Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable. Critically, FAIR aims to make science more transparent and transferable by both people and computers. However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today’s status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2025.101705","usgsCitation":"Bagstad, K.J., Balbi, S., Adamo, G., Athanasiadis, I., Affinito, F., Willcock, S., Magrach, A., Hayashi, K., Harmackova, Z., Niamir, A., Smets, B., Buchhorn, M., Drakou, E., Alfieri, A., Edens, B., Gonzalez Morales, L., Vari, A., Sanz, M., and Villa, F., 2025, Interoperability for ecosystem service assessments: Why, how, who, and for whom?: Ecosystem Services, v. 72, 101705, 19 p., https://doi.org/10.1016/j.ecoser.2025.101705.","productDescription":"101705, 19 p.","ipdsId":"IP-165917","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":489979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2025.101705","text":"Publisher Index 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US-invaded range models outperform global or combined range models after 100 occurrences","interactions":[],"lastModifiedDate":"2025-04-08T15:00:25.716606","indexId":"70265485","displayToPublicDate":"2025-03-04T07:53:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"How to model a new invader? US-invaded range models outperform global or combined range models after 100 occurrences","docAbstract":"Invasive species are an economic and ecological burden, and efforts to limit their impact are greatly improved with reliable maps based on species distribution models (SDMs). However, the potential distribution of new invaders is difficult to anticipate because they are still spreading with few observations in their invaded habitat. Therefore, an accepted practice in predicting the distribution of invasive species has been to incorporate habitat information from its entire geographic distribution (invaded and native ranges) into SDMs. Yet, this approach, due to niche shifts, niche expansions, and data deficiencies, commonly misrepresents where an invasive species is found in its new range. Here, we use time series records (invasion stages) from 13 invasive plant species in North America to explore the tension between modeling invasive species using global range and invaded range data and to determine if there is a “tipping point” at which one SDM strategy performs better than the other in predicting the ultimate distribution. At the earliest invasion stage, models developed using both invaded range and global occurrences on average performed better and had less variability across species than other model strategies at this stage. However, after as few as 100 observations of an invasive plant had been made, US-invaded range models, on average, outperformed global range models and models that combined occurrences. By building models with global and US-scale predictors, we show that higher performance of invaded range models was in part because of greater data quality at the invaded-range scale. Our work demonstrates that after relatively few observations of an invasive species in its invaded range, it is more accurate to model its potential distribution using only information from the invaded range while disregarding information from other regions. This work develops a robust and comprehensive approach to modeling novel distributions of newly observed invasive species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70010","usgsCitation":"Young, N.E., Williams, D.A., Shadwell, K.S., Pearse, I., and Jarnevich, C.S., 2025, How to model a new invader? 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0000-0001-6835-425X","orcid":"https://orcid.org/0000-0001-6835-425X","contributorId":332473,"corporation":false,"usgs":false,"family":"Shadwell","given":"Keana","email":"","middleInitial":"S.","affiliations":[{"id":79471,"text":"Student contractor to the U.S. Geological Survey, Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":932815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"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":932816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 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with thinned point process models","docAbstract":"Mortality risk for animals often varies spatially and can be linked to how animals use landscapes. While numerous studies collect telemetry data on animals, the focus is typically on the period when animals are alive, even though there is important information that could be gleaned about mortality risk. We introduce a thinned spatial point process (SPP) modelling framework that couples relative abundance and space use with a mortality process to formally treat the occurrence of mortality events across the landscape as a spatial process. We show how this model can be embedded in a hierarchical statistical framework and fit to telemetry data to make inferences about how spatial covariates drive both space use and mortality risk. We apply the method to two data sets to study the effects of roads and habitat on spatially explicit mortality risk: (1) VHF telemetry data collected for willow ptarmigan in Alaska, and (2) hourly GPS telemetry data collected for black bears in Colorado. These case studies demonstrate the applicability of this method for different species and data types, making it broadly useful in enabling inferences about the mechanisms influencing animal survival and spatial population processes while formally treating survival as a spatial process, especially as the development and implementation of joint analyses continue to progress.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.70092","usgsCitation":"Eisaguirre, J.M., Lohman, M., Frye, G., Johnson, H.E., Riecke, T., and Williams, P.J., 2025, Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models: Ecology Letters, v. 28, no. 3, e70092, 11 p., https://doi.org/10.1111/ele.70092.","productDescription":"e70092, 11 p.","ipdsId":"IP-160909","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":496394,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70092","text":"Publisher Index Page"},{"id":492795,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HSN8PV","text":"USGS data release","linkHelpText":"Thinned Point Process Models for Telemetry Data"},{"id":482801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":929454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lohman, Medeleine G.","contributorId":351785,"corporation":false,"usgs":false,"family":"Lohman","given":"Medeleine G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frye, Graham G.","contributorId":351786,"corporation":false,"usgs":false,"family":"Frye","given":"Graham G.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":929456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":929457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":929458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":929459,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264274,"text":"70264274 - 2025 - Dynamic and context-dependent keystone species effects in kelp forests","interactions":[],"lastModifiedDate":"2025-03-10T14:09:02.612583","indexId":"70264274","displayToPublicDate":"2025-03-03T09:05:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic and context-dependent keystone species effects in kelp forests","docAbstract":"Sea otters are an iconic keystone predator that can maintain kelp forests by preying on grazing invertebrates such as sea urchins. However, the effects of sea otters on kelp forests vary over their geographic range. Here, we analyze two 30-y datasets on kelp forest communities during the reintroduction of sea otters along the west coast of Vancouver Island, BC, Canada, and around San Nicolas Island, CA. We developed a community model to estimate species interactions as dynamic rates, varying with community state. We find evidence of a classic trophic cascade off Vancouver Island; the arrival of otters quickly led to depletion of urchins and recovery of kelp. However, this cascade was muted around San Nicolas Island, with otters, urchins, and kelp all coexisting at intermediate densities for multiple years. Our models show that this difference came from a pulse of strong otter impacts on urchins following recolonization off Vancouver Island, but not off San Nicolas Island. The mean effects of otters on urchins and urchins on kelp were not stronger in the north, indicating that interaction dynamics and not average interaction strength are key to explaining differences in community trajectories. We also find stronger multistep interaction chains in the south, arising from competitive interactions that indirectly buffered otter effects. These findings shed light on long-standing hypotheses about how interspecific interactions can alter the function of keystone species across community contexts. More broadly, we show how community change can be more accurately predicted by considering dynamic interaction strengths.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2413360122","usgsCitation":"Langendorf, R., Estes, J.A., Watson, J.C., Kenner, M.C., Hatfield, B.B., Tinker, M.T., Waddle, E., DeMarche, M., and Doak, D., 2025, Dynamic and context-dependent keystone species effects in kelp forests: Proceedings of the National Academy of Sciences, v. 122, no. 10, e2413360122, 10 p., https://doi.org/10.1073/pnas.2413360122.","productDescription":"e2413360122, 10 p.","ipdsId":"IP-168469","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":486979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2413360122","text":"Publisher Index Page"},{"id":483132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Langendorf, Ryan E.","contributorId":352190,"corporation":false,"usgs":false,"family":"Langendorf","given":"Ryan E.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Estes, James A. 0000-0002-3632-4555 jim_estes@usgs.gov","orcid":"https://orcid.org/0000-0002-3632-4555","contributorId":240955,"corporation":false,"usgs":false,"family":"Estes","given":"James","email":"jim_estes@usgs.gov","middleInitial":"A.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":930236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Jane C.","contributorId":201244,"corporation":false,"usgs":false,"family":"Watson","given":"Jane","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":930237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":930238,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatfield, Brian B. 0000-0003-1432-2660 brian_hatfield@usgs.gov","orcid":"https://orcid.org/0000-0003-1432-2660","contributorId":147917,"corporation":false,"usgs":true,"family":"Hatfield","given":"Brian","email":"brian_hatfield@usgs.gov","middleInitial":"B.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":930239,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tinker, M. T. 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":54152,"corporation":false,"usgs":false,"family":"Tinker","given":"M.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":930240,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waddle, Ellen","contributorId":352192,"corporation":false,"usgs":false,"family":"Waddle","given":"Ellen","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930241,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeMarche, Megan L.","contributorId":352195,"corporation":false,"usgs":false,"family":"DeMarche","given":"Megan L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":930242,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Doak, Daniel F.","contributorId":352197,"corporation":false,"usgs":false,"family":"Doak","given":"Daniel F.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930243,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70264152,"text":"70264152 - 2025 - METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2025-03-07T15:06:33.51307","indexId":"70264152","displayToPublicDate":"2025-03-03T09:03:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed","docAbstract":"Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes (i.e., agreement between the expected and monitored trends) across the Chesapeake Bay watershed.
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USA","interactions":[],"lastModifiedDate":"2025-02-03T15:39:22.816973","indexId":"70263224","displayToPublicDate":"2025-03-03T08:35:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA","docAbstract":"Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2024.104412","usgsCitation":"Dillon, J.W., Peitzsch, E.H., Miller, Z., Bartelt, P., and Hammonds, K.D., 2025, Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA: Cold Regions Science and Technology, v. 231, 104412, 10 p., https://doi.org/10.1016/j.coldregions.2024.104412.","productDescription":"104412, 10 p.","ipdsId":"IP-167691","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":489926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coldregions.2024.104412","text":"Publisher Index Page"},{"id":481608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0973168994086,\n 48.755499897415916\n ],\n [\n -114.0973168994086,\n 48.536591733185475\n ],\n [\n -113.50134190625533,\n 48.536591733185475\n ],\n [\n -113.50134190625533,\n 48.755499897415916\n ],\n [\n -114.0973168994086,\n 48.755499897415916\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"231","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, James W.","contributorId":330951,"corporation":false,"usgs":false,"family":"Dillon","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":925961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":925962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":925963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartelt, Perry","contributorId":336797,"corporation":false,"usgs":false,"family":"Bartelt","given":"Perry","affiliations":[{"id":80867,"text":"SLF","active":true,"usgs":false}],"preferred":false,"id":925964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammonds, Kevin D.","contributorId":330952,"corporation":false,"usgs":false,"family":"Hammonds","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":925965,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266033,"text":"70266033 - 2025 - Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance","interactions":[],"lastModifiedDate":"2025-04-24T15:34:31.554972","indexId":"70266033","displayToPublicDate":"2025-03-03T08:28:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance","docAbstract":"Copper (Cu) stamp mill mining in North America from the early 1900s produced a pulverized ore by-product now known as stamp sands (SS). In a mining operation near the city of Gay (Michigan, USA), SS were originally deposited near a Lake Superior beach, but erosion and wave action have moved many SS into beaches and reefs that are critical spawning and nursery areas for native fish (e.g., Lake Whitefish). Larval and juvenile native fish consume zooplankton and benthic invertebrates during their development, and many of these invertebrate taxa may be sensitive to metal contamination from the SS. Here, we sampled the invertebrate community from beaches with high SS, moderate SS and low SS, as well as a control beach 58 km from the source of the SS. The high SS site was characterized by fewer benthic taxa, and less density of several taxa than the low SS site, especially benthic copepods. All beaches had comparable zooplankton diversity, but the abundance was ~ 2 orders of magnitude lower at the high SS site. Cu and several other metals were elevated at beaches with more SS. We found support for associations between benthic density and diversity with depth (positive effect) and Cu concentration (negative effect). Cu concentration was a better predictor of declines in benthic invertebrate abundance and diversity than SS although sensitivity to Cu varied among taxa. We also observed that the relationship between Cu concentration and SS was non-linear, and highly variable. For example, 149 mg Cu/kg dry weight sediment is a consensus threshold used in the literature to identify Cu toxicity, but the prediction interval for estimating that concentration of Cu from measurements of SS is 26-851 mg Cu/kg dry weight. A better predictive model of this relationship would be beneficial to develop an understanding of what level of SS reduction would prevent Cu impacts on invertebrates.","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0318980","usgsCitation":"Larson, J.H., Lowe, M.R., Bailey, S., Bell, A.H., and Cleveland, D.M., 2025, Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance: PLoS ONE, v. 20, no. 3, e0318980, 27 p., https://doi.org/10.1371/journal.pone.0318980.","productDescription":"e0318980, 27 p.","ipdsId":"IP-159260","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487903,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0318980","text":"Publisher Index Page"},{"id":484986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Keweenaw Bay, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.51454370384035,\n 46.89135600920267\n ],\n [\n -88.51454370384035,\n 46.7436589844292\n ],\n [\n -88.37255101673634,\n 46.7436589844292\n ],\n [\n -88.37255101673634,\n 46.89135600920267\n ],\n [\n -88.51454370384035,\n 46.89135600920267\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":934422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":934423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":934424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bell, Amanda H. 0000-0002-7199-2145 ahbell@usgs.gov","orcid":"https://orcid.org/0000-0002-7199-2145","contributorId":1752,"corporation":false,"usgs":true,"family":"Bell","given":"Amanda","email":"ahbell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":934426,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263177,"text":"70263177 - 2025 - Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","interactions":[],"lastModifiedDate":"2025-01-31T15:18:02.303715","indexId":"70263177","displayToPublicDate":"2025-03-03T08:10:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","docAbstract":"Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2025.109308","usgsCitation":"Ji, L., Senay, G.B., Friedrichs, M., and Kagone, S., 2025, Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches: Agricultural Water Management, v. 309, 109308, 17 p., https://doi.org/10.1016/j.agwat.2025.109308.","productDescription":"109308, 17 p.","ipdsId":"IP-163904","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2025.109308","text":"Publisher Index Page"},{"id":481548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"High Plains","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.057698,44.997431],[-104.043814,45.868385],[-103.668479,45.945242],[-96.571871,45.871846],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.41932,40.048442],[-94.916918,39.836138],[-95.113077,39.559133],[-94.615834,39.160003],[-94.617919,36.499414],[-94.431822,35.397652],[-94.485528,33.663388],[-94.386086,33.544923],[-94.070395,33.574561],[-94.0427,32.056012],[-93.523248,31.037842],[-93.765822,30.333318],[-93.702436,30.112721],[-93.922744,29.818808],[-93.852868,29.675885],[-94.731047,29.369141],[-94.532348,29.5178],[-94.767246,29.525523],[-94.724616,29.774766],[-94.965963,29.70033],[-94.894234,29.338],[-95.16525,29.113566],[-94.73132,29.338066],[-94.803695,29.279237],[-96.341617,28.417334],[-95.983106,28.641942],[-96.221784,28.580364],[-96.287942,28.683164],[-96.473694,28.57324],[-96.664534,28.696904],[-96.481836,28.407844],[-96.790235,28.383926],[-96.898123,28.152881],[-97.21535,28.076575],[-97.040618,28.028708],[-97.183455,27.833231],[-97.354614,27.849572],[-97.296598,27.613947],[-97.399398,27.344735],[-97.640111,27.270943],[-97.485149,27.250841],[-97.552325,26.867633],[-97.145567,25.971132],[-97.36542,25.849826],[-99.110855,26.426278],[-99.452316,27.062669],[-99.556812,27.614336],[-99.841708,27.766464],[-100.280518,28.267969],[-100.785521,29.228137],[-101.441059,29.753451],[-102.341033,29.869305],[-102.698347,29.695591],[-102.944911,29.18882],[-103.227801,28.991532],[-104.46652,29.609296],[-104.924796,30.604832],[-106.158218,31.438885],[-106.381039,31.73211],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.050076,41.000659],[-111.046723,40.997959],[-111.055199,45.001321],[-104.057698,44.997431]]],[[[-97.240849,26.411504],[-97.383531,26.875521],[-97.366771,27.333276],[-96.946988,28.026522],[-96.403206,28.371475],[-96.929053,27.99044],[-97.276091,27.472145],[-97.370731,26.909706],[-97.161471,26.088705],[-97.240849,26.411504]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"309","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":925791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":925792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":925793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":199091,"corporation":false,"usgs":false,"family":"Kagone","given":"Stefanie","affiliations":[],"preferred":false,"id":925794,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264422,"text":"70264422 - 2025 - Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin","interactions":[],"lastModifiedDate":"2025-03-14T14:59:53.935548","indexId":"70264422","displayToPublicDate":"2025-03-03T07:53:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin","docAbstract":"As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70011","usgsCitation":"Kemper, J.T., Underwood, K.L., Hamshaw, S.D., Davis, D., Siemion, J., Shanley, J.B., and Schroth, A.W., 2025, Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin: Journal of the American Water Resources Association (JAWRA), v. 61, no. 2, e70011, 20 p., https://doi.org/10.1111/1752-1688.70011.","productDescription":"e70011, 20 p.","ipdsId":"IP-165362","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.70011","text":"Publisher Index Page"},{"id":483342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Catskill Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.54611692479963,\n 42.29523893577249\n ],\n [\n -74.54611692479963,\n 42.01259179761368\n ],\n [\n -74.01248762206035,\n 42.01259179761368\n ],\n [\n -74.01248762206035,\n 42.29523893577249\n ],\n [\n -74.54611692479963,\n 42.29523893577249\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Kemper, John T.","contributorId":270040,"corporation":false,"usgs":false,"family":"Kemper","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":930705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Kristen L. 0000-0003-3008-3057","orcid":"https://orcid.org/0000-0003-3008-3057","contributorId":340200,"corporation":false,"usgs":false,"family":"Underwood","given":"Kristen","email":"","middleInitial":"L.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":930706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - 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2025 - Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023)","interactions":[],"lastModifiedDate":"2025-04-22T14:54:19.271208","indexId":"70265965","displayToPublicDate":"2025-03-01T09:47:53","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023)","docAbstract":"Understanding temporal variations in a geothermal field can support operators in decision making that pertains to optimizing production and mitigating hazards. Between 2021 and 2023, The Geysers geothermal field in northern California was monitored with an array of continuous passive seismic sensors and annual repeat magnetotelluric (MT) measurements. Each of these data sets were analyzed and modelled separately to understand the data, sensitivity, and any observable changes. Then, the data were inverted jointly using a crossgradient method to further constrain temporal changes in geophysical properties within the geothermal field. Multiple permutations of annual datasets were used as inputs to the joint inversion. Results demonstrate seismic data constrain smooth inversion of the MT data, and the MT data provide supplementary information about the location of temporal fluid changes. Estimating relative changes in steam saturation for various time intervals of the joint models shows compartmentalized changes in the field, and good spatial correlation with the location of injection wells. These results demonstrate that collecting both passive seismic and MT measurements then modeling them jointly provide complementary information and a relatively inexpensive method for monitoring temporal changes in an active geothermal field that provides results to support operators.
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We have compiled a gravity database, consisting of new and re-processed existing data, from which we have constructed a new isostatic residual gravity anomaly map of the Salton trough. We have used this new gravity dataset together with a compilation of publicly available borehole data to develop new depth to basement inversion models for the region. These depth to basement models help to constrain basin geometries, inform alteration mapping, and reveal variations in basement rocks. Due to the concealed nature of the complex tectonic framework at the Salton trough, it is necessary to utilize geophysical methods for subsurface characterization. These new depth to basement models are a first step toward constructing 2D and 3D geophysical and geologic models of the Imperial Valley and Salton Sea geothermal area. This analysis complements other geophysical initiatives, including magnetotelluric (MT) modeling (Tokmakoff et al., 2024), magnetic mapping (Glen and Earney, 2023, 2024) and potential field modeling, and seismic studies focused on hazard and resource investigations in the Imperial Valley.
","conferenceTitle":"50th Stanford Geothermal Workshop","conferenceDate":"February 12, 2025","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Anderson, J.E., Glen, J.M., Schermerhorn, W.D., Earney, T.E., and Morbeck, B., 2025, Preliminary depth to basement modeling at Salton Sea, California, 50th Stanford Geothermal Workshop, Stanford, CA, February 12, 2025, 9 p.","productDescription":"9 p.","ipdsId":"IP-175199","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":483761,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=37957","linkFileType":{"id":5,"text":"html"}},{"id":483774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.17095889118062,\n 33.553526286643006\n ],\n [\n -116.17095889118062,\n 32.69509941552114\n ],\n [\n -115.1118650865392,\n 32.69509941552114\n ],\n [\n -115.1118650865392,\n 33.553526286643006\n ],\n [\n -116.17095889118062,\n 33.553526286643006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","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":931830,"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":931831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931832,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":931833,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morbeck, Benjamin Lyter 0009-0000-6043-0481","orcid":"https://orcid.org/0009-0000-6043-0481","contributorId":335638,"corporation":false,"usgs":true,"family":"Morbeck","given":"Benjamin Lyter","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931834,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264813,"text":"70264813 - 2025 - Natural capital accounting on forested lands: An application to the Colorado River basin","interactions":[],"lastModifiedDate":"2025-03-25T14:20:01.525675","indexId":"70264813","displayToPublicDate":"2025-03-01T09:10:39","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Natural capital accounting on forested lands: An application to the Colorado River basin","docAbstract":"This paper creates a first set of forest natural capital accounts and demonstrates how these accounts can be integrated with general equilibrium models of the economy. Focusing on the Colorado River Basin, we show that deforestation has direct implications for the forest industry and indirect impacts on the economy through water treatment costs and carbon stock. 327,000 acres of forest are projected to be lost to development by 2100, representing a loss of 1.3 million tons of carbon stored in forests. The direct economic impacts associated with forest loss are estimated to be over $30 million, with $28 million of that coming directly from the value of lost carbon.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Measuring and accounting for environmental public goods: A national accounts perspective","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"University of Chicago Press","collaboration":"USDA Forest Service, North Carolina State University, USDA ERS, U of Tennessee Knoxville","usgsCitation":"Warziniack, T., Bagstad, K.J., Knowles, M., Mihiar, C., Nehra, A., Rhodes, C., Sanchez, L., Sichko, C., and Sims, C., 2025, Natural capital accounting on forested lands: An application to the Colorado River basin, in Measuring and accounting for environmental public goods: A national accounts perspective, p. 191-214.","productDescription":"24 p.","startPage":"191","endPage":"214","ipdsId":"IP-150771","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":483773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Muller, Nicholas Z.","contributorId":352667,"corporation":false,"usgs":false,"family":"Muller","given":"Nicholas Z.","affiliations":[],"preferred":false,"id":931958,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Fenichel, Eli P.","contributorId":177324,"corporation":false,"usgs":false,"family":"Fenichel","given":"Eli","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":931959,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Bohman, Mary","contributorId":352668,"corporation":false,"usgs":false,"family":"Bohman","given":"Mary","affiliations":[],"preferred":false,"id":931960,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Warziniack, Travis 0000-0002-9431-2288","orcid":"https://orcid.org/0000-0002-9431-2288","contributorId":217841,"corporation":false,"usgs":false,"family":"Warziniack","given":"Travis","email":"","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":931807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":931808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knowles, Michael","contributorId":352586,"corporation":false,"usgs":false,"family":"Knowles","given":"Michael","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mihiar, Christopher","contributorId":352587,"corporation":false,"usgs":false,"family":"Mihiar","given":"Christopher","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931810,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nehra, Arpita","contributorId":352588,"corporation":false,"usgs":false,"family":"Nehra","given":"Arpita","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":931811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rhodes, Charles 0000-0002-9040-3684","orcid":"https://orcid.org/0000-0002-9040-3684","contributorId":245881,"corporation":false,"usgs":true,"family":"Rhodes","given":"Charles","email":"","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":931812,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sanchez, Leslie","contributorId":352589,"corporation":false,"usgs":false,"family":"Sanchez","given":"Leslie","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":931813,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sichko, Christopher","contributorId":352590,"corporation":false,"usgs":false,"family":"Sichko","given":"Christopher","affiliations":[{"id":84276,"text":"USDA ERS","active":true,"usgs":false}],"preferred":false,"id":931814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sims, Charles B.","contributorId":352591,"corporation":false,"usgs":false,"family":"Sims","given":"Charles B.","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":931815,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263980,"text":"70263980 - 2025 - Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California","interactions":[],"lastModifiedDate":"2025-03-04T15:11:12.24565","indexId":"70263980","displayToPublicDate":"2025-03-01T09:07:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California","docAbstract":"An important goal for the applied ecological sciences is to understand the extent to which the biodiversity on conserved or managed lands is exposed to anthropogenic stressors. Among the various categories of conserved lands in the United States, the National Wildlife Refuge System is focused on the protection and management of native wildlife and plant populations. Refuge lands can be associated with wetlands and, especially in California, tend to be near areas of intense human use, including agriculture. The proximity of refuges to cultivated lands results in potential exposure to pesticide drift, which increases the possibility of non-target effects on plants and wildlife. We used a passive sampler approach to quantify aerial pesticide deposition during 28 days in spring of 2023, across five refuges in the Sacramento Valley of California. Pesticides were detected across all refuges and plots within the study area, with 36 individual compounds identified, including insecticides, herbicides, and fungicides, from 176 silicone bands from 49 plots. Distance to agriculture was not an important predictor in our models for most indices of contamination. However, our models revealed that the nearest crop type was an important mediator of the importance of distance to agriculture, as fungicide contamination decreased with distance to agriculture only when orchards were the nearest crop type. The overall lack of distance effects suggests that the interior of refuges in the study area do not necessarily provide more protection from pesticides than refuge edges, although larger refuges could offer more protection. We currently lack information on how the level of contamination found herein is biologically relevant to wildlife, and ongoing monitoring efforts have found productive communities of insects in the study area. The results of this study provide a foundation for future investigations on refuges to analyze impacts of specific pesticide compounds on plants and wildlife.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.178991","usgsCitation":"Lenard, A., Burns, T., Hladik, M.L., Keller, K., Marcum, S., Thogmartin, W.E., and Forister, M.L., 2025, Pesticide contamination detected across five wildlife refuges in the Sacramento Valley of California: Science of the Total Environment, v. 969, 178991, 11 p., https://doi.org/10.1016/j.scitotenv.2025.178991.","productDescription":"178991, 11 p.","ipdsId":"IP-172823","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":487280,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.178991","text":"Publisher Index Page"},{"id":482793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.37300689853365,\n 39.68687752157848\n ],\n [\n -122.37300689853365,\n 38.24719803978664\n ],\n [\n -121.17657922197687,\n 38.24719803978664\n ],\n [\n -121.17657922197687,\n 39.68687752157848\n ],\n [\n -122.37300689853365,\n 39.68687752157848\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"969","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lenard, Angie 0000-0002-0085-0473","orcid":"https://orcid.org/0000-0002-0085-0473","contributorId":344593,"corporation":false,"usgs":false,"family":"Lenard","given":"Angie","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":929408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Therese 0000-0003-0213-6094","orcid":"https://orcid.org/0000-0003-0213-6094","contributorId":344594,"corporation":false,"usgs":false,"family":"Burns","given":"Therese","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keller, Kaylene","contributorId":351762,"corporation":false,"usgs":false,"family":"Keller","given":"Kaylene","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marcum, Samantha","contributorId":351763,"corporation":false,"usgs":false,"family":"Marcum","given":"Samantha","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":929413,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Forister, Matthew L.","contributorId":173758,"corporation":false,"usgs":false,"family":"Forister","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":929414,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264760,"text":"70264760 - 2025 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024","interactions":[],"lastModifiedDate":"2025-03-24T14:18:44.046889","indexId":"70264760","displayToPublicDate":"2025-03-01T09:07:02","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024","docAbstract":"A comprehensive understanding of fish populations and their interactions is the cornerstone of modern fishery management and the basis for Lake Erie’s Fish Community Objectives (FCOs) developed in 2020 (Francis et al. 2020). The 2024 U.S. Geological Survey (USGS) Lake Erie Biological Station Annual Report is responsive to these FCOs and the USGS obligations via a Memorandum of Understanding (MOU 2017) with the Great Lakes Fishery Commission (GLFC) Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies were to monitor long-term changes in the fish community and track population dynamics of key fishes of interest to management agencies. Specific to Lake Erie, expectations were sustained investigations of native percids, prey fish populations, and Lake Trout. All work was conducted as part of the Deepwater Science Program under the authority of the Great Lakes Fishery Research Authorization Act of 2019 (16 USC §941h).\n\nThe USGS 2024 Deepwater Science Program fieldwork began in Lake Erie in March and concluded in December, using trawl, gill net, hydroacoustic, lower trophic sampling devices, and telemetry methods. This work resulted in 44 bottom trawls covering 41 ha of lake bottom and catching 48,936 fish totaling 995 kg in the West Basin of Lake Erie, with detailed results described below. Overnight gill net sets (n=25) for coldwater species were performed in the East Basin of Lake Erie. A total of 8 km of gillnet was deployed during these surveys, which caught 106 fish, 92 of which were native coldwater species: Lake Trout, Burbot, and Lake Whitefish. Results from coldwater species assessments will be reported in the Coldwater Task Group report to the GLFC and the CLC (CTG 2025). These reports are used to inform Lake Trout stocking decisions and direct lamprey control measures (16 USC §939a). USGS hydroacoustic sampling included twenty-six 5-km transects (130 km total) in the Central Basin as part of a collaborative lake-wide survey with details and results reported by the Forage Task Group (FTG 2025). Lower trophic sampling provided data from zooplankton samples (n=12) and water quality profiles (n=12) to populate a database maintained by the Michigan Department of Natural Resources (MDNR), Ontario Ministry of Natural Resources (OMNR), Ohio Department of Natural Resources (ODNR), Pennsylvania Fish and Boat Commission (PFBC), and New York State Department of Environmental Conservation (NYSDEC). USGS also assisted CLC member agencies with deployment and maintenance of Great Lakes Acoustic Telemetry Observation System (GLATOS) infrastructure throughout all three Lake Erie basins and tributaries, supporting multiple coordinated telemetry investigations.\n\nThis report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2024 during June and September. The survey design compliments the August ODNR- OMNRF effort by reinforcing stock assessments with more robust data. Analyses herein evaluated trends in total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity, and community structure. Data from this effort are accessible for download (Keretz et al. 2025)","language":"English","publisher":"Great Lakes Fisheries Commission","usgsCitation":"Dufour, M.R., Guzzo, F., Hilling, C.D., Keretz, K.R., Kraus, R., Oldham, R.C., Roberts, J., and Schmitt, J., 2025, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2024, 18 p.","productDescription":"18 p.","ipdsId":"IP-176058","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":483683,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://glfc.org/publication-media-search.php","linkFileType":{"id":5,"text":"html"}},{"id":483708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.27940568089508,\n 42.15760057509911\n ],\n [\n -83.54993583176982,\n 42.15760057509911\n ],\n [\n -83.54993583176982,\n 41.240913888913184\n ],\n [\n -82.27940568089508,\n 41.240913888913184\n ],\n [\n -82.27940568089508,\n 42.15760057509911\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guzzo, Francesco 0000-0003-2374-0514","orcid":"https://orcid.org/0000-0003-2374-0514","contributorId":351294,"corporation":false,"usgs":true,"family":"Guzzo","given":"Francesco","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":931562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oldham, Richard Cole 0000-0002-2331-7612","orcid":"https://orcid.org/0000-0002-2331-7612","contributorId":294345,"corporation":false,"usgs":true,"family":"Oldham","given":"Richard","email":"","middleInitial":"Cole","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931564,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931565,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931566,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267311,"text":"70267311 - 2025 - Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population","interactions":[],"lastModifiedDate":"2025-05-20T16:04:38.811631","indexId":"70267311","displayToPublicDate":"2025-03-01T08:59:27","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population","docAbstract":"The greater sage-grouse (Centrocercus urophasianus: hereafter sage-grouse) population in Modoc County California is geographically isolated and contains a single lek (from 56 leks in the 1940s), despite significant efforts to increase the population through translocations and habitat improvement. Repeated wildfire within the landscape has led to an increase in invasive annual grasses and a decrease in sagebrush (Artemisia sp.) cover in important nesting and brood-rearing habitat. We estimated survival for adult females, nests, and chicks and assessed biotic characteristics that may influence these survival estimates to identify factors that may be limiting population growth. We monitored 37 female sage-grouse marked with GPS PTTS, 39 nests, and 8 broods for 3 years (2019–2021). We measured vegetation characteristics for nests at the microsite and landscape scale to evaluate effects on daily nest survival (DNS). We used survival rates from all life stages to parameterize matrix models and estimate population growth rate. Mean nest success over 3 years was 29% (95% CI: 17.1–44.8) across a 29-day incubation period and DNS declined as the proportion of both medusahead Taeniatherum caput-medusae and Japanese brome Bromus japonicus around the nest increased. Across all three years, mean chick survival across a 54-day period was 44% (95% CI: 0.9–72.3) and mean annual survival for adult females was 29% (95% CI: 17.8–43.7). Our estimated vital rates were 45–55% lower than distribution-wide estimates and the projected population growth rate was strongly declining (0.411, 95% CI: 0.30–0.52). Our results suggested recent cover changes associated with wildfire on the study area may have had a detrimental effect on this population across all life stages, and if attention is not given to preventing the transition from sagebrush communities into invasive grasslands, this population and others in degraded landscapes may not persist.
","language":"English","publisher":"Wiley","doi":"10.1002/wlb3.01242","usgsCitation":"Sink, C., Dugger, K., Hagen, C., and Vradenburg, J., 2025, Living on the edge: Identifying demographic bottlenecks in an isolated sage-grouse population: Wildlife Biology, v. 2025, no. 2, e01242, 18 p., https://doi.org/10.1002/wlb3.01242.","productDescription":"e01242, 18 p.","ipdsId":"IP-158671","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wlb3.01242","text":"Publisher Index Page"},{"id":486226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Modoc County","otherGeospatial":"Clear LakeNational Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.24706523722875,\n 41.936621403090356\n ],\n [\n -121.24706523722875,\n 41.792308909369126\n ],\n [\n -121.02561950743748,\n 41.792308909369126\n ],\n [\n -121.02561950743748,\n 41.936621403090356\n ],\n [\n -121.24706523722875,\n 41.936621403090356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Sink, Chelsea E.","contributorId":355595,"corporation":false,"usgs":false,"family":"Sink","given":"Chelsea E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":355597,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vradenburg, John N.","contributorId":355598,"corporation":false,"usgs":false,"family":"Vradenburg","given":"John N.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937698,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267332,"text":"70267332 - 2025 - Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore","interactions":[],"lastModifiedDate":"2025-05-20T15:58:47.136322","indexId":"70267332","displayToPublicDate":"2025-03-01T08:51:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore","docAbstract":"Urban wildlife species have the potential to serve as links in disease transmission between wildlife, humans and domestic animals at the wildland–urban interface (WUI), contributing to both sustained cross-species transmission of pathogens and the emergence of diseases in susceptible populations. However, the relative roles of host and environmental factors in shaping the composition of pathogen communities in urban wildlife is understudied. In this study, we integrated DNA and RNA virome data with host genomic and GPS datasets to investigate factors shaping virome composition in bobcats (Lynx rufus ) at the WUI in the Tucson Mountains, Arizona, USA. Using a hybrid-capture approach for 31 scats and 17 buccal swabs, we identified multiple viruses that could affect carnivore health at the WUI, including canine parvovirus, feline astrovirus, Felis catus papillomaviruses 2 and 3 and Lyon-IARC polyomavirus. Models of virome composition and distribution of viral taxa indicated contributions of host genetic relatedness and factors relating to urbanisation (such as percentages of urban land cover, road and building densities and distances to roads). Genetic associations with virome compositions were particularly influenced by females. While females exhibit significant isolation by distance, partial Mantel tests revealed a significant correlation between beta diversity and host genetic distance in females only. To our knowledge, this study represents the first assessment of factors shaping virome composition in a wild felid. Our finding of known feline and canine pathogens in bobcats underscores the potential of the WUI to facilitate cross-species transmission between wild and domestic animals.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.17681","usgsCitation":"Payne, N., Andersen, D., Mollohan, C., Van Doorslaer, K., Combrink, L., and Culver, M., 2025, Urbanization and host relatedness shape virome composition in a widespread, generalist carnivore: Molecular Ecology, v. 34, no. 6, e17681, 17 p., https://doi.org/10.1111/mec.17681.","productDescription":"e17681, 17 p.","ipdsId":"IP-174506","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","city":"Tucson","otherGeospatial":"Tucson Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.20811415902976,\n 32.32028515103218\n ],\n [\n -111.20811415902976,\n 32.11964386691881\n ],\n [\n -111.03231033562199,\n 32.11964386691881\n ],\n [\n -111.03231033562199,\n 32.32028515103218\n ],\n [\n -111.20811415902976,\n 32.32028515103218\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Payne, Natalie","contributorId":355612,"corporation":false,"usgs":false,"family":"Payne","given":"Natalie","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, Desiree","contributorId":355613,"corporation":false,"usgs":false,"family":"Andersen","given":"Desiree","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollohan, Cheryl","contributorId":355614,"corporation":false,"usgs":false,"family":"Mollohan","given":"Cheryl","affiliations":[{"id":84784,"text":"Bobcats in Tucson Research Project ©","active":true,"usgs":false}],"preferred":false,"id":937760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Doorslaer, Koenraad","contributorId":355615,"corporation":false,"usgs":false,"family":"Van Doorslaer","given":"Koenraad","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937761,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Combrink, Leigh","contributorId":355616,"corporation":false,"usgs":false,"family":"Combrink","given":"Leigh","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":937762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937763,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267776,"text":"70267776 - 2025 - Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","interactions":[],"lastModifiedDate":"2025-05-30T15:24:10.593599","indexId":"70267776","displayToPublicDate":"2025-03-01T08:17:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1577,"text":"Environmetrics","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids","docAbstract":"Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer (Odocoileus virginianus) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2901","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2025, Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids: Environmetrics, v. 36, no. 2, e2901, 14 p., https://doi.org/10.1002/env.2901.","productDescription":"e2901, 14 p.","ipdsId":"IP-166539","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/env.2901","text":"Publisher Index Page"},{"id":489259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.82700363741058,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 42.50633570600206\n ],\n [\n -89.0890731993092,\n 43.768147806390715\n ],\n [\n -90.82700363741058,\n 43.768147806390715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"36","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":356171,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":356173,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":356175,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":938826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938827,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269357,"text":"70269357 - 2025 - ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","interactions":[],"lastModifiedDate":"2025-07-21T14:22:11.345476","indexId":"70269357","displayToPublicDate":"2025-02-28T09:18:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"ARCHI: A new R package for automated imputation of regionally correlated hydrologic records","docAbstract":"Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13474","usgsCitation":"Levy, Z., Glas, R.L., Stagnitta, T.J., and Terry, N., 2025, ARCHI: A new R package for automated imputation of regionally correlated hydrologic records: Groundwater, v. 62, no. 4, p. 595-610, https://doi.org/10.1111/gwat.13474.","productDescription":"16 p.","startPage":"595","endPage":"610","ipdsId":"IP-166664","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":492870,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13474","text":"Publisher Index Page"},{"id":492796,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17NXGHV","text":"USGS data release","linkHelpText":"Example Groundwater-Level Datasets and Benchmarking Results for the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) Software Package"},{"id":492616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York","otherGeospatial":"Central Valley, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.13447900247726,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 35.88363374499093\n ],\n [\n -118.93035501208706,\n 37.3805353859038\n ],\n [\n -121.13447900247726,\n 37.3805353859038\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.76788606860265,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 41.23528810034301\n ],\n [\n -74.12298971233533,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 40.43286561377994\n ],\n [\n -71.76788606860265,\n 41.23528810034301\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glas, Robin L. 0000-0002-7394-1667","orcid":"https://orcid.org/0000-0002-7394-1667","contributorId":300625,"corporation":false,"usgs":true,"family":"Glas","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagnitta, Timothy J. 0000-0001-8903-428X","orcid":"https://orcid.org/0000-0001-8903-428X","contributorId":304230,"corporation":false,"usgs":true,"family":"Stagnitta","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":943533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":943534,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264011,"text":"70264011 - 2025 - The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","interactions":[],"lastModifiedDate":"2025-03-04T15:45:33.410255","indexId":"70264011","displayToPublicDate":"2025-02-28T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought","docAbstract":"Quantitative understanding is lacking on how the depth of active groundwater circulation in\nbedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112927","usgsCitation":"Carroll, R., Manning, A.H., and Williams, K., 2025, The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought: Geophysical Research Letters, v. 52, no. 4, e2024GL112927, 12 p., https://doi.org/10.1029/2024GL112927.","productDescription":"e2024GL112927, 12 p.","ipdsId":"IP-171471","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":487740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112927","text":"Publisher Index Page"},{"id":482802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Copper Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.1775889108842,\n 39.499761316913094\n ],\n [\n -106.1775889108842,\n 39.45001492350738\n ],\n [\n -106.1485831457982,\n 39.45001492350738\n ],\n [\n -106.1485831457982,\n 39.499761316913094\n ],\n [\n -106.1775889108842,\n 39.499761316913094\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H.","contributorId":336921,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary W.H.","affiliations":[{"id":55475,"text":"Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":929471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Kenneth H.","contributorId":336926,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth H.","affiliations":[{"id":80914,"text":"Rocky Mountain Biological Laboratory, Gothic, CO","active":true,"usgs":false}],"preferred":false,"id":929472,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264313,"text":"70264313 - 2025 - Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","interactions":[],"lastModifiedDate":"2025-03-11T14:33:16.317819","indexId":"70264313","displayToPublicDate":"2025-02-27T09:28:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5021,"text":"Environments","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model","docAbstract":"Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.
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Branch","active":true,"usgs":true}],"preferred":true,"id":930396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tick, Geoffrey","contributorId":352233,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gholizadeh, Hossein","contributorId":352234,"corporation":false,"usgs":false,"family":"Gholizadeh","given":"Hossein","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wei, Wei","contributorId":352235,"corporation":false,"usgs":false,"family":"Wei","given":"Wei","affiliations":[{"id":84137,"text":"School of Environment, Nanjing Normal University, Nanjing 210023, China","active":true,"usgs":false}],"preferred":false,"id":930399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yong","contributorId":352236,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":930400,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263949,"text":"70263949 - 2025 - Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","interactions":[],"lastModifiedDate":"2025-03-26T15:59:01.447164","indexId":"70263949","displayToPublicDate":"2025-02-27T09:07:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use","docAbstract":"Thermoelectric power generation accounts for over 41% of total U.S. freshwater withdrawals, making understanding the determinants of power plants’ water withdrawals (WW) and consumption (WC) critical for reducing the sector’s reliance on increasingly scarce water resources. However, reported data inconsistencies and incomplete analysis of potential determinants of thermoelectric water use hinder such understanding. We address these challenges by introducing a novel data filtering method and a more complete assessment of water use determinants. First, we applied a power-cooling ratio as an operations-based data filter that removed operationally implausible records while retaining more original data, outperforming previous statistical filtering methods. Second, we found that different water use reporting methods (WURMs) provided statistically significantly different WW and WC values, revealing the importance of this previously unrecognized feature in reported water use records. Third, our data-driven approach showed that traditionally emphasized features─such as cooling technology and gross generation─are of primary importance but can be surpassed by other, often overlooked, features when modeling WW or WC individually. The plant configuration, cooling technology, and gross generation were the most important features of WW, whereas WURM, cooling technology, and reporting month were the most important for WC. These findings can improve thermoelectric power plant management, water use reporting accuracy, and water use modeling.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c02024","usgsCitation":"Sjostedt, E., Rushforth, R., Tidwell, V., Harris, M.A., McManamay, R., and Marston, L., 2025, Impact of thermoelectric power plant operations and water use reporting methods on thermoelectric power plant water use: Environmental Science & Technology, v. 59, no. 9, p. 4482-4492, https://doi.org/10.1021/acs.est.4c02024.","productDescription":"11 p.","startPage":"4482","endPage":"4492","ipdsId":"IP-160568","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488675,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c02024","text":"Publisher Index Page"},{"id":482736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sjostedt, Eric 0000-0002-7755-8091","orcid":"https://orcid.org/0000-0002-7755-8091","contributorId":351702,"corporation":false,"usgs":false,"family":"Sjostedt","given":"Eric","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":929309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":929310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tidwell, Vincent 0000-0002-4954-897X","orcid":"https://orcid.org/0000-0002-4954-897X","contributorId":351704,"corporation":false,"usgs":false,"family":"Tidwell","given":"Vincent","affiliations":[{"id":84033,"text":"Pacific Northwest National Laboratories","active":true,"usgs":false}],"preferred":false,"id":929311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Melissa A. 0000-0003-2659-9763 mharris@usgs.gov","orcid":"https://orcid.org/0000-0003-2659-9763","contributorId":1903,"corporation":false,"usgs":true,"family":"Harris","given":"Melissa","email":"mharris@usgs.gov","middleInitial":"A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":929313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marston, Landon 0000-0001-9116-1691","orcid":"https://orcid.org/0000-0001-9116-1691","contributorId":239626,"corporation":false,"usgs":false,"family":"Marston","given":"Landon","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":929314,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264266,"text":"70264266 - 2025 - Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","interactions":[],"lastModifiedDate":"2025-03-10T13:53:35.39942","indexId":"70264266","displayToPublicDate":"2025-02-27T08:49:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Survival, travel time, and use of migration routes by juvenile steelhead in a modified river estuary","docAbstract":"Greater understanding of the survival, travel time, and spatial distribution of juvenile salmonids among migration routes between their natal streams and the ocean is critical to the recovery of these threatened species. In the Sacramento–San Joaquin River Delta (Delta), a highly modified estuary in central California, USA, there is a critical need to evaluate how water management (e.g., water pumping) and environmental factors (e.g., water flow) impact these populations. While management actions can affect some environmental variables in the Delta, only recently have studies begun to uncover associations between these variables and key demographic parameters. In this study, we examine the effects of freshwater flows, water exports, tidal environment, and a temporary barrier on juvenile steelhead (Oncorhynchus mykiss) survival, travel times, and migration routing using a multiyear acoustic telemetry dataset and recent advancements in Bayesian multistate mark-recapture modeling. We found that no single covariate explained variation in juvenile steelhead population dynamics across the entire Delta, but that separate regions within the Delta showed association with specific environmental factors.
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Bd is regularly viewed through the lens of a global wildlife epizootic because the spread of highly virulent genetic lineages has resulted in well-documented declines and extinctions of multiple amphibian species. However, the current state of Bd occurrence, host range, host impacts, and ecological drivers remains poorly understood outside of the most negatively affected amphibian species and regions. Our objective was to describe the macroecology of Bd occurrence and infection intensity on caudates (salamanders) across the United States and to compare these patterns with better-studied anurans (frogs and toads). We collected swabs from 11,183 amphibians at 609 sites from 54 species across the United States from 2015 to 2017. We analyzed the prevalence and intensity of Bd infection jointly using a Bayesian hurdle model with covariates of site-level temperature and precipitation, as well as individual characteristics and species identification. 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However, data are limited on reproductive biology specific to wild populations in midwestern North America, despite recent concern for grass carp establishment within the Great Lakes. Basic information on reproductive traits could be useful to determine reproductive potential, inform future population modelling and provide information to aid control efforts. Our objectives were to evaluate grass carp age-at-maturity, spawning season timing, fecundity type, developmental timing and spawning strategy. Additionally, we evaluated the reliability of the gonadosomatic index (GSI) as a proxy for identifying mature grass carp and compared body condition across study areas. We sampled grass carp in portions of the Mississippi River watershed within the state of Missouri and within the Lake Erie basin. Based on gonad histological samples (n = 274), grass carp are batch spawners with indeterminate fecundity and asynchronous ovarian developmental timing. This allows flexibility in their spawning, exemplified by a protracted spawning season in Lake Erie (April to November) in temperatures ranging from 12.3 to 27.3°C. Minimum observed age-at-maturity for females in Missouri and for both males and females in the Lake Erie population was age-3 and age-2 for males in Missouri. Accuracy of GSI as a measure of maturity during the spawning season was 89.7% and 87.5%, for females and males, respectively. Compared to Missouri, grass carp in Lake Erie had a significantly higher body condition, and females ready to spawn had significantly higher GSI values, suggesting that the grass carp in Lake Erie are healthier and more fecund relative to Missouri and, if left uncontrolled, potentially have a high probability of establishment within the Great Lakes. The results of this study will allow managers to identify the reproductive status of grass carp in the field and more accurately estimate populations and reproductive potential.
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Using 6 years (2013–2018) of camera-trapping data and species interaction models, we investigated the spatiotemporal patterns of inter- and intra-guild interspecific interactions in a diverse terrestrial mammalian community in Pakke Wildlife Sanctuary and Tiger Reserve (PTR), Northeast India. We found no evidence of spatial interaction among apex predators (tiger Panthera tigris, leopard Panthera pardus, and dhole Cuon alpinus). However, dholes temporally separated themselves from tigers and leopards. Among small carnivores, marbled cat (Pardofelis marmorata) and leopard cat (Prionailurus bengalensis) exhibited temporal separation, whereas leopard cat overlapped spatially and temporally with other small carnivores. Herbivores exhibited neither spatial nor temporal separation with each other. All apex predators exhibited diel activity and space-use patterns to overlap with their preferred prey. Our results suggest that the assembly of the diverse mammalian community of PTR is a complex process, and coexistence among potential competitors, and predators and prey is likely facilitated by several mechanisms including spatial and temporal segregation, and potentially dietary separation.
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