true,"usgs":false}],"preferred":false,"id":879301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suwanpakdee, Sarin","contributorId":317786,"corporation":false,"usgs":false,"family":"Suwanpakdee","given":"Sarin","email":"","affiliations":[{"id":69155,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science, The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals & Department of Clinical Sciences and Public Health, Faculty of Veterinary Science","active":true,"usgs":false}],"preferred":false,"id":879302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":879303,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70246625,"text":"70246625 - 2023 - BioLake: A first assessment of lake temperature-derived bioclimatic predictors for aquatic invasive species","interactions":[],"lastModifiedDate":"2023-07-12T12:15:02.02119","indexId":"70246625","displayToPublicDate":"2023-07-10T07:10:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"BioLake: A first assessment of lake temperature-derived bioclimatic predictors for aquatic invasive species","docAbstract":"Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature-derived “BioLake” bioclimatic layers for distribution modeling of aquatic species and compared “climatch” climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization.
Interventions of the host–pathogen dynamics provide strong tests of relationships, yet they are still rarely applied across multiple populations. After American bullfrogs (Rana catesbeiana) invaded a wildlife refuge where federally threatened Chiricahua leopard frogs (R. chiricahuensis) were reintroduced 12 years prior, managers launched a landscape-scale eradication effort to help ensure continued recovery of the native species. We used a before-after-control-impact design and environmental DNA sampling of 19 eradication sites and 18 control sites between fall 2016 and winter 2020–2021 to measure community-level responses to bullfrog eradication, including for two pathogens. Dynamic occupancy models revealed successful eradication from 94% of treatment sites. Native amphibians did not respond to bullfrog eradication, but the pathogens amphibian chytrid fungus (Batrachochytrium dendrobatidis) and ranaviruses were coextirpated with bullfrogs. Our spatially replicated experimental approach provides strong evidence that management of invasive species can simultaneously reduce predation and disease risk for imperiled species.
The duration of inundation or saturation (i.e., hydroperiod) controls many wetland functions. In particular, it is a key determinant of whether a wetland will provide suitable breeding habitat for amphibians and other taxa that often have specific hydrologic requirements. Yet, scientists and land managers often are challenged by a lack of sufficient monitoring data to enable the understanding of the wetting and drying dynamics of small depressional wetlands. In this study, we present and evaluate an approach to predict daily inundation dynamics using a large wetland water-level dataset and a random forest algorithm. We relied on predictor variables that described characteristics of basin morphology of each wetland and atmospheric water budget estimates over various antecedent periods. These predictor variables were derived from datasets available over the conterminous United States making this approach potentially extendable to other locations. Model performance was evaluated using two metrics, median hydroperiod and the proportion of correctly classified days. We found that models performed well overall with a median balanced accuracy of 83% on validation data. Median hydroperiod was predicted most accurately for wetlands that were infrequently inundated and least accurate for permanent wetlands. The proportion of inundated days was predicted most accurately in permanent wetlands (99%) followed by frequently inundated wetlands (98%) and infrequently inundated wetlands (93%). This modeling approach provided accurate estimates of inundation and could be useful in other depressional wetlands where the primary water flux occurs with the atmosphere and basin morphology is a critical control on wetland inundation and hydroperiods.
River profiles are shaped by climatic and tectonic history, lithology, and internal feedbacks between flow hydraulics, sediment transport and erosion. In steep channels, waterfalls may self-form without changes in external forcing (i.e., autogenic formation) and erode at rates faster or slower than an equivalent channel without waterfalls. We use a 1-D numerical model to investigate how self-formed waterfalls alter the morphology of bedrock river longitudinal profiles. We modify the standard stream power model to include a slope threshold above which waterfalls spontaneously form and a rate constant allowing waterfalls to erode faster or slower than other fluvial processes. Using this model, we explore how waterfall formation alters both steady state and transient longitudinal profile forms. Our model predicts that fast waterfalls create km-scale reaches in a dynamic equilibrium with channel slope held approximately constant at the threshold slope for waterfall formation, while slow waterfalls can create local channel slope maxima at the location of slow waterfall development. Furthermore, slow waterfall profiles integrate past base level histories, leading to multiple possible profile forms, even at steady-state. Consistency between our model predictions and field observations of waterfall-rich rivers in the Kings and Kaweah drainages in the southern Sierra Nevada, California, supports the hypothesis that waterfall formation can modulate river profiles in nature. Our findings may help identify how bedrock channels are influenced by waterfall erosion and aid in distinguishing between signatures of external and internal perturbations, thereby strengthening our ability to interpret past climate and tectonic changes from river longitudinal profiles.
The steep, tectonically active terrain along the Central California (USA) coast is well known to produce deadly and destructive debris flows. However, the extent to which fire affects debris-flow susceptibility in this region is an open question. We documented the occurrence of postfire debris floods and flows following the landfall of a storm that delivered intense rainfall across multiple burn areas. We used this inventory to evaluate the predictive performance of the US Geological Survey M1 likelihood model, a tool that presently underlies the emergency assessment of postfire debris-flow hazards in the western USA. To test model performance, we used the threat score skill statistic and found that the rainfall thresholds estimated by the M1 model for the Central California coast performed similarly to training (Southern California) and testing (Intermountain West) data associated with the original model calibration. Model performance decreased when differentiating between “minor” and “major” postfire hydrologic response types, which weigh effects on human life and infrastructure. Our results underscore that the problem of false positives is a major challenge for developing accurate rainfall thresholds for the occurrence of postfire debris flows. As wildfire activity increases throughout the western USA, so too will the demand for the assessment of postfire debris-flow hazards. We conclude that additional collection of field-verified inventories of postfire hydrologic response will be critical to prioritize which model variables may be suitable candidates for regional calibration or replacement.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-023-02106-7","usgsCitation":"Thomas, M.A., Kean, J.W., McCoy, S., Lindsay, D.N., Kostelnik, J., Cavagnaro, D.B., Rengers, F.K., East, A.E., Schwartz, J., Smith, D.P., and Collins, B.D., 2023, Postfire hydrologic response along the central California (USA) coast: Insights for the emergency assessment of postfire debris-flow hazards: Landslides, v. 20, p. 2421-2436, https://doi.org/10.1007/s10346-023-02106-7.","productDescription":"16 p.","startPage":"2421","endPage":"2436","ipdsId":"IP-139528","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442830,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10346-023-02106-7","text":"Publisher Index Page"},{"id":435262,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91O03Y7","text":"USGS data release","linkHelpText":"Field-verified inventory of postfire hydrologic response for the 2020 CZU Lightning Complex, River, Camel, and Dolan Fires following a 26-29 January 2021 atmospheric river storm sequence"},{"id":418804,"rank":1,"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 \"coordinates\": [\n [\n [\n -121.47368381570269,\n 35.81897449008355\n ],\n [\n -120.78708136085193,\n 36.2836437903476\n ],\n [\n -121.84398626326276,\n 37.24464732874951\n ],\n [\n -122.37243871446816,\n 36.97394608796073\n ],\n [\n -121.47368381570269,\n 35.81897449008355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2023-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":877143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":877144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCoy, Scott W.","contributorId":267182,"corporation":false,"usgs":false,"family":"McCoy","given":"Scott W.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":877145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsay, Donald N.","contributorId":216337,"corporation":false,"usgs":false,"family":"Lindsay","given":"Donald","email":"","middleInitial":"N.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":877146,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":877147,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cavagnaro, David B.","contributorId":267181,"corporation":false,"usgs":false,"family":"Cavagnaro","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":877148,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":877149,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":877150,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schwartz, Jonathan","contributorId":312505,"corporation":false,"usgs":false,"family":"Schwartz","given":"Jonathan","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":877151,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Douglas P.","contributorId":201716,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":35924,"text":"California State University, Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":877152,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":877153,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70246596,"text":"70246596 - 2023 - Translating stakeholder narratives for participatory modeling in landscape ecology","interactions":[],"lastModifiedDate":"2023-09-06T16:23:41.955537","indexId":"70246596","displayToPublicDate":"2023-07-07T06:57:50","publicationYear":"2023","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":"Translating stakeholder narratives for participatory modeling in landscape ecology","docAbstract":"Engaging stakeholders in research is needed for many of the sustainability challenges that landscape ecologists address. Involving stakeholders’ perspectives through narratives in participatory modeling fosters better understanding of the problem and evaluation of the acceptability of tradeoffs and creates buy-in for management actions. However, stakeholder-driven inputs often take the form of complex qualitative descriptions, rather than model-ready numerical or categorical inputs.
Translating narratives into models, model parameters, or scenarios is essential for leveraging stakeholder knowledge and engagement. Drawing from varied experiences to identify lessons learned and pitfalls, we address the practice of translating narratives into models and using those narratives to interpret and communicate results.
We drew from seven participatory landscape ecology projects across North America to synthesize lessons for the inclusion of stakeholder narratives in modeling studies.
We offer 8 lessons as practical guidance for other landscape ecologists to move the science beyond a unilateral focus on ecological systems and to maximize the benefits of landscape sustainability science.
These lessons are starting points, as real projects are complex, nuanced, and sometimes contradictory. Translating narratives into models is important for addressing complex sustainability challenges; we hope that these starting points are helpful to those foraying into this type of research.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01724-9","usgsCitation":"Vukomanovic, J., Smart, L., Koch, J., Dale, V., Plassin, S., Byrd, K.B., Beier, C., and Doyon, F., 2023, Translating stakeholder narratives for participatory modeling in landscape ecology: Landscape Ecology, v. 38, p. 2453-2474, https://doi.org/10.1007/s10980-023-01724-9.","productDescription":"22 p.","startPage":"2453","endPage":"2474","ipdsId":"IP-136700","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":502612,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.wur.nl/en/publications/translating-stakeholder-narratives-for-participatory-modeling-in-","text":"External Repository"},{"id":418854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationDate":"2023-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Vukomanovic, Jelena","contributorId":316275,"corporation":false,"usgs":false,"family":"Vukomanovic","given":"Jelena","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":877297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smart, Lindsey","contributorId":316276,"corporation":false,"usgs":false,"family":"Smart","given":"Lindsey","affiliations":[{"id":68543,"text":"North Carolina State University, The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":877298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Jennifer","contributorId":316277,"corporation":false,"usgs":false,"family":"Koch","given":"Jennifer","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":877299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dale, Virginia","contributorId":316278,"corporation":false,"usgs":false,"family":"Dale","given":"Virginia","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":877300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plassin, Sophie","contributorId":316279,"corporation":false,"usgs":false,"family":"Plassin","given":"Sophie","email":"","affiliations":[{"id":34610,"text":"Universite de Toulouse","active":true,"usgs":false}],"preferred":false,"id":877301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":877302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beier, Colin","contributorId":316280,"corporation":false,"usgs":false,"family":"Beier","given":"Colin","affiliations":[{"id":37519,"text":"SUNY College of Environmental Science and Forestry","active":true,"usgs":false}],"preferred":false,"id":877303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Doyon, Frederik","contributorId":316282,"corporation":false,"usgs":false,"family":"Doyon","given":"Frederik","email":"","affiliations":[{"id":68544,"text":"Institut des sciences de la foret temperee, Universite du Quebec","active":true,"usgs":false}],"preferred":false,"id":877304,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70246616,"text":"70246616 - 2023 - A detailed view of the 2020-2023 southwestern Puerto Rico seismic sequence with deep learning","interactions":[],"lastModifiedDate":"2023-12-04T16:59:36.832347","indexId":"70246616","displayToPublicDate":"2023-07-06T08:36:24","publicationYear":"2023","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":"A detailed view of the 2020-2023 southwestern Puerto Rico seismic sequence with deep learning","docAbstract":"The 2020–2023 southwestern Puerto Rico seismic sequence, still ongoing in 2023, is remarkable for its multiple‐fault rupture complexity and elevated aftershock productivity. We applied an automatic workflow to continuous data from 43 seismic stations in Puerto Rico to build an enhanced earthquake catalog with ∼180,000 events for the 3+ yr sequence from 28 December 2019 to 1 January 2023. This workflow contained the EQTransformer (EQT) deep learning model for event detection and phase picking, the EikoNet‐Hypocenter Inversion with Stein Variational Inference probabilistic earthquake location approach with a neural network trained to solve the eikonal wave equation, and relocation with event‐pair waveform cross correlation. EQT increased the number of catalog events in the sequence by about seven times, though its performance was not quite as good as thorough analyst review. The enhanced catalog revealed new structural details of the sequence space–time evolution, including sudden changes in activity, on a complex system of many small normal and strike‐slip faults. This sequence started on 28 December 2019 with an M 4.7 strike‐slip earthquake followed by 10 days of shallow strike‐slip foreshocks, including several M 5+ earthquakes, in a compact region. The oblique normal fault
Faults are important controls on hydrothermal circulation worldwide. More specifically, structural discontinuities, i.e. locations where faults interact and intersect, host many hydrothermal systems. In the Great Basin, western USA, an extensive characterization effort demonstrated that hydrothermal systems are controlled by one (or more) of eight types of structural discontinuities. Presumably, specific attributes of these structural settings control the generation and maintenance of permeability and porosity, and therefore localize hydrothermal processes. Herein, I examine representative examples of the eight structural settings that host hydrothermal systems in the Great Basin. For each setting, I use a boundary element method to model fault slip on the major faults and track the distribution of stress and strain in the surrounding crust. Results demonstrate that the largest magnitude and most localized stress and strain effects occur in the structural settings that host the largest number of hydrothermal systems; fault stepovers and fault terminations. Structural settings that are common in areas of strike-slip faulting also show localized stress and strain effects. The modelling presented provides process-based explanations for the empirical and conceptual results of regional characterization of Great Basin hydrothermal systems.
","language":"English","publisher":"Geological Society of London","doi":"10.1144/geoenergy2023-009","usgsCitation":"Siler, D.L., 2023, Structural discontinuities and their control on hydrothermal systems in the Great Basin, USA: Geoenergy, v. 1, no. 1, 10 p., https://doi.org/10.1144/geoenergy2023-009.","productDescription":"10 p.","ipdsId":"IP-146721","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":442846,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/geoenergy2023-009","text":"Publisher Index Page"},{"id":435264,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S73O5C","text":"USGS data release","linkHelpText":"Stress transfer modeling of Great Basin, USA structural discontinuities; Data and MATLAB functions (ver. 1.1, June 2023)"},{"id":418922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.971198043698,\n 43.70678410718071\n ],\n [\n -120.971198043698,\n 33.361880330157675\n ],\n [\n -107.7054053941411,\n 33.361880330157675\n ],\n [\n -107.7054053941411,\n 43.70678410718071\n ],\n [\n -120.971198043698,\n 43.70678410718071\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":877837,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70255560,"text":"70255560 - 2023 - Beyond simple trend tests: Detecting significant changes in design-flood quantiles","interactions":[],"lastModifiedDate":"2024-06-24T11:48:57.352622","indexId":"70255560","displayToPublicDate":"2023-07-06T06:40:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Beyond simple trend tests: Detecting significant changes in design-flood quantiles","docAbstract":"Changes in annual maximum flood (AMF), which are usually detected using simple trend tests (e.g., Mann-Kendall test (MKT)), are expected to change design-flood estimates. We propose an alternate framework to detect significant changes in design-flood between two periods and evaluate it for synthetically generated AMF from the Log-Pearson Type-3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design-flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design-flood quantiles were observed even without a significant trend in AMF and basins with statistically significant trend did not necessarily exhibit statistically significant changes in design-flood. We recommend application of the framework for evaluating changes in design-flood estimates considering changes in all the moments as opposed to simple trend tests.
The Atlantic sturgeon Acipenser oxyrinchus oxyrinchus was once of great commercial importance in many coastal rivers of the eastern USA. Over the 19th and 20th centuries, most historical stocks of Atlantic sturgeon were depleted by human activities. Estimating recruitment for the remaining populations is challenging due to sampling constraints, limited age data, and natural variability. However, recruitment estimates could inform recovery efforts. The objectives of this study were to compare 2 modeling approaches to estimate recruitment of age-1 Atlantic sturgeon and provide an updated index of abundance across more than a decade of sampling in the Altamaha River, Georgia. First, we constructed capture histories of river-resident juveniles, using capture-mark-recapture data collected from 2008 to 2020, and assigned ages based on length-frequency analysis. Second, we compared more traditional Huggins closed population models and a recent nonlinear extension of Huggins models—vector generalized additive models (VGAMs)—to estimate abundance of age-1 fish. Both model types indicated similar yearly age-1 abundance estimates (Huggins: 163 in 2017 to 3839 in 2010; VGAM: 312 in 2020 to 4448 in 2010), but the VGAMs provided more direct interpretation for factors that might affect capture probability (e.g. sampling effort, temperature, fish length). This study indicates that the age-1 Altamaha River Atlantic sturgeon population has remained relatively stable over the past decade and provides a long-term baseline which will better enable managers to assess the effects of either future restoration actions or environmental disturbances on the population.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01250","usgsCitation":"Baker, M., Ingram, E., Higginbotham, D., Irwin, B., and Fox, A., 2023, Refining capture-recapture recruitment estimation methods for Atlantic sturgeon: Endangered Species Research, v. 51, p. 203-214, https://doi.org/10.3354/esr01250.","productDescription":"12 p.","startPage":"203","endPage":"214","ipdsId":"IP-143737","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432972,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":442856,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01250","text":"Publisher Index Page"}],"country":"United States","state":"Georgia","otherGeospatial":"Altamaha River estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.48760060177855,\n 31.490539509150565\n ],\n [\n -81.48760060177855,\n 31.14793158914553\n ],\n [\n -81.23488559221977,\n 31.14793158914553\n ],\n [\n -81.23488559221977,\n 31.490539509150565\n ],\n [\n -81.48760060177855,\n 31.490539509150565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, M.A.","contributorId":340977,"corporation":false,"usgs":false,"family":"Baker","given":"M.A.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ingram, E.C.","contributorId":340978,"corporation":false,"usgs":false,"family":"Ingram","given":"E.C.","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":907757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Higginbotham, D.L.","contributorId":340979,"corporation":false,"usgs":false,"family":"Higginbotham","given":"D.L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907759,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, A.G.","contributorId":340980,"corporation":false,"usgs":false,"family":"Fox","given":"A.G.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907760,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256426,"text":"70256426 - 2023 - The Factors Affecting Female Black Bear Harvest Rates in Pennsylvania","interactions":[],"lastModifiedDate":"2024-09-09T15:01:45.550938","indexId":"70256426","displayToPublicDate":"2023-07-05T09:55:01","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-150-2023","title":"The Factors Affecting Female Black Bear Harvest Rates in Pennsylvania","docAbstract":"Pennsylvania’s black bear (Ursus americanus) population increased in abundance and distribution during the latter third of the 20th century, leading to an increase in human-bear conflicts. Increases in harvest opportunities from 2002–2018 aimed to stabilize black bear population growth but did not substantially increase harvest, and annual harvest was often below the desired goal of 20% removal. Consequently, additional changes to Pennsylvania’s black bear hunting seasons occurred from 2019–2021, including starting harvest earlier, expanding the length of seasons, and adding additional seasons (i.e., a muzzleloader and special rifle season). Understanding how earlier harvest seasons and new methods of take (i.e., muzzleloader) influence female black bear harvest vulnerability is important to informing harvest management. We trapped and GPS-collared adult female bears in the Sproul State Forest in northcentral Pennsylvania from 2019–2021 to determine home range sizes, patterns of resource selection, and sources of mortality during fall harvest seasons. We assessed annual variability in relative abundance of fall hard mast. We evaluated temporal and spatial variation in hunter activity with road-side surveys and remote camera traps, respectively. We estimated fall and weekly home range size with utilization distributions through an autocorrelated kernel density estimation and evaluated the influence of predictors hypothesized to influence third-order resource selection using generalized linear mixed models. We investigated factors hypothesized to influence female black bear survival during hunting seasons with known-fate models. Mean fall home range size was 248.7 km2 (range = 6.1–2636.1 km2). Home range sizes varied by year and were generally smaller during archery harvest season than other periods. Patterns of weekly resource selection indicated bears selected steeper slopes and higher elevations outside of harvest seasons but shifted to less-steep areas in the week before harvest and the first week of harvest, and to lower elevations during harvest. Bears selected for areas containing oak (Quercus spp.) trees throughout the fall. Survival was lower in older age bears, greater relative mast abundance conditions, steeper slopes, and areas of greater hunter space use during the general firearms season. Survival was higher in areas of greater hunter space use during archery season. Harvest rate of adult female bears was 0.345 in 2019, 0.321 in 2020, and 0.150 in 2021, and averaged 0.272 across all three years. The probability of an adult female black bear surviving all harvest seasons each year was 0.611 (SE = 0.086, 95% CI = 0.436, 0.761). The high harvest rate and low predicted survival may lead to population reduction.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Snavely, Brandon M.","contributorId":340528,"corporation":false,"usgs":false,"family":"Snavely","given":"Brandon","email":"","middleInitial":"M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907331,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250980,"text":"70250980 - 2023 - Identifying invertebrate indicators for streamflow duration assessments in forested headwater streams","interactions":[],"lastModifiedDate":"2024-01-17T12:49:54.187192","indexId":"70250980","displayToPublicDate":"2023-07-05T06:46:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Identifying invertebrate indicators for streamflow duration assessments in forested headwater streams","docAbstract":"Streamflow-duration assessment methods (SDAMs) are rapid, indicator-based tools for classifying streamflow duration (e.g., intermittent vs perennial flow) at the reach scale. Indicators are easily assessed stream properties used as surrogates of flow duration, which is too resource intensive to measure directly for many reaches. Invertebrates are commonly used as SDAM indicators because many are not highly mobile, and different species have life stages that require flow for different durations and times of the year. The objectives of this study were to 1) identify invertebrate taxa that can be used as SDAM indicators to distinguish between stream reaches having intermittent and perennial flow, 2) to compare indicator strength across different taxonomic and numeric resolutions, and 3) to assess the relative importance of season and habitat type on the ability of invertebrates to predict streamflow-duration class. We used 2 methods, random forest models and indicator species analysis, to analyze aquatic and terrestrial invertebrate data (presence/absence, density, and biomass) at the family and genus levels from 370 samples collected from both erosional and depositional habitats during both wet and dry seasons. In total, 36 intermittent and 53 perennial reaches were sampled along 31 forested headwater streams in 4 level II ecoregions across the United States. Random forest models for family- and genus-level datasets had stream classification accuracy ranging from 88.9 to 93.2%, with slightly higher accuracy for density than for presence/absence and biomass datasets. Season (wet/dry) tended to be a stronger predictor of streamflow-duration class than habitat (erosional/depositional). Many taxa at the family (58.8%) and genus level (61.6%) were collected from both intermittent and perennial reaches, and most taxa that were exclusive to 1 streamflow-duration class were rarely collected. However, 23 family-level or higher taxa (20 aquatic and 3 terrestrial) and 44 aquatic genera were identified as potential indicators of streamflow-duration class for forested headwater streams. The utility of the potential indicators varied across level II ecoregions in part because of representation of intermittent and perennial reaches in the dataset but also because of variable ecological responses to drying among species. Aquatic invertebrates have been an important field indicator of perennial reaches in existing SDAMs, but our findings highlight how including aquatic and terrestrial invertebrates as indicators of intermittent reaches can further maximize the data collected for streamflow-duration classifications.
Forested landscapes in the Western United States are subject to growing size and severity of wildfires, in part due to historical management strategies focusing on wildfire suppression. Forest restoration treatments and fuels reductions, including thinning and prescribed burning, can reduce the frequency and intensity of wildfires. Extensive restoration and fuels treatment efforts are underway across many areas in the Southwestern United States, including New Mexico. The tradeoff between amenity values provided by forested landscapes and the wildfire risk associated with forested landscapes is becoming increasingly important to understand as development in the wildland-urban interface increases. Understanding how house proximity, relative to forest restoration or fuels treatments, is capitalized into home sale prices can provide useful information about how individuals value forested landscapes that have been altered to reduce wildfire risk or severity. We use a Hedonic Property Model to estimate the average treatment effect of proximity to forest restoration or fuel treatments in New Mexico, United States. We use matching methods to estimate the average treatment effect of proximity to forest restoration. We find that proximity to the forest has a positive amenity value; however, proximity to recent forest restoration or fuel treatments results in a decrease in house sale prices. We combine the results of our two models and calculate that homes not within one kilometer of a treated forest and within one kilometer of Cibola National Forest sell for an average $73,626 premium. The average premium drops to $22,996 for homes within one kilometer of a forest that has been recently treated and within one kilometer of Cibola National Forest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2023.104838","usgsCitation":"Fitch, R.A., Mueller, J.M., Meldrum, J., and Huber, C., 2023, Estimating proximity effects to wildfire fuels treatments on house prices in Cibola National Forest, New Mexico, USA: Landscape and Urban Planning, v. 238, 104838, 9 p., https://doi.org/10.1016/j.landurbplan.2023.104838.","productDescription":"104838, 9 p.","ipdsId":"IP-143350","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":442867,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landurbplan.2023.104838","text":"Publisher Index Page"},{"id":418740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cibola National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.57936451046446,\n 35.35726965713178\n ],\n [\n -106.57936451046446,\n 34.84741122784986\n ],\n [\n -106.11539121622326,\n 34.84741122784986\n ],\n [\n -106.11539121622326,\n 35.35726965713178\n ],\n [\n -106.57936451046446,\n 35.35726965713178\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fitch, Ryan A.","contributorId":316216,"corporation":false,"usgs":false,"family":"Fitch","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":877050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Julie M.","contributorId":219795,"corporation":false,"usgs":false,"family":"Mueller","given":"Julie","email":"","middleInitial":"M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":877051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":877052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":877053,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254722,"text":"70254722 - 2023 - The scale-dependent role of submerged macrophytes as drift-feeding lotic fish habitat","interactions":[],"lastModifiedDate":"2024-06-11T12:14:10.470494","indexId":"70254722","displayToPublicDate":"2023-07-04T07:10:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The scale-dependent role of submerged macrophytes as drift-feeding lotic fish habitat","docAbstract":"The stable isotopic composition of pedogenic carbonates is central to many studies of past climate and topography, providing a basis for our understanding of Earth's terrestrial history. A core assumption of many applications of oxygen isotope values (δ18O) of pedogenic carbonate is that they reflect the δ18O value of precipitation (rain/snow). This assumption is violated if soil carbonates form in evaporated soil waters. In this work, we develop a means to identify evaporation in ancient soils using the triple oxygen isotope composition (16O-17O-18O) of pedogenic carbonates. Both theoretical predictions of isotope kinetics during evaporation and studies of triple oxygen isotopes in other geological materials show that the deviation in the relationship between δ17O and δ18O from a reference line, evaluated using the parameter Δ'17O, is sensitive to evaporation. As a first step in developing the use of Δ'17O in ancient pedogenic carbonates, we report Δ'17O values from 47 near-modern pedogenic carbonate samples from globally distributed environments that vary in aridity (hyper-arid to humid). The Δ'17O values of pedogenic carbonate range from -154 to -60 per meg (as CaCO3, measured via O2, VSMOW-SLAP), corresponding to calculated soil water values of -66 to +27 per meg (VSMOW-SLAP) (using a carbonate-water triple oxygen isotope fractionation exponent of 0.5250 and clumped isotope-derived carbonate growth temperatures). The Δ'17O values indicate that evaporative modification of soil water from which pedogenic carbonate forms is common, especially in arid environments. Arid environments host pedogenic carbonates formed from soil waters ranging from highly to minimally evaporated, while humid environments host pedogenic carbonates formed from waters that are only minimally evaporated. The variability in Δ'17O within environments classified by the same aridity may relate to the fact that pedogenic carbonates record soil conditions only during times of carbonate mineralization, which may deviate from annual conditions. Thus, Δ'17O may be useful in understanding the specific circumstances of pedogenic carbonate formation but may not provide incontrovertible evidence of the magnitude of environmental aridity. Evaporative modification of δ18O values of pedogenic carbonates can be detected with Δ'17O, thereby improving estimates of δ18O of unevaporated waters. Our data show that evaporation must be (re)considered for all paleoclimate inferences based on the δ18O of pedogenic carbonate. The addition of Δ'17O will re-energize paleoclimate studies that use (or have avoided using) δ18O of pedogenic carbonate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2023.06.034","usgsCitation":"Kelson, J., Huth, T., Passey, B.H., Levin, N.E., Petersen, S.V., Ballato, P., Beverly, E.J., Breecker, D.O., Hoke, G.D., Hudson, A.M., Haoyuan, J., Licht, A., and Quade, J., 2023, Triple oxygen isotope compositions of globally distributed soil carbonates record widespread evaporation of soil waters: Geochimica et Cosmochimica Acta, v. 355, p. 138-160, https://doi.org/10.1016/j.gca.2023.06.034.","productDescription":"23 p.","startPage":"138","endPage":"160","ipdsId":"IP-148273","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":442883,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hal.science/hal-04160695","text":"Publisher Index Page"},{"id":418704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"355","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kelson, Julia 0000-0002-0588-5018","orcid":"https://orcid.org/0000-0002-0588-5018","contributorId":219941,"corporation":false,"usgs":false,"family":"Kelson","given":"Julia","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huth, Tyler E.","contributorId":315599,"corporation":false,"usgs":false,"family":"Huth","given":"Tyler E.","affiliations":[{"id":68361,"text":"Department of Earth & Environmental Sciences, University of Michigan","active":true,"usgs":false}],"preferred":false,"id":876923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passey, Benjamin H.","contributorId":315600,"corporation":false,"usgs":false,"family":"Passey","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":68361,"text":"Department of Earth & Environmental Sciences, University of Michigan","active":true,"usgs":false}],"preferred":false,"id":876924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Levin, Naomi E.","contributorId":315601,"corporation":false,"usgs":false,"family":"Levin","given":"Naomi","email":"","middleInitial":"E.","affiliations":[{"id":68361,"text":"Department of Earth & Environmental Sciences, University of Michigan","active":true,"usgs":false}],"preferred":false,"id":876925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Petersen, Sierra V.","contributorId":201014,"corporation":false,"usgs":false,"family":"Petersen","given":"Sierra","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":876926,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ballato, Paolo","contributorId":315602,"corporation":false,"usgs":false,"family":"Ballato","given":"Paolo","email":"","affiliations":[{"id":68362,"text":"Department of Science, Geological Sciences Section, University of Roma Tre","active":true,"usgs":false}],"preferred":false,"id":876927,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beverly, Emily J.","contributorId":315603,"corporation":false,"usgs":false,"family":"Beverly","given":"Emily","email":"","middleInitial":"J.","affiliations":[{"id":68365,"text":"Department of Earth and Atmospheric Sciences, University of Houston","active":true,"usgs":false}],"preferred":false,"id":876928,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Breecker, Daniel O.","contributorId":215845,"corporation":false,"usgs":false,"family":"Breecker","given":"Daniel","email":"","middleInitial":"O.","affiliations":[{"id":39318,"text":"University of Texas-Austin","active":true,"usgs":false}],"preferred":false,"id":876929,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoke, Gregory D.","contributorId":315604,"corporation":false,"usgs":false,"family":"Hoke","given":"Gregory","email":"","middleInitial":"D.","affiliations":[{"id":68366,"text":"Department of Earth and Environmental Sciences, Syracuse University,","active":true,"usgs":false}],"preferred":false,"id":876930,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hudson, Adam M. 0000-0002-3387-9838 ahudson@usgs.gov","orcid":"https://orcid.org/0000-0002-3387-9838","contributorId":195419,"corporation":false,"usgs":true,"family":"Hudson","given":"Adam","email":"ahudson@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":876931,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Haoyuan, Ji","contributorId":315605,"corporation":false,"usgs":false,"family":"Haoyuan","given":"Ji","email":"","affiliations":[{"id":68367,"text":"Google LLC, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":876932,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Licht, Alexis","contributorId":315606,"corporation":false,"usgs":false,"family":"Licht","given":"Alexis","email":"","affiliations":[{"id":68368,"text":"Aix-Marseille Université","active":true,"usgs":false}],"preferred":false,"id":876933,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Quade, Jay","contributorId":22108,"corporation":false,"usgs":false,"family":"Quade","given":"Jay","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":876934,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70249392,"text":"70249392 - 2023 - Human and infrastructure exposure to large wildfires in the United States","interactions":[],"lastModifiedDate":"2023-11-20T17:39:09.584907","indexId":"70249392","displayToPublicDate":"2023-07-03T06:43:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5791,"text":"Nature Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Human and infrastructure exposure to large wildfires in the United States","docAbstract":"An increasing number of wildfire disasters have occurred in recent years in the United States. Here we demonstrate that cumulative primary human exposure—the population residing within the perimeters of large wildfires—was 594,850 people from 2000 to 2019 across the contiguous United States (CONUS), 82% of which occurred in the western United States. Primary population exposure increased by 125% in the CONUS in the past two decades; it was noted that there were large statistical uncertainties in the trend analysis due to the short study timeline. Population dynamics from 2000 to 2019 alone accounted for 24% of the observed increase rate in human exposure, and an increased wildfire extent drove the majority of the observed trends. In addition, we document the widespread exposure of roads (412,155 km) and transmission powerlines (14,835 km) to large wildfires in the CONUS, with a relative increase of 58% and 70% in the past two decades, respectively. Our results highlight that deliberate mitigation and adaptation efforts to help societies cope with wildfires are ever more needed.
Earthquake risk associated with Quito, Ecuador; Cali, Colombia; and Santiago de los Caballeros, Dominican Republic is examined by generating a set of hypothetical earthquake scenarios considering seismic sources, recent seismicity, and major historical earthquakes recorded in the vicinity. In this study, particular focus is given to the development of earthquake scenarios for use in emergency planning in each city as well as stimulating discussion with respect to addressing the gaps in current understanding of built stock inventory and their vulnerability when subjected to strong shaking. Exposure and vulnerability models from the Global Earthquake Model foundation, hazard and local site information available for the cities, and the U.S. Geological Survey near‐real‐time products are utilized to estimate potential consequences for postearthquake response planning. Results showed that the historic city centers remain the most susceptible to experiencing severe damage resulting in widespread casualties. Similarly, the scenarios highlight areas susceptible to shaking induced ground failure hazards, which may pose additional challenges when responding to such earthquakes. Moderate earthquakes originating from nearby seismic sources, for example, Quito fault system for Quito or the Septentrional fault zone in the case of Santiago de Los Caballeros, could potentially be of greater consequence in terms of direct economic impact and disruption to the city when compared to very large distant subduction interface earthquakes.
Bats are important pest control agents in agriculture. Yet, the underlying fine-scale biotic and abiotic mechanisms that drive their foraging behaviors and responses to insect outbreaks are unclear. Herbivore-induced plant volatiles (HIPVs) can attract both invertebrate and vertebrate natural enemies that use the chemical plant cues to locate insect prey. The ability of HIPVs to attract multiple species raises the question of whether they may also be a biotic factor influencing insectivorous bat activity. Additionally, abiotic factors, such as weather conditions, can affect bat activity in agricultural settings, but little is known about how bats respond to shifting environmental conditions on short timescales in this landscape context. Using a model crop system, soybean (Glycine max), our study asked three questions: (1) Which bat species are active in eastern Maryland soybean fields? (2) Is insectivorous bat activity affected by naturally occurring soybean HIPVs and/or synthetic soybean HIPVs (indole or farnesene)? (3) How is insectivorous bat activity affected by hourly weather conditions in this landscape? In soybean fields in eastern Maryland, we created paired treatment plots: HIPV plots (damaged plants or synthetic HIPV dispensers) and control plots (undamaged plants or empty dispensers). We measured bat activity using ultrasonic recorders, summarizing hourly and nightly activity, and detected 10 total species. The most abundant species were big brown/silver-haired bats (Eptesicus fuscus/Lasionycteris noctivagans). Bat activity did not significantly differ between control and HIPV plots in any of the three experiments. Thus, our results do not support our expectation that bats in eastern Maryland use soybean HIPVs to locate insect prey. However, bat activity did increase with increasing average hourly temperature and wind speed. This initial study of bats and HIPVs, as well as the fine-scale examination of weather conditions on bat activity, may serve as a guide for future research on bat–plant interactions that can support the development of new strategies for sustainable pest management.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4575","usgsCitation":"Maynard, L., Ford, W., Parker, J., and Whitehead, S., 2023, Biotic and abiotic factors shaping bat activity in Maryland soybean fields: Ecosphere, v. 14, no. 7, e4575, 13 p., https://doi.org/10.1002/ecs2.4575.","productDescription":"e4575, 13 p.","ipdsId":"IP-145893","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4575","text":"Publisher Index Page"},{"id":466422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":22,\"properties\":{\"name\":\"Maryland\",\"nation\":\"USA 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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parker, John D.","contributorId":348247,"corporation":false,"usgs":false,"family":"Parker","given":"John D.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":923306,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitehead, Susan R.","contributorId":348249,"corporation":false,"usgs":false,"family":"Whitehead","given":"Susan R.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":923307,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247715,"text":"70247715 - 2023 - 2023 Coastal master plan: ICM-wetlands – Submerged aquatic vegetation (SAV) updates","interactions":[],"lastModifiedDate":"2023-08-15T15:31:34.073116","indexId":"70247715","displayToPublicDate":"2023-07-01T10:27:06","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"chapter":"Attachment D3","title":"2023 Coastal master plan: ICM-wetlands – Submerged aquatic vegetation (SAV) updates","docAbstract":"Submerged aquatic vegetation (SAV) provides critical structural habitat for valuable nekton and wildlife species across coastal ecosystems and can buffer the negative effects of land loss. Landscape change and restoration efforts across coastal Louisiana can impact the occurrence, coverage, and species assemblages of SAV, and changes to these foundational species can have cascading impacts across food webs. To support the 2023 Coastal Master Plan efforts, a unique SAV model was developed to assess coverage and occurrence of SAV across aquatic waterbodies in response to environmental variables evaluated.
This effort created a spatial model describing the probability of presence of SAV across the study area in response to changing conditions over the modeled time period. To develop the initial coverage data layer, we used remotely sensed Normalized Difference Vegetation Index (NDVI) and modified Normalized Difference Water Index (mNDWI) data from 2015-2018 to identify areas containing variable vegetation and water spectral reflectance. Key environmental variables evaluated included total suspended sediments (TSS), salinity, and physical exposure. Seasonal estimates for TSS and salinity were used, as research indicates that seasonal environmental variability is a significant driver for SAV establishment. Seasonal salinity was derived from Coast-wide Reference Monitoring Station (CRMS) data, and seasonal TSS was estimated from hyperspectral imagery. Estimates of physical exposure have previously been provided by calculating fetch (the distance across water over which waves can propagate), but this proved to be too computationally intensive to be feasible, and we found distance to land to be a reasonable proxy for exposure. To represent geographic conditions and historical factors influences on SAV establishment and occurrence (e.g., variables too numerous and complex to model) we developed a basin variable that served as a proxy for complex historical, or prior, conditions, determined by the forested, fresh, intermediate, brackish, or saline (FFIBS) score. The final model included spring TSS, spring salinity, distance to land, and the basin prior.
The model performed well for the area evaluated, correctly classifying SAV (as present or absent) 89% of the time (Kappa = 580). SAV probability of presence responded as expected to change in these environmental variables, with likelihood of occurrence decreasing in response to increasing spring TSS, spring salinity, and distance to land. However, the model was more accurate at predicting absence (true negative = 0.940) than predicting presence (true positive = 0.626), suggesting that the scale of the model may limit the ability to predict presence. Moreover, the simplicity of the model limited the accuracy in highly dynamic environments, for example near the outflow of diversions or areas of significant changes in salinity or TSS. Through incorporating underwater communities like SAV, this master plan provides a holistic view of coastal change and restoration. To create healthy ecological structure and function in wetland habitats, the submergent communities must be considered alongside the emergent habitats. As the benefits of SAV are increasingly recognized, both here in Louisiana and beyond, SAV restoration and the use of SAV communities in assessing and improving ecological condition are becoming more common.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Louisiana’s comprehensive master plan for a sustainable coast","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","usgsCitation":"DeMarco, K., Schoolmaster, D., and Couvillion, B., 2023, 2023 Coastal master plan: ICM-wetlands – Submerged aquatic vegetation (SAV) updates (Version 2), 58 p.","productDescription":"58 p.","ipdsId":"IP-151482","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":419827,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419802,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/2023-plan-appendices/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.79077632406495,\n 30.706267411766817\n ],\n [\n -93.79077632406495,\n 28.854615329475607\n ],\n [\n -88.34722956694776,\n 28.27842992330551\n ],\n [\n -89.00478141362511,\n 30.706267411766817\n ],\n [\n -93.79077632406495,\n 30.706267411766817\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeMarco, Kristin","contributorId":200003,"corporation":false,"usgs":false,"family":"DeMarco","given":"Kristin","email":"","affiliations":[],"preferred":false,"id":880146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoolmaster, Donald 0000-0003-0910-4458","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":202356,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":880147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":880148,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}