Federal and state agencies within the United States have recently issued directives prioritizing the conservation of ungulate migration corridors and winter ranges. The ability to identify and delineate the spatial distribution of seasonal ranges underpins these policies. While such delineations are often derived from global positioning system (GPS) collar data collected for a few years on a focal population, they are being used in long-term conservation planning. Our objectives were to quantify consistency in migration corridors from year to year and cumulatively across multiple years and identify which aspects of the sampling design of GPS collar deployment will delineate a consistent and relatively complete migration corridor. We used data from 6 sub-herds of mule deer (Odocoileus hemionus), a species known to have high migratory fidelity, located in Wyoming and northern New Mexico, USA, monitored for 5–7 years (510 unique individuals). We calculated 2 types of migration corridors over time: cumulative corridors where each new year of data was added to all previous years and yearly corridors where each year was based only on data collected in that year. We then calculated the year-to-year consistency in the 2 types of migration corridors by calculating the percent overlap between corridors calculated in sequential years. We found that collaring a higher proportion of a sub-herd increased the consistency in migration corridors, whereas collaring new individuals via redeployments in a subsequent year of monitoring caused corridors to shift. To obtain a corridor with ≥90% consistency (i.e., approaching the complete area used by a population in our data), our results suggest that biologists should strive to collar ≥6% of a sub-herd for a minimum of 2 years. However, if ≥6% of a sub-herd cannot be collared, monitoring for longer (3–4 years) will provide roughly 90% consistency in a migration corridor estimate for mule deer. Furthermore, adding 16–25% new individuals each year will help capture variation among individuals while maintaining corridor consistency of ≥90%, leading to a more accurate delineation of the corridor. Our results provide managers with a logistical framework for collaring projects aimed at delineating migration corridors that are durable into the future.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70009","usgsCitation":"Gelzer, E., Becker, J., Dwinnell, S., Fralick, G., Hall, E., Kaiser, R.C., Kauffman, M., LaSharr, T., Monteith, K., Ortega, A.C., Randall, J.E., Sawyer, H., Thonhoff, M.A., and Merkle, J., 2025, How sampling design of GPS collar deployment influences consistency of mapped migration corridors over time: Journal of Wildlife Management, v. 89, no. 4, e70009, 15 p., https://doi.org/10.1002/jwmg.70009.","productDescription":"e70009, 15 p.","ipdsId":"IP-175330","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.87494700057805,\n 45.02268702593605\n ],\n [\n -110.99156146867723,\n 40.956403263455265\n ],\n [\n -109.0932311191346,\n 40.960139474218906\n ],\n [\n -109.16883609742519,\n 37.07352465596277\n ],\n [\n -109.12674409323587,\n 35.063970198256655\n ],\n [\n -103.023660653696,\n 35.076921949317224\n ],\n [\n -102.93151087722988,\n 36.88068018683272\n ],\n [\n -102.10930455084087,\n 37.11415790473275\n ],\n [\n -102.13691673529834,\n 41.09404876267421\n ],\n [\n -103.91763078114458,\n 41.08136907287786\n ],\n [\n -103.87586205554331,\n 44.92487662047682\n ],\n [\n -110.87494700057805,\n 45.02268702593605\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Gelzer, Emily R.","contributorId":355619,"corporation":false,"usgs":false,"family":"Gelzer","given":"Emily R.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":937772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Justine A.","contributorId":355620,"corporation":false,"usgs":false,"family":"Becker","given":"Justine A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwinnell, Samantha P.H.","contributorId":288166,"corporation":false,"usgs":false,"family":"Dwinnell","given":"Samantha P.H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":937774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fralick, Gary L.","contributorId":288169,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary L.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":937775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, Embere","contributorId":289727,"corporation":false,"usgs":false,"family":"Hall","given":"Embere","email":"","affiliations":[],"preferred":false,"id":937776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaiser, Rusty C.","contributorId":339124,"corporation":false,"usgs":false,"family":"Kaiser","given":"Rusty","email":"","middleInitial":"C.","affiliations":[{"id":40027,"text":"United States Forest Service","active":true,"usgs":false}],"preferred":false,"id":937777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937778,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LaSharr, Tayler N.","contributorId":288167,"corporation":false,"usgs":false,"family":"LaSharr","given":"Tayler N.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":937779,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Monteith, Kevin L.","contributorId":287801,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":937780,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ortega, Anna C.","contributorId":280169,"corporation":false,"usgs":false,"family":"Ortega","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":937781,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Randall, Jill E.","contributorId":288816,"corporation":false,"usgs":false,"family":"Randall","given":"Jill","email":"","middleInitial":"E.","affiliations":[{"id":54471,"text":"wyfg","active":true,"usgs":false}],"preferred":false,"id":937782,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sawyer, Hall","contributorId":287880,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","affiliations":[{"id":61660,"text":"Western Ecosystems Technology, Inc., Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":937783,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thonhoff, Mark A.","contributorId":288818,"corporation":false,"usgs":false,"family":"Thonhoff","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":937784,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Merkle, Jerod A.","contributorId":287300,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod A.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":937785,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70267293,"text":"70267293 - 2025 - 2022–2024 Status and trends of the Palila (Loxioides bailleui)","interactions":[],"lastModifiedDate":"2025-05-23T19:58:57.262267","indexId":"70267293","displayToPublicDate":"2025-05-01T09:30:04","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":6053,"text":"Hawaii Cooperative Studies Unit Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCSU-115","displayTitle":"2022–2024 Status and trends of the Palila (Loxioides bailleui)","title":"2022–2024 Status and trends of the Palila (Loxioides bailleui)","docAbstract":"Palila (Loxioides bailleui) are critically endangered Hawaiian honeycreepers specializing on the seedpods of māmane (Sophora chrysophylla) and restricted to Mauna Kea volcano on the Island of Hawaiʻi. A previous analysis of survey data estimated an 89% population decline between 1998 and 2021. Using the most recent annual survey data from 2022, 2023, and 2024, we report updated annual population estimates and trends since 1998. The 2022 population estimate was 367–742 birds (point estimate: 545); the 2023 population estimate was 374–842 birds (point estimate: 596); and the 2024 population estimate was 412–970 birds (point estimate: 666). Our estimates for survey years prior to 2022 were within the confidence intervals of the estimates from the previous analysis. Our models likewise showed a population fluctuating between 4,000 and 6,800 birds from 1998 to 2005 (except for an unusually low estimate in 2000), and then a steep decline through 2010. For the next decade, palila abundance fluctuated between 776 and 1,346 birds, before declining again in 2021 to 679 birds. From 1998 to 2024, the population declined by >90% or 203 birds/year, with very strong statistical evidence of an overall downward trend.
","language":"English","publisher":"University of Hawai‘i at Hilo","usgsCitation":"Hunt, N., Asing, C.K., Nietmann, L., Banko, P.C., and Camp, R.J., 2025, 2022–2024 Status and trends of the Palila (Loxioides bailleui): Hawaii Cooperative Studies Unit Technical Report HCSU-115, iii, 19 p.","productDescription":"iii, 19 p.","ipdsId":"IP-176419","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":486210,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":486153,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5398"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.6,\n 19.8333\n ],\n [\n -155.6,\n 19.7\n ],\n [\n -155.40506707246803,\n 19.7\n ],\n [\n -155.40506707246803,\n 19.8333\n ],\n [\n -155.6,\n 19.8333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hunt, Noah","contributorId":355564,"corporation":false,"usgs":false,"family":"Hunt","given":"Noah","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":937642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":937643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nietmann, Lindsey","contributorId":331548,"corporation":false,"usgs":false,"family":"Nietmann","given":"Lindsey","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":937644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":937645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":937646,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269940,"text":"70269940 - 2025 - Idiosyncratic spatial scaling of biodiversity–disease relationships","interactions":[],"lastModifiedDate":"2025-08-07T16:23:56.559115","indexId":"70269940","displayToPublicDate":"2025-05-01T09:17:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Idiosyncratic spatial scaling of biodiversity–disease relationships","docAbstract":"High host biodiversity is hypothesized to dilute the risk of vector-borne diseases if many host species are ‘dead ends' that cannot effectively transmit the disease and low-diversity areas tend to be dominated by competent host species. However, many studies on biodiversity–disease relationships characterize host biodiversity at single, local spatial scales, which complicates efforts to forecast disease risk if associations between host biodiversity and disease change with spatial scale. Here, our objective is to evaluate the spatial scaling of relationships between host biodiversity and Borrelia (the bacterial taxon which causes Lyme disease) infection prevalence in small mammals. We compared the associations between infection prevalence and small mammal host diversity for local communities (individual plots) and metacommunities (multiple plots aggregated within a landscape) sampled by the National Ecological Observatory Network (NEON), an emerging continental-scale environmental monitoring program with a hierarchical sampling design. We applied a multispecies, spatially-stratified capture–recapture model to a trapping dataset to estimate five small mammal biodiversity metrics, which we used to predict infection status for a subset of trapped individuals. We found that relationships between Borrelia infection prevalence and biodiversity did indeed vary when biodiversity was quantified at different spatial scales but that these scaling behaviors were idiosyncratic among the five biodiversity metrics. For example, species richness of local communities showed a negative (dilution) effect on infection prevalence, while species richness of the small mammal metacommunity showed a positive (amplification) effect on infection prevalence. Our modeling approach can inform future analyses as data from similar monitoring programs accumulate and become increasingly available through time. Our results indicate that a focus on single spatial scales when assessing the influence of biodiversity on disease risk provides an incomplete picture of the complexity of disease dynamics in ecosystems.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1111/ecog.07541","usgsCitation":"Gilbert, N.A., DiRenzo, G.V., and Zipkin, E., 2025, Idiosyncratic spatial scaling of biodiversity–disease relationships: Ecography, v. 2025, no. 5, e07541, 13 p., https://doi.org/10.1111/ecog.07541.","productDescription":"e07541, 13 p.","ipdsId":"IP-166571","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07541","text":"Publisher Index Page"},{"id":493725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.79469929309123,\n 49.09646929603289\n ],\n [\n -103.68585727501954,\n 40.73760320671225\n ],\n [\n -103.41172872889952,\n 31.989738528769458\n ],\n [\n -106.97439225502573,\n 31.559368481919833\n ],\n [\n -99.233036018366,\n 26.03830017900006\n ],\n [\n -79.60172129404337,\n 24.871286670280753\n ],\n [\n -68.62754461055297,\n 42.45895346896019\n ],\n [\n -71.39052201247834,\n 45.79255113886533\n ],\n [\n -79.93472542396827,\n 44.33548909164021\n ],\n [\n -84.1564540560646,\n 48.14762016540982\n ],\n [\n -103.79469929309123,\n 49.09646929603289\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Neil A.","contributorId":359068,"corporation":false,"usgs":false,"family":"Gilbert","given":"Neil","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":945185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":944996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zipkin, Elise 0000-0003-4155-6139 ezipkin@usgs.gov","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":242667,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise","email":"ezipkin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":945186,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267804,"text":"70267804 - 2025 - Trade-offs in designing a participatory acoustic study of bats: Comparison of user engagement and data quality between two ultrasonic detectors","interactions":[],"lastModifiedDate":"2026-01-20T20:33:15.356917","indexId":"70267804","displayToPublicDate":"2025-05-01T09:11:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19892,"text":"Journal of North American Bat Research","active":true,"publicationSubtype":{"id":10}},"title":"Trade-offs in designing a participatory acoustic study of bats: Comparison of user engagement and data quality between two ultrasonic detectors","docAbstract":"Technology for the acoustic detection of animals has advanced rapidly over the past few decades. Due to ease of use, consistency, and safety, acoustic methods are particularly useful for science applications that engage the public. In this study, we evaluated the technological and educational trade-offs between 2 acoustic bat detectors in a participatory science application along the Colorado River in the Grand Canyon, Arizona. Both devices were deployed simultaneously by commercial river guides in parallel with sampling insect prey for 1 h at dusk on 48 dates between April and October 2022. The detector with higher data quality capabilities recorded more bats overall (a mean of 231 more passes per hour) and more species (19 species, including 4 not detected by the lower quality detector). However, data from both detectors showed a decrease in total bat activity from spring to fall, despite the differences in recording capabilities. We conclude that detector quality matters, but user engagement is important when designing participatory research.
","language":"English","publisher":"Eagle Hill Institute","usgsCitation":"Metcalfe, A., Weller, T.J., Fritzinger, C., Holton, B.P., and Kennedy, T., 2025, Trade-offs in designing a participatory acoustic study of bats: Comparison of user engagement and data quality between two ultrasonic detectors: Journal of North American Bat Research, no. Special Issue 1, p. 89-99.","productDescription":"11 p.","startPage":"89","endPage":"99","ipdsId":"IP-167526","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":489334,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://eaglehill.us/NABRonline/access-pages/spec01/007-Metcalfe-accesspage.shtml"}],"issue":"Special Issue 1","noUsgsAuthors":false,"publicationDate":"2025-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Metcalfe, Anya 0000-0002-6286-4889","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":221738,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":938952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weller, Theodore J.","contributorId":105961,"corporation":false,"usgs":false,"family":"Weller","given":"Theodore","email":"","middleInitial":"J.","affiliations":[{"id":13261,"text":"USDA Forest Service, Pacific Southwest Research Station, Davis, California","active":true,"usgs":false}],"preferred":false,"id":938953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritzinger, Carol","contributorId":303018,"corporation":false,"usgs":false,"family":"Fritzinger","given":"Carol","email":"","affiliations":[{"id":65611,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":938954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holton, Brandon P.","contributorId":245212,"corporation":false,"usgs":false,"family":"Holton","given":"Brandon","email":"","middleInitial":"P.","affiliations":[{"id":49123,"text":"NPS - Grand Canyon National Park","active":true,"usgs":false}],"preferred":false,"id":938955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":938956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270331,"text":"70270331 - 2025 - Vegetation community monitoring: Forest structure in Klamath Network parks","interactions":[],"lastModifiedDate":"2025-08-14T14:01:47.898898","indexId":"70270331","displayToPublicDate":"2025-05-01T08:58:34","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":22172,"text":"Science Authors Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR-2025/291","title":"Vegetation community monitoring: Forest structure in Klamath Network parks","docAbstract":"The Klamath Network, comprising six national park units in northern California and southern Oregon, initiated a vegetation monitoring protocol in 2011 to discern ecologically significant vegetation trends in these parks. The premise of the protocol is that multivariate analyses of species composition data is the most robust means for early detection of vegetation change over time. Here, we present these community metrics based on our sampling efforts from 2011 to 2019. Observations from the first sampling event (2011–2013) were used to establish baseline conditions for the vegetation communities. Observations from subsequent sampling in 2014–2019 were used to identify potential temporal variation in forest structure across habitat types and parks.
Park landscapes were categorized into three strata: matrix (low- to mid-elevation upland habitats), riparian (within 10 m of a perennial stream), and high-elevation (above a predefined elevation, park-specific). At the onset of the network’s vegetation monitoring protocol, 241 permanent plots were established at random locations across the three strata. We present summary statistics from three repeated samplings (2011–2019) of each plot, describing variation in forest structure across broad habitat types and parks. Observable differences in forest structure aligned with expected productivity gradients across the parks. Measures of forest structure (vegetation cover, stem density, basal area, tree heights, height to live crown, shrub cover, and surface fuels) were generally higher in mesic sites, compared to sites located in more arid, continental climates. Differences across sampling frames also followed this general pattern of productivity. Matrix and riparian sampling frames had similar ranges of values in most cases, while high elevation sites had relatively lower stem density, basal area, shrub cover, fuels, and recruitment. Notably, we observed a relative lack of change in forest structure over time. This is not surprising given the relatively short (six-year) timespan of observations in each park. The fourth set of Klamath Network surveys (2021–2023) is likely to show substantial changes in vegetation cover and forest structure, particularly for parks that have recently experienced major fires.
Continued long-term vegetation monitoring is crucial for understanding ecosystem responses to a rapidly changing world. This report on vegetation composition is the second in a series; upcoming reports will analyze structure and function, aiming to detect spatiotemporal trends.
Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse (Bonasa umbellus). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2025.103166","usgsCitation":"Clarfeld, L.A., Gieder, K.D., Abrams, R., Bernier, C., Cahill, J., Staats, S., Wixsom, S., and Donovan, T.M., 2025, Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse: Ecological Informatics, v. 89, 103166, 14 p., https://doi.org/10.1016/j.ecoinf.2025.103166.","productDescription":"103166, 14 p.","ipdsId":"IP-172423","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2025.103166","text":"Publisher Index Page"},{"id":494382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Green Mountain National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.16516605268014,\n 43.209373047171994\n ],\n [\n -73.16516605268014,\n 42.88542467752458\n ],\n [\n -72.86267666610819,\n 42.88542467752458\n ],\n [\n -72.86267666610819,\n 43.209373047171994\n ],\n [\n -73.16516605268014,\n 43.209373047171994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clarfeld, Laurence A.","contributorId":359990,"corporation":false,"usgs":false,"family":"Clarfeld","given":"Laurence","middleInitial":"A.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":946634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gieder, Katherina D.","contributorId":359991,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","middleInitial":"D.","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":946635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abrams, Robert","contributorId":359992,"corporation":false,"usgs":false,"family":"Abrams","given":"Robert","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernier, Christopher","contributorId":359993,"corporation":false,"usgs":false,"family":"Bernier","given":"Christopher","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":946637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cahill, Joseph","contributorId":359994,"corporation":false,"usgs":false,"family":"Cahill","given":"Joseph","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staats, Susan","contributorId":359995,"corporation":false,"usgs":false,"family":"Staats","given":"Susan","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wixsom, Scott","contributorId":359996,"corporation":false,"usgs":false,"family":"Wixsom","given":"Scott","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946640,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946641,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269948,"text":"70269948 - 2025 - Assessing shifting technology in genetic monitoring of the North American plains bison Federal conservation herds","interactions":[],"lastModifiedDate":"2025-08-07T14:03:30.389115","indexId":"70269948","displayToPublicDate":"2025-04-29T08:57:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Assessing shifting technology in genetic monitoring of the North American plains bison Federal conservation herds","docAbstract":"Human expansion is a major driver of both declining wildlife abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, inferences from different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of North American plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses have been used to inform management of the US Federal bison conservation herds since the early 2000s. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., single nucleotide polymorphisms [SNPs]) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types. We compared microsatellite-based measures of genetic diversity, differentiation, and population structure for 20 bison conservation herds (17 Federal, 1 Tribal, 2 Canadian) to inference from SNP-based analyses for the Tribal herd and 15 of the Federal herds. Data from both genetic marker types found that all herds have remarkably high genetic diversity given the severity of the bottleneck from which these populations recovered, and that population structure was consistent with founding histories. Importantly, SNPs had greater power to describe differences in genetic diversity and groups of related herds, but only if analyses are based on 250 or more loci. Overall, we found that microsatellite and SNP data can provide comparable conservation insight, but SNPs must be carefully selected to ensure continuity in genetic monitoring and to achieve the increased precision in genetic diversity and differentiation among herds that we observed in this study.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-025-01694-2","usgsCitation":"Zimmerman, S.J., Giglio, R.M., Geremia, C., Jones, L.C., McCann, B., Smyser, T., Moynahan, B., and Oyler-McCance, S.J., 2025, Assessing shifting technology in genetic monitoring of the North American plains bison Federal conservation herds: Conservation Genetics, v. 26, p. 657-675, https://doi.org/10.1007/s10592-025-01694-2.","productDescription":"19 p.","startPage":"657","endPage":"675","ipdsId":"IP-173854","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":493794,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-025-01694-2","text":"Publisher Index Page"},{"id":493702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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0000-0003-4183-3546","orcid":"https://orcid.org/0000-0003-4183-3546","contributorId":359173,"corporation":false,"usgs":false,"family":"Giglio","given":"Rachael","middleInitial":"Marie","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":945013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Geremia, Chris","contributorId":167003,"corporation":false,"usgs":false,"family":"Geremia","given":"Chris","email":"","affiliations":[],"preferred":false,"id":945014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Lee C.","contributorId":149998,"corporation":false,"usgs":false,"family":"Jones","given":"Lee","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":945015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCann, Blake","contributorId":347580,"corporation":false,"usgs":false,"family":"McCann","given":"Blake","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":945016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smyser, Timothy J 0000-0003-4542-3077","orcid":"https://orcid.org/0000-0003-4542-3077","contributorId":359176,"corporation":false,"usgs":false,"family":"Smyser","given":"Timothy J","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":945017,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moynahan, Brendan J","contributorId":347582,"corporation":false,"usgs":false,"family":"Moynahan","given":"Brendan J","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":945018,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":945019,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266220,"text":"70266220 - 2025 - The tortoise and the antilocaprid: Adapting GPS tracking and terrain data to model wildlife walking functions","interactions":[],"lastModifiedDate":"2025-04-30T16:05:14.416417","indexId":"70266220","displayToPublicDate":"2025-04-29T08:53:18","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The tortoise and the antilocaprid: Adapting GPS tracking and terrain data to model wildlife walking functions","docAbstract":"Context
The relationship between slope and terrestrial animal locomotion is key to landscape ecology but underexplored across species. This is partly due to a lack of scalable methodology that applies to a diversity of wildlife.
Objectives
This study investigates the slope-speed relationship for two species, Texas tortoise (Gopherus berlandieri) and pronghorn (Antilocapra americana), through the combined application of remote sensing, GPS tracking, behavior models, and parametric distribution. While using readily available Digital Elevation Models (DEM) for pronghorn, we explore the use of very high-resolution lidar Digital Terrain Models (DTM) from Unoccupied Aerial Systems (UAS) to characterize tortoise movements at micro-scales.
Methods
After classifying animal behavior with GPS tracking data and Hidden Markov Models (HMMs), we analyzed the relationship between the speed of the animals and the slope of the terrain using a 30-m DEM for pronghorn, and a fine-scale UAS DTM for Texas tortoise, and three nonlinear models: Laplace, Gauss, and Lorentz.
Results
High-resolution DTM, coupled with GPS tracking, accurately models the relationship of speed and slope at a micro-scale, while a DEM is suitable for a larger scale. Laplace models best predicted the speed of both the Texas tortoise and pronghorn. Models showed tortoises, which are not known for rapid and agile movement like the pronghorn, have a broader tolerance for varying slopes at a fine scale.
Conclusions
These findings enhance understanding of species-specific movement offering valuable insights for habitat management and conservation tailored to species’ behaviors and capabilities.
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A.","contributorId":354033,"corporation":false,"usgs":false,"family":"Guerra","given":"Daniel A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":934986,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":934981,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","interactions":[{"subject":{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","indexId":"ofr20211030V","publicationYear":"2025","noYear":false,"chapter":"V","displayTitle":"System Characterization Report on Resourcesat-2A Advanced Wide Field Sensor","title":"System characterization report on Resourcesat-2A Advanced Wide Field Sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation 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These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resourcesat-2 and -2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about Indian Space Research Organisation satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium and from the Indian Space Research Organisation at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performance of the Resourcesat-2A AWiFS sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had offsets in the range of −1.10 meters (m; −0.020 pixel) to 3.67 m (0.066 pixel) in easting and −5.68 m (−0.101 pixel) to 10.38 m (0.185 pixel) in northing with root mean square error values from 5.60 m (0.100 pixel) to 11.31 m (0.202 pixel) in easting and from 3.00 m (0.054 pixel) to 13.52 m (0.241 pixel) in northing.
The exterior geometric performance had mean offsets of −25.29 m in easting and 16.22 m northing with root mean square error values of 26.07 m in easting and 17.60 m in northing compared to the Landsat 8 Operational Land Imager sensor. The radiometric performance had offsets from −0.002 to 0.029 and slopes from 0.733 to 1.012. Spatial performance was in the range of 1.354 to 1.639 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.108 to 0.174.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030V","usgsCitation":"Shrestha, M., Kim, M., Sampath, A., and Clausen, J., 2025, System characterization report on Resourcesat-2A Advanced Wide Field Sensor, chap. V of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 18 p., https://doi.org/10.3133/ofr20211030V.","productDescription":"v, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170096","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":485174,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030V/full"},{"id":485170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/v/coverthb.jpg"},{"id":485171,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-V"},{"id":485172,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.XML"},{"id":485173,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/v/images/"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The overarching issue we address here is how to extract clear and actionable ecological and management insights from real-world field data that often do not satisfy traditional statistical assumptions. Toward this goal, we developed a general 12+6 step adaptive management framework tool. We applied this framework tool to existing biodiversity monitoring data to create a proof-of-concept result that addresses the overarching question of “why might a specific native stream fish taxon be present or absent at specific locations?” Our multi-step framework tool links established steps and steps that are unique to our framework through weight-of evidence (WOE) integration, an approach that combines quantitative results from multiple visualization and statistical procedures. The systematic use of all steps in our framework can provide improved conservation outcomes compared to a single analysis. Advantages accrue from our approach because our framework tool refines the overarching goal into related sub-questions, applies a specific quantitative procedure to each sub-step, combines results from all sub-questions using a WOE integration, identifies testable questions that elucidate ambiguities and gaps revealed through WOE integration, and proposes practical field methods for obtaining this clarifying information through future research and data collections. The process of considering multiple visualizations and analyses as individual pieces of a shared puzzle offers a new way to approach the use of existing data. Our team-based approach transforms the collection and analysis of existing data into a series of field tests that can guide future actions (e.g., data collection-analysis events, restoration initiatives, research). Habitat and impact regressors will vary with taxa and system, but our structured process tool has broad generality for a range of conservation issues in which freshwater systems are threatened by human impacts.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2025.1520312","usgsCitation":"Rode, O., Mather, M.E., Oliver, D., Nelson, K., Reed, V., Moore, T., and Pratap, S., 2025, A framework tool that applies weight-of-evidence integration to the analysis of existing datasets to guide freshwater conservation: Frontiers in Freshwater Science, v. 3, 1520312, 22 p., https://doi.org/10.3389/ffwsc.2025.1520312.","productDescription":"1520312, 22 p.","ipdsId":"IP-162976","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffwsc.2025.1520312","text":"Publisher Index Page"},{"id":497280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.02756449525691,\n 39.99175254524536\n ],\n [\n -102.02756449525691,\n 37.01810081729228\n ],\n [\n -94.52122428405349,\n 37.01810081729228\n ],\n [\n -94.52122428405349,\n 39.99175254524536\n ],\n [\n -102.02756449525691,\n 39.99175254524536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2025-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rode, Olivia","contributorId":363581,"corporation":false,"usgs":false,"family":"Rode","given":"Olivia","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":951801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":951802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliver, Devon","contributorId":195899,"corporation":false,"usgs":false,"family":"Oliver","given":"Devon","affiliations":[],"preferred":false,"id":951803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Katherine","contributorId":363584,"corporation":false,"usgs":false,"family":"Nelson","given":"Katherine","affiliations":[{"id":86724,"text":"Department of Geography & Geospatial Sciences","active":true,"usgs":false}],"preferred":false,"id":951804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Victoria","contributorId":363585,"corporation":false,"usgs":false,"family":"Reed","given":"Victoria","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":951805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Trisha","contributorId":353634,"corporation":false,"usgs":false,"family":"Moore","given":"Trisha","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":951806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pratap, Suyash","contributorId":363586,"corporation":false,"usgs":false,"family":"Pratap","given":"Suyash","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":951807,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268437,"text":"70268437 - 2025 - Integrating marine historical ecology into management of Alaska’s Pacific cod fishery for climate readiness","interactions":[],"lastModifiedDate":"2025-06-25T14:47:04.627818","indexId":"70268437","displayToPublicDate":"2025-04-29T07:41:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Integrating marine historical ecology into management of Alaska’s Pacific cod fishery for climate readiness","docAbstract":"The Pacific cod (Gadus macrocephalus) fishery was closed in 2020 after a rapid decline in biomass caused by the marine heat waves of 2014–2019. Pacific cod are exceptionally thermally sensitive and management of this fishery is now challenged by increasingly unpredictable climate conditions. Fisheries monitoring is critical for climate readiness, but short-term monitoring data may be inadequate for recognizing and anticipating change under rapid climate changes. We propose an interdisciplinary, marine historical ecology framework that looks to long-term records (local and traditional knowledge, history, archaeology, and paleoclimatology) to capture a long range of ecological variability and provide historical context for management. In order to connect to contemporary fisheries management, this framework must be built on a common vocabulary and an understanding of the key metrics used in fisheries stock assessments. Here, we propose metrics derived from Pacific cod stock assessment and synthesize information relevant to understanding the effects of past warming periods on cod populations across the Gulf of Alaska and Bering Sea. This case study provides a framework for thinking about how to use these historical records in the context of fisheries management under rapidly changing climate conditions.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/icesjms/fsaf056","usgsCitation":"West, C., McClenachan, L., Barbeaux, S.J., Spies, I.B., Addison, J.A., Anderson, B., Hofman, C.A., Reedy, K., Smith, E., Etnier, M., Helser, T.E., and Finney, B., 2025, Integrating marine historical ecology into management of Alaska’s Pacific cod fishery for climate readiness: ICES Journal of Marine Science, v. 82, no. 4, fsaf056, 17 p., https://doi.org/10.1093/icesjms/fsaf056.","productDescription":"fsaf056, 17 p.","ipdsId":"IP-170032","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":491443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsaf056","text":"Publisher Index Page"},{"id":491279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -203.937437508192,\n 54.954559063654074\n ],\n [\n -203.937437508192,\n 50.326282088235786\n ],\n [\n -148.52655859208141,\n 50.326282088235786\n ],\n [\n -148.52655859208141,\n 54.954559063654074\n ],\n [\n -203.937437508192,\n 54.954559063654074\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"West, Catherine F. 0000-0001-5177-9235","orcid":"https://orcid.org/0000-0001-5177-9235","contributorId":345839,"corporation":false,"usgs":false,"family":"West","given":"Catherine F.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":941291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClenachan, Loren","contributorId":260606,"corporation":false,"usgs":false,"family":"McClenachan","given":"Loren","email":"","affiliations":[{"id":51887,"text":"Colby College","active":true,"usgs":false}],"preferred":false,"id":941292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbeaux, Steven J.","contributorId":256680,"corporation":false,"usgs":false,"family":"Barbeaux","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":34572,"text":"NOAA, National Marine Fisheries Service, Alaska Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":941293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spies, Ingrid B.","contributorId":256688,"corporation":false,"usgs":false,"family":"Spies","given":"Ingrid","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":941294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Addison, Jason A. 0000-0003-2416-9743 jaddison@usgs.gov","orcid":"https://orcid.org/0000-0003-2416-9743","contributorId":4192,"corporation":false,"usgs":true,"family":"Addison","given":"Jason","email":"jaddison@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":941295,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Bruce T. 0000-0001-7006-5967","orcid":"https://orcid.org/0000-0001-7006-5967","contributorId":345835,"corporation":false,"usgs":false,"family":"Anderson","given":"Bruce T.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":941296,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hofman, Courtney A.","contributorId":127441,"corporation":false,"usgs":false,"family":"Hofman","given":"Courtney","email":"","middleInitial":"A.","affiliations":[{"id":6997,"text":"Department of Anthropology, Smithsonian Institution National Museum of Natural History (NMNH)","active":true,"usgs":false}],"preferred":false,"id":941297,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reedy, Katherine L.","contributorId":345838,"corporation":false,"usgs":false,"family":"Reedy","given":"Katherine L.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":941298,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Emma A. Elliott 0000-0002-3221-0737","orcid":"https://orcid.org/0000-0002-3221-0737","contributorId":357366,"corporation":false,"usgs":false,"family":"Smith","given":"Emma A. Elliott","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":941299,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Etnier, Michael A. 0000-0002-6546-7854","orcid":"https://orcid.org/0000-0002-6546-7854","contributorId":357369,"corporation":false,"usgs":false,"family":"Etnier","given":"Michael A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":941300,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Helser, Thomas E.","contributorId":203203,"corporation":false,"usgs":false,"family":"Helser","given":"Thomas","email":"","middleInitial":"E.","affiliations":[{"id":36580,"text":"Alaska Fisheries Science Center, National Oceanic and Atmospheric Administration, Seattle, Washington","active":true,"usgs":false}],"preferred":false,"id":941301,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Finney, Bruce P.","contributorId":267775,"corporation":false,"usgs":false,"family":"Finney","given":"Bruce P.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":941302,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70271909,"text":"70271909 - 2025 - Mapping predicted ecological states at landscape scales using remote sensing data and machine learning","interactions":[],"lastModifiedDate":"2025-09-24T15:44:17.513747","indexId":"70271909","displayToPublicDate":"2025-04-28T08:37:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Mapping predicted ecological states at landscape scales using remote sensing data and machine learning","docAbstract":"Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70243","usgsCitation":"Kleist, N.J., Domschke, C.T., Knight, A.C., Nauman, T.W., Duniway, M.C., and Carter, S.K., 2025, Mapping predicted ecological states at landscape scales using remote sensing data and machine learning: Ecosphere, v. 16, no. 4, e70243, 16 p., https://doi.org/10.1002/ecs2.70243.","productDescription":"e70243, 16 p.","ipdsId":"IP-157413","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":496158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70243","text":"Publisher Index Page"},{"id":496018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.0509637142687,\n 42.64630716588371\n ],\n [\n -111.8266792759012,\n 39.96910670756075\n ],\n [\n -114.76249387460862,\n 35.930067724930424\n ],\n [\n -108.04708953433448,\n 35.58278337785393\n ],\n [\n -107.09257565522607,\n 37.663677272255455\n ],\n [\n -107.38346712125988,\n 40.56490356337224\n ],\n [\n -108.74500520215983,\n 41.22226787777939\n ],\n [\n -111.0509637142687,\n 42.64630716588371\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domschke, Christopher T.","contributorId":361734,"corporation":false,"usgs":false,"family":"Domschke","given":"Christopher","middleInitial":"T.","affiliations":[{"id":86338,"text":"Bureau of Land Management, Colorado State Office, 2850 Youngfield St., Lakewood, CO 80215","active":true,"usgs":false}],"preferred":false,"id":949336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W.","contributorId":360619,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis","middleInitial":"W.","affiliations":[{"id":86060,"text":"USDA Natural Resources Conservation Service, Soil and Plant Science Division, Moab, UT, USA","active":true,"usgs":false}],"preferred":false,"id":949338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269031,"text":"70269031 - 2025 - Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","interactions":[],"lastModifiedDate":"2025-07-14T14:48:30.146948","indexId":"70269031","displayToPublicDate":"2025-04-28T07:43:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","docAbstract":"Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (gsc) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven gsc model; (3) a gsc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of gsc, finding that AET models with SIF-driven gsc models correlated well with in-situ measured AET (R2 = 0.64). Modelled AET with dynamic variations of gsc generated lower prediction error (0.85 mm day−1), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2025.133404","usgsCitation":"Gao, S., Nagler, P.L., Woodgate, W., Huete, A., and Doody, T.M., 2025, Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements: Journal of Hydrology, v. 660, no. 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However, planners must quantify the relative magnitude of the CO2 storage resource in these reservoirs to formulate a comprehensive strategy for CO2 mitigation. Even reconnaissance-type estimates of CO2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO2 storage resources in reservoirs capable of hosting CO2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. 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These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.
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Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.
","language":"English","publisher":"Sage Publications","doi":"10.1177/87552930251331981","usgsCitation":"Hussaini, S.M., Karimzadeh, S., Rezaeian, S., and Lourenco, P., 2025, Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake: Earthquake Spectra, v. 41, no. 3, p. 2399-2435, https://doi.org/10.1177/87552930251331981.","productDescription":"37 p.","startPage":"2399","endPage":"2435","ipdsId":"IP-174088","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930251331981","text":"Publisher Index Page"},{"id":493834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","city":"Kahramanmaraş","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hussaini, S. M. Sajad","contributorId":359418,"corporation":false,"usgs":false,"family":"Hussaini","given":"S.","middleInitial":"M. Sajad","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimzadeh, Shaghayegh","contributorId":359419,"corporation":false,"usgs":false,"family":"Karimzadeh","given":"Shaghayegh","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lourenco, Paulo B.","contributorId":359420,"corporation":false,"usgs":false,"family":"Lourenco","given":"Paulo B.","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945291,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269917,"text":"70269917 - 2025 - Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","interactions":[],"lastModifiedDate":"2025-08-07T14:34:46.482133","indexId":"70269917","displayToPublicDate":"2025-04-25T09:23:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","docAbstract":"Batrachochytrium dendrobatidis (Bd) has been decimating amphibian populations globally; previous work indicates that infection risk increases with moisture and thermal mismatch from a host’s optimum. We hypothesized that, in addition to these abiotic influences, mismatch of hosts from their reproductive habitat heightens infection risk via exposure and/or susceptibility mechanisms. We evaluated this “reproductive habitat mismatch hypothesis” by quantifying the interplay of host breeding mode, habitat, and rainfall on Bd infection dynamics using two years of frog survey data—including swab data for 3427 captures representing 44 species—from Brazil’s Atlantic Forest. We modeled infection prevalence, infection intensity, and the number of frogs captured as a function of rainfall, reproductive mode (aquatic or terrestrial), and habitat (aquatic or terrestrial) using hierarchical models. High rainfall was associated with increases in infection prevalence and infection intensity; however, these increases were particularly apparent for species in habitats that were mismatched from the species’ reproductive habitat. Tropical regions experiencing increases in precipitation will likely see higher Bd risk, and our results indicate that such increases in rainfall will be particularly problematic for species that are forced to move from their reproductive habitats by factors such as habitat loss or thermal stress.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03599","usgsCitation":"Gilbert, N.A., Bell, R.C., Catenazzi, A., Martins, R.A., Buttimer, S., Neely, W.J., Lambertini, C., Saenz Calderon, V., Haddad, C.F., Becker, C.G., and DiRenzo, G.V., 2025, Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community: Global Ecology and Conservation, v. 60, e03599, 12 p., https://doi.org/10.1016/j.gecco.2025.e03599.","productDescription":"e03599, 12 p.","ipdsId":"IP-171930","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494048,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TRTVG","text":"USGS data release","linkHelpText":"Code for reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community"},{"id":493796,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03599","text":"Publisher Index Page"},{"id":493706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Parque Estadual da Serra do Mar–Núcleo Santa Virgínia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -45.079106081456246,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.236053619691134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Neil A.","contributorId":359068,"corporation":false,"usgs":false,"family":"Gilbert","given":"Neil","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":944942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Rayna C.","contributorId":359069,"corporation":false,"usgs":false,"family":"Bell","given":"Rayna","middleInitial":"C.","affiliations":[{"id":12937,"text":"California Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":944943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catenazzi, Alessandro","contributorId":359070,"corporation":false,"usgs":false,"family":"Catenazzi","given":"Alessandro","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":944944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martins, Renato A.","contributorId":359071,"corporation":false,"usgs":false,"family":"Martins","given":"Renato","middleInitial":"A.","affiliations":[{"id":85744,"text":"Universidade Federal de São Carlo","active":true,"usgs":false}],"preferred":false,"id":944945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buttimer, Shannon","contributorId":359073,"corporation":false,"usgs":false,"family":"Buttimer","given":"Shannon","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, Wesley J.","contributorId":359075,"corporation":false,"usgs":false,"family":"Neely","given":"Wesley","middleInitial":"J.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":944947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lambertini, Carolina","contributorId":359077,"corporation":false,"usgs":false,"family":"Lambertini","given":"Carolina","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saenz Calderon, Veronica","contributorId":359079,"corporation":false,"usgs":false,"family":"Saenz Calderon","given":"Veronica","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944949,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haddad, Célio F.B.","contributorId":359081,"corporation":false,"usgs":false,"family":"Haddad","given":"Célio","middleInitial":"F.B.","affiliations":[{"id":48854,"text":"Universidade Estadual Paulista","active":true,"usgs":false}],"preferred":false,"id":944950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Becker, C. Guilherme","contributorId":359083,"corporation":false,"usgs":false,"family":"Becker","given":"C.","middleInitial":"Guilherme","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944951,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":944952,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70266162,"text":"70266162 - 2025 - Detection of landslide-generated tsunami by shipborne GNSS precise point positioning","interactions":[],"lastModifiedDate":"2025-04-29T14:12:46.499973","indexId":"70266162","displayToPublicDate":"2025-04-25T09:08:24","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":"Detection of landslide-generated tsunami by shipborne GNSS precise point positioning","docAbstract":"Precise point positioning (PPP) of ships using Global Navigation Satellite System (GNSS) data reveals the precise movements of marine vessels. This method may quantify anomalies in sea surface height with implications for oceanographic monitoring, exploration, and tsunami warning. The GNSS PPP data from the R/V Sikuliaq, a research ship of the University of Alaska Fairbanks, were processed to detect a small local tsunami generated by the Lowell Point landslide, which occurred near Seward, Alaska, on 8 May 2022 (UTC). The GNSS receiver aboard the R/V Sikuliaq recorded the waves generated by the landslide, with a maximum wave amplitude of 6 cm and wave periods between 40 and 50 s. These results are consistent with simulations of the landslide event.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112472","usgsCitation":"Manaster, A., Sheehan, A.F., Goldberg, D.E., Barnhart, K.R., and Roth, E., 2025, Detection of landslide-generated tsunami by shipborne GNSS precise point positioning: Geophysical Research Letters, v. 52, e2024GL112472, 9 p., https://doi.org/10.1029/2024GL112472.","productDescription":"e2024GL112472, 9 p.","ipdsId":"IP-169782","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487831,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112472","text":"Publisher Index Page"},{"id":485128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Seward","otherGeospatial":"Resurrection Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.60008399844827,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 60.18626730817803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Manaster, Adam E. 0000-0001-8183-4274","orcid":"https://orcid.org/0000-0001-8183-4274","contributorId":215663,"corporation":false,"usgs":true,"family":"Manaster","given":"Adam E.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheehan, Anne F 0000-0002-9629-1687","orcid":"https://orcid.org/0000-0002-9629-1687","contributorId":224234,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","email":"","middleInitial":"F","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":934769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":934770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":934771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Ethan F.","contributorId":353914,"corporation":false,"usgs":false,"family":"Roth","given":"Ethan F.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":934772,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273501,"text":"70273501 - 2025 - Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","interactions":[],"lastModifiedDate":"2026-01-20T15:43:08.696041","indexId":"70273501","displayToPublicDate":"2025-04-25T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","docAbstract":"Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.
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We developed a set of 54 microsatellite loci to assess genetic structure and diversity across the United States range of the Light-footed Ridgway’s Rail (Rallus obsoletus levipes), a federally endangered marsh bird for which populations have been augmented by a captive breeding program annually since 2001. We identified three regional genetic clusters, with the highest genetic diversity reported in the central cluster, which included all sampled wetlands in north San Diego County. Recent (2019–24) captive-breeding adults all clustered within the northernmost cluster (Orange and Ventura Counties), which was expected given that this cluster included the source wetland for the captive breeding program. Gene flow rates, which approximate the proportions of individuals in a population originating from other populations, were relatively high among clusters (4–24 percent) and may have been enhanced through the release of captive-bred rails. Based on the genetic data analyzed in a genetic rescue decision framework, sourcing new breeding birds from the north San Diego County cluster could provide the greatest genetic diversity benefits. The northernmost cluster, which included Mugu Lagoon and all sampled Orange County wetlands, was considered the most in need of genetic rescue. Recent breeding pairs in the captive breeding program have comparatively low diversity and high interrelatedness. Sourcing birds from wetlands with high genetic diversity and population sizes, assessing genetic relatedness before pairing, and focusing releases in areas that have low estimates of genetic diversity could improve the distribution of genetic diversity across wild populations in the future.
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The Rocky Mountains and the Colorado River Basin in the Western United States are complex, interconnected systems that sustain a large variety of species, including tens of millions of humans. These regions face risks from drought, wildfires, invasive plant and animal species, and habitat reduction. Working with many stakeholders, scientists can help to characterize these risks by providing data and analytical tools to inform land and water resource management decisions.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253008","usgsCitation":"Andrews, W.J., Titus, T.N., Eng, L.E., Zellman, K.L., Anderson, P.J., and Havens, J.C., 2025, U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs: U.S. Geological Survey Fact Sheet 2025-3008, 4 p., https://doi.org/10.3133/fs20253008.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-167985","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":485159,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3008"},{"id":484983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3008/coverthb.jpg"},{"id":484985,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3008"},{"id":485041,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3008/images"},{"id":485042,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.xml"}],"contact":"Director, Rocky Mountain Region
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Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
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