As conversion of natural areas to human development continues, there is a lack of information about how developed areas can sustainably support wildlife. While large predators are often extirpated from areas of human development, some medium-bodied mammalian predators (hereafter, mesocarnivores) have adapted to co-exist in human-dominated areas.
How human-dominated areas such as residential yards are used by mesocarnivores is not well understood. Our study aimed to identify yard and landscape features that influence occupancy, relative abundance and spatial-temporal overlap of three widespread mesocarnivores, namely, coyote (Canis latrans), grey fox (Urocyon cineroargenteus) and red fox (Vulpes vulpes).
Over the summers of 2021 and 2022, we deployed camera-traps in 46 and 96 residential yards, spanning from low-density rural areas (<1 home per km2) to more urban areas (589 homes per km2) in north-western Arkansas, USA.
We found that mesocarnivore occupancy was marginally influenced by yard-level features as opposed to landscape composition. Fences reduced the occupancy probability of coyotes, although they were positively associated with the total area of potential shelter sites in a yard. We found that relative abundance of grey fox was highest in yards with poultry, highlighting a likely source of conflict with homeowners. We found that all three species were primarily nocturnal and activity overlap between the species pairs was high.
Thus, these species may be using spatio-temporal partitioning to avoid antagonistic encounters and our data supported this, with few examples of species occurring in the same yards during the same 24-h period.
As the number of residential yards continues to grow, our results suggested that there are ways in which our yards can provide resources to mesocarnivores and that homeowners also have agency to mitigate overlap with mesocarnivores through management of their yard features.
No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Groundwater science relevant to the Great Lakes Water Quality Agreement: An updated status report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"U.S. Environmental Protection Agency and Environment and Climate Change Canada","usgsCitation":"Reeves, H.W., Danielescu, S., Priebe, E., and Zhang, H., 2024, Groundwater/surface-water interaction, 15 p.","productDescription":"15 p.","startPage":"9","endPage":"23","ipdsId":"IP-167804","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":480779,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://binational.net/2025/01/10/groundwater-science-updated-status-report/"},{"id":480933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Danielescu, Serban","contributorId":349778,"corporation":false,"usgs":false,"family":"Danielescu","given":"Serban","affiliations":[],"preferred":false,"id":924784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Priebe, Elizabeth","contributorId":349779,"corporation":false,"usgs":false,"family":"Priebe","given":"Elizabeth","affiliations":[],"preferred":false,"id":924785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Helen","contributorId":295491,"corporation":false,"usgs":false,"family":"Zhang","given":"Helen","email":"","affiliations":[{"id":63895,"text":"Ontario Ministry of the Environment","active":true,"usgs":false}],"preferred":false,"id":924786,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262206,"text":"70262206 - 2024 - Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds","interactions":[],"lastModifiedDate":"2025-01-15T15:04:12.077019","indexId":"70262206","displayToPublicDate":"2025-01-06T09:01:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds","docAbstract":"Floating photovoltaic (FPV) solar energy offers promise for renewable electricity production that spares land for other societal benefits. FPV deployment may alter greenhouse gas (GHG) production and emissions from waterbodies by changing physical, chemical, and biological processes, which can have implications for the carbon cost of energy production with FPV. Here, we use an ecosystem-scale experiment to assess how GHG dynamics in ponds respond to installation of operationally representative FPV. Following FPV deployments of 70% array coverage, daily whole-pond GHG emissions increased by 26.8% on a carbon dioxide-equivalent (CO2-eq) basis, and dissolved oxygen availability rapidly decreased. Despite increased emissions following FPV deployment, FPV-derived GHG emissions from waterbodies are likely lower than landscape GHG emissions associated with terrestrial solar and hydropower production on a CO2-eq kWh–1 basis. Adaptive management strategies like bubbler installation may reduce the magnitude of FPV impacts on GHG and dissolved oxygen dynamics.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c06363","usgsCitation":"Ray, N.E., Holgerson, M., and Grodsky, S.M., 2024, Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds: Environmental Science and Technology, v. 58, no. 50, p. 22104-22113, https://doi.org/10.1021/acs.est.4c06363.","productDescription":"10 p.","startPage":"22104","endPage":"22113","ipdsId":"IP-165832","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466694,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1021/acs.est.4c06363","text":"External Repository"},{"id":466411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"50","noUsgsAuthors":false,"publicationDate":"2024-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ray, Nicholas E.","contributorId":333311,"corporation":false,"usgs":false,"family":"Ray","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":923502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holgerson, Meredith A.","contributorId":348513,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grodsky, Steven Mark 0000-0003-0846-7230","orcid":"https://orcid.org/0000-0003-0846-7230","contributorId":328517,"corporation":false,"usgs":true,"family":"Grodsky","given":"Steven","email":"","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923504,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260994,"text":"70260994 - 2024 - Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations","interactions":[],"lastModifiedDate":"2024-11-20T15:34:27.919637","indexId":"70260994","displayToPublicDate":"2025-01-01T08:25:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations","docAbstract":"The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2024.104641","usgsCitation":"Itzkin, M., Palmsten, M.L., Buckley, M.L., Birchler, J.J., and Torres-Garcia, L.M., 2024, Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations: Coastal Engineering, v. 195, 104641, 13 p., https://doi.org/10.1016/j.coastaleng.2024.104641.","productDescription":"104641, 13 p.","ipdsId":"IP-156552","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2024.104641","text":"Publisher Index Page"},{"id":464342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Duck","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.76606524647661,\n 36.204406383193415\n ],\n [\n -75.76606524647661,\n 36.151059222902816\n ],\n [\n -75.73552468770632,\n 36.151059222902816\n ],\n [\n -75.73552468770632,\n 36.204406383193415\n ],\n [\n -75.76606524647661,\n 36.204406383193415\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"195","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Itzkin, Michael 0000-0003-0693-0607","orcid":"https://orcid.org/0000-0003-0693-0607","contributorId":291846,"corporation":false,"usgs":true,"family":"Itzkin","given":"Michael","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buckley, Mark L. 0000-0002-1909-4831","orcid":"https://orcid.org/0000-0002-1909-4831","contributorId":203481,"corporation":false,"usgs":true,"family":"Buckley","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torres-Garcia, Legna M. 0000-0002-6786-5944 ltorresgarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-5944","contributorId":196150,"corporation":false,"usgs":true,"family":"Torres-Garcia","given":"Legna","email":"ltorresgarcia@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":918822,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261333,"text":"70261333 - 2024 - Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis","interactions":[],"lastModifiedDate":"2024-12-06T15:15:40.076596","indexId":"70261333","displayToPublicDate":"2025-01-01T08:10:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis","docAbstract":"The increasing threat of post-wildfire hazards creates an imperative for improved post-wildfire flooding and debris flow prediction capabilities. Because rainfall is a primary driver of predictive hydrology and debris flow initiation and inundation models, recent efforts have emphasized the need for interdisciplinary collaboration between meteorology and post-wildfire hazard science that develops more accurate rainfall estimates with longer lead times. In this work, we identified critical knowledge gaps for developing rainfall estimates and filled those gaps by reviewing recent literature and synthesizing pre-existing datasets. Gap areas were organized into the following general topics: a) rainfall intensity-duration-frequency relations, b) time-varying rainfall, c) spatially varying rainfall, and d) rainfall regimes.
Recent key research advances include the increasing availability of gridded quantitative rainfall estimates, the expanded use of distributed hydrologic and erosion models that incorporate spatial and temporal variability in rainfall, and the linking of concepts and modeling from the atmospheric and climate sciences with post-wildfire hazard science. We prototype a rainfall regime regionalization schema that captures self-similar properties of rainfall intensity (k, the maximum rainfall intensity) and temporal scaling (n, the decay rate). Our k-n relations schema could serve as a framework for organizing, interpreting, and predicting post-wildfire hydrologic and erosional responses. Finally, we summarize salient gaps for implementing spatiotemporally varying rainfall as the driver of post-wildfire hydrologic models designed to improve the prediction of flooding and debris flow hazards to the built environment for emergency managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104990","usgsCitation":"Collar, N.M., Moody, J.A., and Ebel, B., 2024, Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis: Earth-Science Reviews, v. 260, 104990, 32 p., https://doi.org/10.1016/j.earscirev.2024.104990.","productDescription":"104990, 32 p.","ipdsId":"IP-164156","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":464885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"260","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collar, Natalie M. 0000-0003-4711-0090","orcid":"https://orcid.org/0000-0003-4711-0090","contributorId":306155,"corporation":false,"usgs":false,"family":"Collar","given":"Natalie","email":"","middleInitial":"M.","affiliations":[{"id":66376,"text":"Colorado School of Mines, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":920406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":920407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":920408,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261548,"text":"70261548 - 2024 - Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","interactions":[],"lastModifiedDate":"2024-12-13T15:13:03.033614","indexId":"70261548","displayToPublicDate":"2025-01-01T07:58:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","docAbstract":"Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.122520","usgsCitation":"Suarez-Castro, A.F., Robertson, D., Lehner, B., de Souza, M.L., Kittridge, M., Saad, D., Linke, S., McDowell, R.W., Ranjbar, M.H., Ausseil, O., and Hamilton, D.P., 2024, Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales: Water Research, v. 268, no. 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NZ","active":true,"usgs":false}],"preferred":false,"id":920991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ranjbar, Mohammad H.","contributorId":347161,"corporation":false,"usgs":false,"family":"Ranjbar","given":"Mohammad","email":"","middleInitial":"H.","affiliations":[{"id":80337,"text":"Griffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ausseil, Olivier","contributorId":347162,"corporation":false,"usgs":false,"family":"Ausseil","given":"Olivier","email":"","affiliations":[{"id":83091,"text":"Girffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920993,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hamilton, David P. 0000-0002-9341-8777","orcid":"https://orcid.org/0000-0002-9341-8777","contributorId":347163,"corporation":false,"usgs":false,"family":"Hamilton","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":83086,"text":"Griffith University, Brisbane, Australia","active":true,"usgs":false}],"preferred":false,"id":920994,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70261890,"text":"sir20245126 - 2024 - Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","interactions":[],"lastModifiedDate":"2025-07-10T15:28:29.176134","indexId":"sir20245126","displayToPublicDate":"2024-12-31T15:00:00","publicationYear":"2024","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":"2024-5126","displayTitle":"Simulating Present and Future Groundwater/Surface-Water Interactions and Stream Temperatures in Beaver Creek, Kenai Peninsula, Alaska","title":"Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","docAbstract":"In many places, coldwater ecosystems are facing increasing pressure from anthropogenic warming. This study examined stream temperatures and the water balance in the Beaver Creek watershed on the Kenai Peninsula in south-central Alaska—an area that is experiencing rapid warming. Low-gradient streams near the Kenai coast provide important spawning and rearing habitat for salmon but may be especially vulnerable to rising temperatures, because of long residence times, inflows from abundant riparian wetlands, and reliance on groundwater discharge that may also warm, or decrease in volume with rising evapotranspiration. In recent decades, observed maximum 7-day temperatures have consistently exceeded statistical (regression-based) projections. Here we simulate total streamflows and temperatures with a physics-based model that links the Soil Water Balance, MODFLOW 6 and SNTEMP simulation codes on a 7-day timestep. The model is based on existing data and groundwater levels, instream flows, and stream temperatures collected during 2019–23. Future climate scenarios were developed for 2023–50 from downscaled climate projections.
Results indicate that groundwater discharge is about 64 percent of the total streamflow during the months of May through September. Total streamflow and groundwater discharge are expected to remain similar to current conditions through 2050. Stream temperatures are expected to rise; by midcentury, near the Beaver Creek mouth the model predicts 34 to 63 additional days per year with average weekly temperatures above 13 degrees Celsius, 14 to 81 additional days with average weekly temperatures above 15 degrees Celsius, and routine exceedances of 20 degrees Celsius during the warmest periods. Projected stream temperatures vary spatially. Areas of high groundwater inflows in the lower main stem and some tributaries may be most resilient to warming air temperatures during dry conditions. During storm events, groundwater-dominated tributaries may have the coolest stream temperatures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245126","usgsCitation":"Leaf, A.T., Haserodt, M.J., Meyer, B.E., Westenbroek, S.M., and Koch, J.C., 2024, Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5126, 111 p., https://doi.org/10.3133/sir20245126.","productDescription":"Report: ix, 111 p.; 2 Data Releases; Dataset","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167012","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":465606,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.XML"},{"id":465583,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14UAWGB","text":"USGS data release","linkHelpText":"Surface water and groundwater hydrology and temperature, Beaver Creek, Kenai Peninsula, Alaska, 2022–2023"},{"id":465585,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465584,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"http://doi.org/10.5066/P9K30VAP","text":"USGS data release","linkHelpText":"Soil water balance, groundwater flow, and stream temperature models for Beaver Creek, Alaska, 2019 to 2050"},{"id":492014,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118271.htm","linkFileType":{"id":5,"text":"html"}},{"id":465607,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5126/images/"},{"id":465605,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245126/full"},{"id":465582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.pdf","text":"Report","size":"34.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5126"},{"id":465581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5126/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaver Creek, Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.3330540056542,\n 60.957367731319806\n ],\n [\n -152.38912986860709,\n 59.25155522317334\n ],\n [\n -148.50867874659667,\n 59.25563148250791\n ],\n [\n -148.50867874659667,\n 60.95766646209441\n ],\n [\n -152.3330540056542,\n 60.957367731319806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Wolf harvest season setting is complicated and controversial. State law requires Montana Fish, Wildlife and Parks (MFWP) to both reduce the wolf population and avoid federal relisting under the Endangered Species Act (Montana Fish, Wildlife and Parks, 2002). Disparate stakeholder groups each have different objectives for wolf management. For instance, big game advocates want to see improved big game populations and hunting opportunities in northwest Montana, while wolf advocates want to see regulations that minimize wolf mortality. Decision making about season setting tries to balance these objectives. Wolf hunting and trapping season decisions are made by the Montana Fish and Wildlife Commission and are informed by annual wolf abundance estimates from an integrated patch occupancy model (iPOM, Sells et al., 2022c) as well as the predictions of wolf abundance into the future under potential constant harvest levels. Parametric uncertainty (uncertainty surrounding the value of a parameter) from the iPOM estimates is propagated through to future projections, providing the Commission with plausible and worst-case outcomes of different levels of public harvest over the short term, i.e., five years into the future, on the wolf population in Montana (Parks et al., 2024).
An alternative approach to inform wolf management and harvest decisions is through adaptive management. Adaptative management is appropriate for decisions that are made iteratively and when monitoring data are collected to learn about the outcomes from decisions, where monitoring data help to reduce critical uncertainties regarding ecosystem function or management outcomes (Walters, 1986; Williams, 2011). Management strategy evaluation (MSE) is one way to develop an adaptive management framework. MSE was developed by fisheries managers and scientists to more accurately and fully incorporate various forms of uncertainty, consider long-term time horizons, and add more transparency in a fisheries context (Punt et al., 2016). It has been used routinely and has become a standard approach for complicated and contentious marine fisheries management situations, yet it has been underutilized in wildlife management (but see Bunnefeld et al., 2013, 2011).
MSE is a forward simulation approach for testing prospective management options or strategies over a wide range of possible states (Punt et al., 2016). A MSE framework captures the ‘truth’ or what is happening in the system (termed the ‘the operating model’) and the information available to the decision makers (termed ‘the estimation model’ or ‘management strategy’). More precisely, there are four main processes modeled. First, models are constructed based on current understanding and data to represent ‘truth’. Second, the collection of monitoring data is simulated from the ‘truth’ model. Third, the simulated monitoring data are fit to an estimation model and the next time step’s population metrics are predicted from the estimated parameters. Fourth, based on the estimation model results and the predictions, the decision-making process is simulated following a management strategy, whereby a decision is made and the implementation of this decision feeds back into the ‘truth’ model (Figure 1). This process continues through time. Additionally, each simulation through time is repeated to capture the full range of stochasticity and uncertainty.
Patterns of phytochemistry localisation in plant tissues are diverse within and across leaves. These spatial heterogeneities are important to the fitness of herbivores, but their effects on herbivore foraging and dietary experience remain elusive. We manipulated the spatial variance and clusteredness of a plant toxin in a synthetic diet landscape on which individual caterpillars fed. We monitored caterpillars with cameras across most of their larval development. Caterpillars that fed on diets with a lower spatial variance and more clustered arrangement of toxins had overall worse performance, mostly because those caterpillars ate less, moved more, ingested more toxin, or failed to physiologically acclimate. Using empirically parameterised individual-based models, we found that differences in movement away from, not towards, less toxic food drove a body size-dependent effect of clusteredness. Hence, the spatial pattern of phytochemicals itself, beyond mean concentration, can have important consequences for herbivores through complex interactions with herbivore foraging.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.70044","usgsCitation":"Pan, V.S., Ghosh, E., Ode, P.J., Wetzel, W.C., Gilbert, K.J., and Pearse, I.S., 2024, Large differences in herbivore performance emerge from simple herbivore behaviors and fine-scale spatial heterogeneity in phytochemistry: Ecology Letters, v. 28, no. 1, e70044, 12 p., https://doi.org/10.1111/ele.70044.","productDescription":"e70044, 12 p.","ipdsId":"IP-171430","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":497402,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70044","text":"Publisher Index Page"},{"id":497197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Pan, Vincent S. 0000-0001-9892-7805","orcid":"https://orcid.org/0000-0001-9892-7805","contributorId":332717,"corporation":false,"usgs":false,"family":"Pan","given":"Vincent","middleInitial":"S.","affiliations":[{"id":79600,"text":"Department of Integrative Biology, Michigan State University, 288 Farm Lane, East Lansing, Michigan, USA 48824","active":true,"usgs":false}],"preferred":false,"id":951695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ghosh, Enakshi","contributorId":363457,"corporation":false,"usgs":false,"family":"Ghosh","given":"Enakshi","affiliations":[{"id":80402,"text":"Colorado State U","active":true,"usgs":false}],"preferred":false,"id":951696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ode, Paul J.","contributorId":197314,"corporation":false,"usgs":false,"family":"Ode","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":951697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wetzel, William C.","contributorId":229437,"corporation":false,"usgs":false,"family":"Wetzel","given":"William","email":"","middleInitial":"C.","affiliations":[{"id":41642,"text":"Michigan State U","active":true,"usgs":false}],"preferred":false,"id":951698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilbert, Kadeem J.","contributorId":342370,"corporation":false,"usgs":false,"family":"Gilbert","given":"Kadeem","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":951699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":951700,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270667,"text":"70270667 - 2024 - Environmental DNA reveals invasion of Puerto Rican waterways by non-native Clarias catfish","interactions":[],"lastModifiedDate":"2025-08-22T14:14:43.752446","indexId":"70270667","displayToPublicDate":"2024-12-31T09:05:46","publicationYear":"2024","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":"CSS-164-2024","displayTitle":"Environmental DNA reveals invasion of Puerto Rican waterways by non-native Clarias catfish","title":"Environmental DNA reveals invasion of Puerto Rican waterways by non-native Clarias catfish","docAbstract":"The Sharptooth walking catfish (Clarias gariepinus) is native to parts of the Middle East, Asia, and Africa but is one of the world's most invasive freshwater fish species. The species’ ability to invade is partly due to its ability to breathe atmospheric oxygen, thereby avoiding hypoxia or poor water quality in receiving waterbodies; further, it can crawl over moist land to disperse to new systems. In 2018, two individuals purported to be Sharptooth walking catfish were collected and identified within the Canal de Patillas, a canal in the Guayama municipality in southeastern Puerto Rico. Several other specimens were also collected the following year. Given that this invasion was in its infancy, environmental DNA (eDNA) surveillance was employed as a rapid and efficient surveillance tool to help delineate the extent of this invasion. Water samples were collected and analyzed for Clarias eDNA from 66 sites encompassing a wide range of habitats in canals, lakes, and a river surrounding the area where they were first reported. Using eDNA-metabarcoding, we detected Clarias eDNA at 40 of the 63 sites throughout the sampled waterbodies. Furthermore, our molecular analysis indicated that the Whitespotted Clarias (C. fuscus) is the likely invader, and the original specimens, classified as C. gariepinus, were misidentified based on the use of morphological rather than genetic features for species classification. Environmental DNA surveillance indicated that the distribution of Clarias catfish in this system is substantially more extensive than previously documented. These findings suggest that additional survey efforts could help determine the invasion's full spatial extent and leading edge to inform the scope and scale of any potential management responses.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css20629765","usgsCitation":"Paine, R.T., Rogers, M.W., and Rosenberger, A.E., 2024, Environmental DNA reveals invasion of Puerto Rican waterways by non-native Clarias catfish: Cooperator Science Series CSS-164-2024, ii, 27 p., https://doi.org/10.3996/css20629765.","productDescription":"ii, 27 p.","ipdsId":"IP-173700","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946805,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266181,"text":"70266181 - 2024 - Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California","interactions":[],"lastModifiedDate":"2025-04-29T14:07:48.72537","indexId":"70266181","displayToPublicDate":"2024-12-31T09:03:52","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California","docAbstract":"Mammoth Lakes, California hosts a productive hydrothermal system within the seismically active south moat of Long Valley Caldera. Surficial evidence of the shallow hydrothermal system includes discrete zones of tree-kill dispersed between Shady Rest Park and the Casa Diablo Geothermal Power Plant (40 MW), as well as east of the power plant. The tree-kill areas are associated with elevated diffuse CO₂ emissions, heated ground, hydrothermal alteration, diffuse soil H₂S emissions, and gas vents. Previous mapping delineates prominent north and northwest trending structures within the south moat along the southwestern edge of the resurgent dome that may accommodate gas and fluid flow at the Shady Rest Park and Basalt Canyon Tree Kill Areas (SRTKA and BCTKA, respectively). Both tree-kill areas are also located along contacts between resurgent rhyolite, mafic lavas, and surficial deposits which may provide additional pathways for gas and fluid migration in the shallow subsurface.\nCharacterizing structure and lithology using geophysical anomalies is critical to determining primary structural controls on the hydrothermal system and the extent of subsurface alteration at these sites. We conducted ground and airborne-based potential field geophysical surveys to map gravity and magnetic anomalies. These anomalies are then used to model subsurface geology, structure, and hydrothermal alteration. Here we present our preliminary geophysical mapping and modelling results at both tree-kill locations. Gravity and magnetic data suggest complex structural intersections are coincident with heated ground and gas emissions at the SRTKA and BCTKA. Hydrothermal systems are often observed or interpreted to exploit fault intersections which can serve as highly permeable pathways for hydrothermal fluid and gas discharge, enabling economic geothermal energy production. Geophysical mapping and modelling are an effective means of investigating such structural complexity at Mammoth Lakes due to the presence of unidentified and concealed structures.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to Save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Rising Conference (GRC)","usgsCitation":"Anderson, J.E., Glen, J.M., Bouligand, C., Rea-Downing, G.H., and Earney, T.E., 2024, Preliminary ground and airborne-based geophysical mapping and modelling of an active hydrothermal system at Mammoth Lakes, California, in Using the Earth to Save the Earth, v. 48, p. 1613-1639.","productDescription":"17 p.","startPage":"1613","endPage":"1639","ipdsId":"IP-169794","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":485127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":485121,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035013"}],"country":"United States","state":"California","otherGeospatial":"Mammoth Lakes","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Jacob Elliott 0000-0002-0709-2548","orcid":"https://orcid.org/0000-0002-0709-2548","contributorId":329989,"corporation":false,"usgs":true,"family":"Anderson","given":"Jacob","email":"","middleInitial":"Elliott","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouligand, Claire 0000-0002-2923-1780","orcid":"https://orcid.org/0000-0002-2923-1780","contributorId":345142,"corporation":false,"usgs":false,"family":"Bouligand","given":"Claire","email":"","affiliations":[{"id":82499,"text":"Univ. Grenoble Alpes, Univ. Savoie Mont Blanc","active":true,"usgs":false}],"preferred":false,"id":934810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rea-Downing, Grant Harold 0000-0002-8567-683X","orcid":"https://orcid.org/0000-0002-8567-683X","contributorId":333087,"corporation":false,"usgs":true,"family":"Rea-Downing","given":"Grant","email":"","middleInitial":"Harold","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934811,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934812,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271332,"text":"70271332 - 2024 - Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","interactions":[],"lastModifiedDate":"2025-09-05T15:31:48.53114","indexId":"70271332","displayToPublicDate":"2024-12-31T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","docAbstract":"The spread of non-native freshwater mussels in North America is a growing threat that has already resulted in substantial ecological and economic damage to infested areas. A primary vector by which invasive mussels spread is watercraft that are transported over land from an infested waterbody to an uninfested waterbody. Management efforts such as watercraft inspection and detection programs that attempt to intercept infected watercraft can help limit this type of spread, but optimizing the effectiveness of these programs under limited resources is complicated. Studies have looked at developing watercraft inspection and decontamination program strategies that focus on policy-specific objectives such as maximizing the number of infested watercraft interceptions. However, there is limited work that has considered the heterogeneous impact of protection efforts across different regions and waterbodies. Knowledge about this heterogeneity can highlight regions that would benefit the most from protection as well as regions that would require less effort to protect, e.g., areas with naturally unsuitable water quality for dreissenids. To this end, we construct a composite relative risk index (CRR) for watersheds within the Missouri River Basin, a region in the United States on the front line of dreissenid spread. The CRR uses a model that mirrors an expected value model but uses relative indexing as a proxy for the model components. The CRR incorporates a wide array of data sets to account for the direct and indirect damages from a potential infestation along with the risk of an infestation occurring. Our results suggest that the relative priority of a specific watershed—measured through CRR—can depend on whether we consider the entire Missouri River Basin or just the watersheds in the same state. This also indicates substantial state-level heterogeneity in the CRR. Another contribution is that the CRR index includes user-specified weights for certain parameters so that a user can adjust the relative importance of various factors to match their specific context. An accompanying web tool allows users to view the CRR results and adjust multiple parameters to see the resulting impacts on the CCR for watersheds in the Missouri River Basin.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2024.112526","usgsCitation":"Raymond, J., Bair, L., Counihan, T., Daniel, W., Duntugan, S., Neilson, M., and Springborn, M.R., 2024, Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin: Ecological Indicators, v. 170, 112526, 14 p., https://doi.org/10.1016/j.ecolind.2024.112526.","productDescription":"112526, 14 p.","ipdsId":"IP-160154","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495378,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2024.112526","text":"Publisher Index 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It is known only from Mud Cave, a small linear cave on the 3,200 ha Caney Mountain Conservation Area in Ozark County, Missouri. The species is listed as “Endangered” by the state of Missouri, and “Threatened” by the American Fisheries Society. Previous studies documented its population range and limited life history notes; but little is known about Mud Cave’s habitat. We conducted a one-year study to document the cave’s water chemistry, quality, and interior wetted pool volume, and how this habitat varies monthly, to establish a baseline for future study.
","language":"English","publisher":"International Association of Astacology","doi":"10.5869/fc.2024.v29-1.121","collaboration":"Missouri Department of Conservation","usgsCitation":"Ellingsworth, E.A., DiStefano, R., Westhoff, J.T., and O'Brian, B., 2024, The endangered Caney Mountain cave crayfish: A preliminary study of its habitat with brief life history notes: Freshwater Crayfish, v. 29, no. 1, p. 121-132, https://doi.org/10.5869/fc.2024.v29-1.121.","productDescription":"12 p.","startPage":"121","endPage":"132","ipdsId":"IP-166086","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Ozark County","otherGeospatial":"Caney Mountain Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.45151688083625,\n 36.66449702263134\n ],\n [\n -92.45151688083625,\n 36.625266091515115\n ],\n [\n -92.40079722946713,\n 36.625266091515115\n ],\n [\n -92.40079722946713,\n 36.66449702263134\n ],\n [\n -92.45151688083625,\n 36.66449702263134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellingsworth, Emila A.","contributorId":360156,"corporation":false,"usgs":false,"family":"Ellingsworth","given":"Emila","middleInitial":"A.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiStefano, R.J.","contributorId":360159,"corporation":false,"usgs":false,"family":"DiStefano","given":"R.J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'Brian, B.M.","contributorId":360164,"corporation":false,"usgs":false,"family":"O'Brian","given":"B.M.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274212,"text":"70274212 - 2024 - Community synchrony in seed production is associated with trait similarity and climate across North America","interactions":[],"lastModifiedDate":"2026-03-13T15:03:12.321279","indexId":"70274212","displayToPublicDate":"2024-12-31T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Community synchrony in seed production is associated with trait similarity and climate across North America","docAbstract":"Mast seeding, the synchronous and highly variable production of seed crops in perennial plants, is a population level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long-term data are rarely available. To investigate synchrony between species within communities, we used long-term data from seven forest communities in the US Long-Term Ecological Research (LTER) network, ranging from tropical rainforest to taiga. We focus on cross-species synchrony and i) quantify synchrony in reproduction overall and within LTER sites, ii) test for relationships between synchrony with trait and phylogenetic similarity, and iii) investigate how climate conditions are related to levels of synchrony. Overall, synchrony in reproduction between woody plant species was greater than expected by chance, but also spanned a wide range of values between species. Based on 11 traits for 103 species (plus phylogenetic relatedness), cross-species synchrony in reproduction was driven primarily by trait similarity with phylogeny being largely unimportant, and synchrony was higher in sites with greater climatic water deficit. Synchronous masting within a community has consequences for understanding forest regeneration dynamics and consumer-resource interactions.","language":"English","publisher":"Wiley","doi":"10.1111/ele.14498","usgsCitation":"LaMontagne, J.M., Greene, D.F., Holland, E., Johnstone, J.F., Schulze, M., Zimmerman, J.K., Lyon, N.J., Chen, A., Miller, T.E., Nigro, K.M., Snell Rebecca S., Barton, J.H., Chaudhary, V., Cleavitt, N.L., Crone, E., Koenig, W.D., Macias, D., Pearse, I.S., and Redmond, M.D., 2024, Community synchrony in seed production is associated with trait similarity and climate across North America: Ecology Letters, v. 27, no. 12, e14498, 15 p., https://doi.org/10.1111/ele.14498.","productDescription":"e14498, 15 p.","ipdsId":"IP-162505","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":501361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.14498","text":"Publisher Index Page"},{"id":501146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -174.57469846241375,\n 59.07732223048754\n ],\n [\n -132.94118629888828,\n 46.634323740727496\n ],\n [\n -102.87615155066331,\n 14.159655778644463\n ],\n [\n -86.60507271467972,\n 18.528872570028568\n ],\n [\n -76.70651169792454,\n 26.155075649047504\n ],\n [\n -55.08810680677202,\n 45.22442345962252\n ],\n [\n -93.39098226670879,\n 53.00878959626715\n ],\n [\n -134.4869880449841,\n 65.05021822139254\n ],\n [\n -154.62493341933438,\n 66.11219190375385\n ],\n [\n -174.57469846241375,\n 59.07732223048754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"LaMontagne, Jalene M.","contributorId":223096,"corporation":false,"usgs":false,"family":"LaMontagne","given":"Jalene","middleInitial":"M.","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":957043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greene, David F.","contributorId":302454,"corporation":false,"usgs":false,"family":"Greene","given":"David","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":957044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holland, E. 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Field work and monitoring took place during 2012–18. The area is underlain by sedimentary formations that form a fractured-rock aquifer used for drinking water and industrial supply. The EPA placed the Site on the National Priorities List in 1989, identifying tetrachloroethylene (PCE) and trichloroethylene (TCE) as contaminants of concern.
During 2012–18, the USGS conducted field activities that included drilling an 82-foot (ft)-deep monitoring well (MG 2220) in 2016, reconstructing a 208-ft-deep former industrial production well (MG 668 [Granite Knitting Mill]), and collecting borehole geophysical and video logs and water levels from those and five additional wells, which ranged in depth from about 50 to 200 ft below land surface. Continuous water levels were collected during 2014–17, and a synoptic set of water levels were measured in April 2018 in the seven wells.
The borehole geophysical logs (caliper, acoustic televiewer, natural gamma, single-point resistance, vertical flow, and fluid temperature and resistivity) and borehole video logs in the seven wells were evaluated to assess potential for lithologic correlation and to identify and describe water-bearing features, which included both low- and high-angle fractures and other openings oriented along dipping bedding planes, joints, or possible faults. Borehole geophysical logs collected by USGS in 1992 in a 300-ft-deep former production well near the Site were also evaluated. Few to no distinctive features were identified on geophysical logs (natural gamma and single-point resistance) that could be used for correlation, thus limiting this approach to determining local geologic structure. Extensive fracturing in the upper 62 ft of monitoring well MG 2220 indicates that the well was likely drilled through a zone of faulting, and other evidence of faulting is present in the area near the Site. Assessment of continuous water levels showed hydraulic connections among some wells as indicated by rising or falling water levels in response to changes in pumping rates at nearby wells. A map of water levels measured in April 2018 indicates potential for groundwater flow generally toward the stream to the south and southwest of the Site, but the limited water-level data are insufficient to describe vertical groundwater gradients or lateral gradients in any detail.
Review of 1999–2022 volatile organic compound (VOC) monitoring data collected by the Pennsylvania Department of Environmental Protection for five monitoring wells indicates that the highest groundwater concentrations of PCE and TCE were found in samples from extraction well MG 2201 (S-1) downgradient from, and nearest to, the previously identified Site contaminant source area, and these concentrations fluctuated through time. PCE concentrations were higher than TCE concentrations in samples from all five monitoring wells and were much higher than TCE concentrations in samples from extraction well MG 2201 (S-1). Temporally variable recharge is a possible factor affecting observed fluctuations in PCE concentrations in groundwater samples from well extraction MG 2201 (S-1), as indicated by a general inverse relation between PCE concentrations and water levels in a nearby long-term observation well. The PCE concentration of 1,830 micrograms per liter (μg/L) in a May 2018 water sample from monitoring well MG 2220 was more than four times the PCE concentration of 444 μg/L in a December 2017 sample from the nearby extraction well MG 2201 (S-1), which is open to fewer fractures. Low concentrations of VOCs were measured in surface water at two stream sites downgradient from wells with the highest groundwater VOC concentrations at the Site, indicating that discharge of contaminated groundwater to the stream is likely.
Development of a conceptual model of the groundwater system was constrained by limited data. In areas with no pumping, groundwater-flow directions generally are thought to be controlled by topography and geologic structure (bedding orientation) and likely to the south and southwest of the Site, with local flow directions affected by orientations of fractures, joints, and local faults. Additional investigations that could help improve the conceptual model of the groundwater system and help delineate the extent of groundwater contamination and its transport are discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241080","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Risser, D.W., Goode, D.J., and Bird, P.H., 2024, Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania: U.S. Geological Survey Open-File Report 2024–1080, 78 p., https://doi.org/10.3133/ofr20241080.","productDescription":"xi, 78 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151018","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":494216,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118273.htm","linkFileType":{"id":5,"text":"html"}},{"id":465486,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1080 XML"},{"id":465485,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241080/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1080 HTML"},{"id":465479,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1080/images/"},{"id":465476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.pdf","text":"Report","size":"18.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1080 PDF"},{"id":465475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1080/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","city":"Souderton","otherGeospatial":"North Penn Area 1 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.33380565402877,\n 40.30337215850042\n ],\n [\n -75.33067431094733,\n 40.30297414782885\n ],\n [\n -75.32310689850118,\n 40.30864557850933\n ],\n [\n -75.32121504538941,\n 40.31133187946756\n ],\n [\n -75.32415067952832,\n 40.31496319053656\n ],\n [\n -75.33002194780529,\n 40.3133714069823\n ],\n [\n -75.33432754454195,\n 40.307053646040714\n ],\n [\n -75.33380565402877,\n 40.30337215850042\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
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New geologic mapping (Wells and others, 2020b) and geophysical mapping (Blakely and others, 2000; McPhee and others, 2014; Wells and others, 2020a) document kilometers of Cenozoic right-lateral offset along the Gales Creek Fault Zone, a major, northwest-striking fault zone forming the boundary between the Tualatin Valley and the Coast Range. The Bureau of Reclamation’s (Reclamation) Scoggins Dam (fig. 1), in the Coast Range foothills west of Forest Grove, Oregon, lies within the Gales Creek Fault Zone as mapped by Wells and others (2020a, 2020b; fig. 2).
Active faults of the Gales Creek Fault Zone defined by paleoseismic trenching (Redwine and others, 2017, 2019b, Horst and others, 2018, 2019, 2021, and Wells and others, 2020a) are presently mapped as projecting through the existing dam. The Pacific Northwest Region of Reclamation requested assistance with geologic studies around Scoggins Dam to provide better understanding of fault locations and their activity, which are needed to design a modification of the dam (Maguire, 2019a, b). The scope of this project includes detailed geology of the existing Scoggins Dam site, Henry Hagg Lake, the reservoir behind the dam, and Scoggins Valley downstream of the existing dam, particularly around a potential new dam site, where Scoggins Creek cuts through a narrow gap formed by a resistant felsic tuff bed that crosses the valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3528","collaboration":"Bureau of Reclamation, Clean Water Services","usgsCitation":"Wells, R.E., Bennett, S.E.K., Redwine, J.R., Staisch, L.M., Holm-Denoma, C.S., and Mahan, S.A., 2024, Geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon: U.S. Geological Survey Scientific Investigations Map 3528, 4 sheets, scales 1:2,000 and, 1:12,000, 37 p. pamphlet, https://doi.org/10.3133/sim3528.","productDescription":"Pamphlet: x, 37 p.; 4 Sheets: 43.91 x 34.01 inches or smaller; 3 Data Releases","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-121928","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":494215,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118270.htm","linkFileType":{"id":5,"text":"html"}},{"id":465359,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ZSKV3","text":"USGS Data Release","description":"Pianowski, L.S., Holm-Denoma, C.S., Staisch, L.M., and Wells, R.E., 2023, U-Pb zircon data for Cenozoic clastic and volcaniclastic units deformed along the Gales Creek Fault Zone, northwestern Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P91ZSKV3.","linkHelpText":"U-Pb zircon data for Cenozoic clastic and volcaniclastic units deformed along the Gales Creek Fault Zone, northwestern Oregon"},{"id":465358,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PVK0Q3","text":"USGS Data Release","description":"Mahan, S.A., Krolczyk, E.T., and Redwine, J., 2022, Data Release for Luminescence; Geologic map of Scoggins Dam, Henry Hagg Lake, and the Scoggins Valley area, Washington County, Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P9PVK0Q3.","linkHelpText":"Data Release for Luminescence; Geologic map of Scoggins Dam, Henry Hagg Lake, and the Scoggins Valley area, Washington County, Oregon"},{"id":465357,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M5300X","text":"USGS Data Release","description":"Wells, R.E., Bennett, S.E.K., Redwine, J.R., Staisch, L.M., Holm-Denoma, C.S., and Mahan, S.A.,2024, Digital data for the geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P9M5300X.","linkHelpText":"Digital data for the geologic map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon"},{"id":465356,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_pamphlet.pdf","text":"Pamphlet","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465355,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet4.pdf","text":"Sheet 4","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Cross Sections of the Option 3 Dam Site"},{"id":465354,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet3.pdf","text":"Sheet 3","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of the Option 3 Dam Site"},{"id":465353,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet2.pdf","text":"Sheet 2","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of Scoggins Dam"},{"id":465352,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3528/sim3528_sheet1.pdf","text":"Sheet 1","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of Scoggins Dam, Henry Hagg Lake, and Scoggins Valley, Washington County, Oregon"},{"id":465351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3528/covrthb.jpg"}],"country":"United States","state":"Oregon","county":"Washington County","otherGeospatial":"Henry Hagg Lake, Scoggins Dam, Scoggins Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.30887944247891,\n 45.55096796789627\n ],\n [\n -123.30887944247891,\n 45.44121330051283\n ],\n [\n -123.1570053249566,\n 45.44121330051283\n ],\n [\n -123.1570053249566,\n 45.55096796789627\n ],\n [\n -123.30887944247891,\n 45.55096796789627\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
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Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine (Pinus albicaulis) trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP). Here, we used this subfossil wood record to develop tree-ring-based temperature estimates for the upper-elevation climate conditions that resulted in ancient forest establishment and growth and the subsequent regional ice-patch growth and downslope shift of treeline. Results suggest that mid-Holocene forest establishment and growth occurred under warm-season (May-Oct) mean temperatures of 6.2 °C (±0.2 °C), until a multicentury cooling anomaly suppressed temperatures below 5.8 °C, resulting in stand mortality by c. 5,440 y BP. Transient climate model simulations indicate that regional cooling was driven by changes in summer insolation and Northern Hemisphere volcanism. The initial cooling event was followed centuries later (c. 5,100 y BP) by sustained Icelandic volcanic eruptions that forced a centennial-scale 1.0 °C summer cooling anomaly and led to rapid ice-patch growth and preservation of the trees. With recent warming (c. 2000–2020 CE), warm-season temperatures now equal and will soon exceed those of the mid-Holocene period of high treeline. It is likely that perennial ice cover will again disappear from the region, and treeline may expand upslope so long as plant-available moisture and disturbance are not limiting.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2412162121","usgsCitation":"Pederson, G.T., Stahle, D.K., McWethy, D.B., Toohey, M., Jungclaus, J., Lee, C., Martin, J.T., Alt, M., Kichas, N.E., Chellman, N.J., McConnell, J.R., and Whitlock, C., 2024, Dynamic treeline and cryosphere response to pronounced mid-Holocene climatic variability in the US Rocky Mountains: Proceedings of the National Academy of Sciences, v. 122, e2412162121, 11 p., https://doi.org/10.1073/pnas.2412162121.","productDescription":"e2412162121, 11 p.","ipdsId":"IP-163460","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466697,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2412162121","text":"Publisher Index Page"},{"id":465611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.40405049321484,\n 45.200185933759144\n ],\n [\n -110.40405049321484,\n 44.77582421595028\n ],\n [\n -109.75503610328678,\n 44.77582421595028\n ],\n [\n -109.75503610328678,\n 45.200185933759144\n ],\n [\n -110.40405049321484,\n 45.200185933759144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"122","noUsgsAuthors":false,"publicationDate":"2024-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science 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Oregon","active":true,"usgs":false}],"preferred":false,"id":922193,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70263186,"text":"70263186 - 2024 - The MIEM guidelines: Minimum information for reporting of environmental metabarcoding data","interactions":[],"lastModifiedDate":"2025-01-31T15:26:41.141271","indexId":"70263186","displayToPublicDate":"2024-12-30T09:18:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8122,"text":"Metabarcoding and Metagenomics","active":true,"publicationSubtype":{"id":10}},"title":"The MIEM guidelines: Minimum information for reporting of environmental metabarcoding data","docAbstract":"Environmental DNA (eDNA) and RNA (eRNA) metabarcoding has become a popular tool for assessing biodiversity from environmental samples, but inconsistent documentation of methods, data and metadata makes results difficult to reproduce and 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A working group of scientists have collaborated to produce a set of minimum reporting guidelines for the constituent steps of metabarcoding workflows, from the physical layout of laboratories through to data archiving. We emphasise how reporting the suite of data and metadata should adhere to findable, accessible, interoperable and reproducible (FAIR) data standards, thereby providing context for evaluating and understanding study results. An overview of the documentation considerations for each workflow step is presented and then summarised in a checklist that can accompany a published study or report. Ensuring workflows are transparent and documented is critical to reproducible research and should allow for more efficient uptake of metabarcoding data into management decision-making.
","language":"English","publisher":"Pensoft Publishers","doi":"10.3897/mbmg.8.128689","usgsCitation":"Klymus, K.E., Baker, J., Abbott, C., Brown, R., Craine, J.M., Gold, Z., Hunter, M., Johnson, M., Jones-Slobodian, D.N., Jungbluth, M., Jungbluth, S., Lor, Y., Maloy, A., Merkes, C.M., Noble, R.T., Patin, N., Sepulveda, A., Spear, S.F., Steele, J., Takahashi, M., Watts, A.W., and Theroux, S., 2024, The MIEM guidelines: Minimum information for reporting of environmental metabarcoding data: Metabarcoding and Metagenomics, v. 8, e128689, 30 p., https://doi.org/10.3897/mbmg.8.128689.","productDescription":"e128689, 30 p.","ipdsId":"IP-166699","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research 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Limited studies explore the impacts of wildfires on aquatic taxa and few focus on lentic habitats that are essential for amphibians, many of which are of conservation concern. We capitalized on existing pre-fire surveys for anuran species and resurveyed a random subset of wetlands across a gradient of soil burn severity to investigate the short-term effects of wildfire on a relict population of wood frogs in the southern Rocky Mountains. We also investigated whether maps created to support rapid post-fire emergency response activities (i.e., United States Forest Service Burned Area Emergency Response program) accurately characterize soil burn severity around small habitat features (i.e., ponds) that serve as important amphibian breeding and rearing habitat. Soil burn severity reflects fire impacts on soil and surface organic layers, including vegetation loss and changes in soil structure and function. We found that wood frog (Lithobates sylvaticus) breeding persistence following fires was negatively influenced by the percentage of their terrestrial habitat (100 m buffer surrounding breeding ponds) that was burned. Wood frog colonization probability of previously unoccupied ponds was low (∼ 0.10) and unaffected by soil burn severity. Importantly, we found that remotely sensed data typically produced to predict flooding and erosion at broad (catchment) scales is a poor representation of the amount and variation in soil burn severity surrounding small habitat features, suggesting that additional field sampling is necessary to understand wildfire responses for species that rely on these small habitat features. Understanding short-term geographic- and species-specific variation in response to wildfires provides the basis to explore time to recovery (e.g., when wood frogs return to burned breeding sites) or to determine if declines in breeding distributions intensify over time.
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As requested by the U.S. Fish and Wildlife Service, we used aerial photography and satellite remote sensing data to map surface water and other land cover types within the wetland complexes. We identified five land cover classes using the green normalized difference vegetation index (gNDVI) and its inverse relationship to the normalized difference water index (NDWI) within three U.S. Department of Agriculture National Agriculture Imagery Program aerial images (acquired in 2015, 2017, and 2019) and 110 European Space Agency Sentinel-2 satellite images (acquired 2015–2022). The relative wetness of soil conditions within each land cover class is estimated by comparison to previously published observations of relative conductivity measured by 79 field-based sensors within the wetlands from 2019 to 2021. We mapped the areal coverage of the five land cover classes for approximately 385 acres (1,559,000 square meters [m²]) comprising six individual wetland complexes as well as a larger 1,298- acre (5,254,000-m2) area of interest inclusive of the wetland complexes and adjacent landscape. Land cover of open water (Class 5) primarily within ponds at one of the wetland complexes comprised 8,333 m2, on average, of the wetland complexes. Land cover of mixed shallow surface water, saturated soil, and vegetation (Class 4) comprised 111,723 m2 on average of the wetland complexes. Land cover of dense green vegetation canopy cover (Class 3) that often (46 percent of observations) had underlying surface water or saturated soil conditions comprised 592,522 m2 on average of the wetland complexes. The remaining areas of the wetland complexes not mapped as these three land cover types (Classes 2 and 1) had sparse vegetation or bare soil cover and commonly (greater than or equal to 67 percent of observations) had dry soil conditions. The investigation of land cover detailed in this report could inform future efforts to map land cover more precisely via higher resolution remote sensing or ground-based surveying or could be incorporated with other environmental monitoring data to characterize habitat and hydrology of the wetland complexes at Dixie Meadows.
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U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The topography of New Hampshire ranges from the Coastal Lowlands to the Eastern New England Upland to the White Mountains region. High-quality statewide elevation data are useful in managing this very diverse landscape. For example, the short coastline, including the Great Bay estuary and the Hampton-Seabrook marshes, is of disproportionately high value to New Hampshire’s tourist economy. The vulnerability of the coast to the effects of sea-level rise underscores the need for accurate, high-quality nearshore topographic elevation data and offshore bathymetric data to effectively manage the coast’s valuable resources, which include important fisheries, habitat, and infrastructure. Another important use for accurate elevation data in New Hampshire is in the evaluation of flood hazards and their potential environmental and infrastructure effects. This evaluation includes mapping of inundation and sediment transport, and assessing the associated costs of flooding. Addressing this challenge requires detailed knowledge of both surface topography and inland bathymetry. Other important activities having a substantial economic element and needing accurate elevation data include geologic resource assessment and hazard mitigation, urban and regional planning, infrastructure and construction management, and cultural resources preservation and management. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional model of the Earth’s surface and aboveground features.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Prevention and early detection of invasive species are championed as the most cost-effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula, is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes (Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine-assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult-to-detect invasive species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70113","usgsCitation":"Fuller, A.K., Augustine, B., Clifton, E., Hajek, A., Blumenthal, A., Beese, J., Hurt, A., and Brown-Lima, C., 2024, Effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly: Ecological Applications, v. 15, no. 12, e70113, 22 p., https://doi.org/10.1002/ecs2.70113.","productDescription":"e70113, 22 p.","ipdsId":"IP-161109","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466698,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70113","text":"Publisher Index Page"},{"id":466010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, 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H.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hajek, Ann E.","contributorId":347875,"corporation":false,"usgs":false,"family":"Hajek","given":"Ann E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922718,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blumenthal, Arden","contributorId":347878,"corporation":false,"usgs":false,"family":"Blumenthal","given":"Arden","affiliations":[{"id":83267,"text":"New York-New Jersey Trail Conference","active":true,"usgs":false}],"preferred":false,"id":922719,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beese, Josh","contributorId":347880,"corporation":false,"usgs":false,"family":"Beese","given":"Josh","affiliations":[{"id":83267,"text":"New York-New Jersey Trail Conference","active":true,"usgs":false}],"preferred":false,"id":922720,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurt, Aimee","contributorId":347881,"corporation":false,"usgs":false,"family":"Hurt","given":"Aimee","affiliations":[{"id":40629,"text":"Working Dogs for Conservation","active":true,"usgs":false}],"preferred":false,"id":922721,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown-Lima, Carrie J.","contributorId":347882,"corporation":false,"usgs":false,"family":"Brown-Lima","given":"Carrie J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922722,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261925,"text":"70261925 - 2024 - Nitrogen deposition weakens soil carbon control of nitrogen dynamics across the contiguous United States","interactions":[],"lastModifiedDate":"2025-01-03T15:17:09.57762","indexId":"70261925","displayToPublicDate":"2024-12-26T09:08:36","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen deposition weakens soil carbon control of nitrogen dynamics across the contiguous United States","docAbstract":"Anthropogenic nitrogen (N) deposition is unequally distributed across space and time, with inputs to terrestrial ecosystems impacted by industry regulations and variations in human activity. Soil carbon (C) content normally controls the fraction of mineralized N that is nitrified (ƒnitrified), affecting N bioavailability for plants and microbes. However, it is unknown whether N deposition has modified the relationships among soil C, net N mineralization, and net nitrification. To test whether N deposition alters the relationship between soil C and net N transformations, we collected soils from coniferous and deciduous forests, grasslands, and residential yards in 14 regions across the contiguous United States that vary in N deposition rates. We quantified rates of net nitrification and N mineralization, soil chemistry (soil C, N, and pH), and microbial biomass and function (as beta-glucosidase (BG) and N-acetylglucosaminidase (NAG) activity) across these regions. Following expectations, soil C was a driver of ƒnitrified across regions, whereby increasing soil C resulted in a decline in net nitrification and ƒnitrified. The ƒnitrified value increased with lower microbial enzymatic investment in N acquisition (increasing BG:NAG ratio) and lower active microbial biomass, providing some evidence that heterotrophic microbial N demand controls the ammonium pool for nitrifiers. However, higher total N deposition increased ƒnitrified, including for high soil C sites predicted to have low ƒnitrified, which decreased the role of soil C as a predictor of ƒnitrified. Notably, the drop in contemporary atmospheric N deposition rates during the 2020 COVID-19 pandemic did not weaken the effect of N deposition on relationships between soil C and ƒnitrified. Our results suggest that N deposition can disrupt the relationship between soil C and net N transformations, with this change potentially explained by weaker microbial competition for N. Therefore, past N inputs and soil C should be used together to predict N dynamics across terrestrial ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.70016","usgsCitation":"Nieland, M., Lacy, P., Allison, S.D., Bhatnagar, J., Doroski, D., Frey, S.D., Greaney, K., Hobbie, S.E., Kuebbing. 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The volcano has been monitored by a network of permanent, short period and broadband seismometers. I used the continuous waveform data from that network starting in 2012 to generate a catalog of seismicity that enhances the US Geological Survey Hawaiian Volcano Observatory’s public seismic catalog with four times the number of earthquakes, which were then grouped by waveform similarity. Analysis of subtle delays in the timing of arrivals of scattered waves between pairs of earthquakes in this catalog yields a history of small changes in the shallow seismic velocity structure of the volcano. Seismic velocities have been shown at other volcanoes to change during unrest and eruption. My results show a decrease in seismic velocity centered on the summit beginning in September 2022, corresponding to the onset of a vigorous precursory swarm of seismic activity and shallow inflation. During the eruption itself, I observe large changes due likely to dike opening along the northeast rift zone and deflation of the summit reservoir. However, seismic velocity changes associated with non-volcanic sources such as ground shaking from large earthquakes and meteorological influences at seasonal and diurnal time scales are also observed, and these dominate the velocity changes prior to the eruption. Proper accounting of these effects will be a requirement for use in real-time monitoring, and this work serves as a starting point in that endeavor for Mauna Loa.","language":"English","publisher":"Springer Nature","doi":"10.1007/s00445-024-01793-x","usgsCitation":"Hotovec-Ellis, A.J., 2024, Seismic velocity changes from repetitive seismicity at Mauna Loa prior to and during its 2022 eruption: Bulletin of Volcanology, v. 87, 9, 18 p., https://doi.org/10.1007/s00445-024-01793-x.","productDescription":"9, 18 p.","ipdsId":"IP-167796","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466699,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-024-01793-x","text":"Publisher Index Page"},{"id":465750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.6256226783436,\n 19.49598540207714\n ],\n [\n -155.6256226783436,\n 19.445066390638345\n ],\n [\n -155.56470750742835,\n 19.445066390638345\n ],\n [\n -155.56470750742835,\n 19.49598540207714\n ],\n [\n -155.6256226783436,\n 19.49598540207714\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","noUsgsAuthors":false,"publicationDate":"2024-12-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":922542,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}