Consumptive use of freshwater is of concern in many estuarine ecosystems, and various frameworks have been used to prescribe environmental flows to benefit native species. However, few of these frameworks explicitly examine the potential trade-offs between socioeconomic and conservation-oriented values. This is exemplified in California, USA, where freshwater management has been an area of focus and controversy. Operations of numerous reservoirs and water diversion facilities distributed throughout the state, while critical for economic and public health benefits, have contributed to the decline of many native species. The endangered delta smelt (Hypomesus transpacificus) is endemic to the Sacramento-San Joaquin Delta, the heart of California's complex water conveyance system. To aid recovery of delta smelt, fall-timed freshwater pulse flows were implemented, which require water to be either released from reservoirs, or made unavailable to export for consumptive uses. Previous research has indicated that the effectiveness of the current pulse flow action could be improved by reconsidering the timing and magnitude; however, uncertainties in the predicted fish response to flow pulses may hinder decision-making about flow management. Using a water resource planning model, different iterations of an individual-based life cycle model, and decision analysis tools, we assessed the importance of sources of uncertainty to hypothetical flow management decisions, including uncertainty surrounding the predicted responses in delta smelt population growth rates, and variability of decision-maker's values. We found both the choice of which (if any) flow action to take for delta smelt, and the expected value of further research, depended on how decision-makers weight the delta smelt and water supply objectives. There was expected value of information (VOI) only if a decision-maker weighted the delta smelt objective ≥0.59, and within this range, research to improve estimates of changes in delta smelt prey items related to flow actions could be prioritized over other sources of uncertainty to improve outcomes of decision-making. Our study demonstrates how uncertainty, even if large, may not be equally relevant to different decision-makers (e.g., with different agency missions), and how VOI analysis can be used to guide management in an overallocated water system such as California.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70558","usgsCitation":"Mahardja, B., Smith, W.E., Healy, B.D., Koizumi, C., Nobriga, M.L., Acuña, S., Crawford, B., Arend, K.K., and Runge, M.C., 2026, The effects of scientific uncertainty and values trade-offs on flow management decisions for an endangered fish: Ecosphere, v. 17, no. 3, e70558, 19 p., https://doi.org/10.1002/ecs2.70558.","productDescription":"e70558, 19 p.","ipdsId":"IP-179082","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":502057,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70558","text":"Publisher Index Page"},{"id":501969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.70835642002126,\n 38.292492080303305\n ],\n [\n -122.70835642002126,\n 36.78504888193622\n ],\n [\n -120.74800512414426,\n 36.78504888193622\n ],\n [\n -120.74800512414426,\n 38.292492080303305\n ],\n [\n -122.70835642002126,\n 38.292492080303305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mahardja, Brian","contributorId":174645,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","email":"","affiliations":[{"id":13461,"text":"U.C. 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One of the strongest drivers for these declines is woody plant encroachment of grasslands. In the Great Plains and Sagebrush biomes of North America, spatial covariance—a remote-sensing metric for tracking boundaries between vegetation types—is emerging as a new method to identify and strategize conservation of grassland cores in the face of woody plant encroachment. However, the relationship between spatial covariance and grassland bird community occupancy is unknown. Here, we used Bayesian multispecies occupancy models to understand how occupancy probability of six declining grassland species responded to spatial covariance at three scales (0.81, 7.29, and 65.61 ha) and tree cover in fragmented grasslands of Arkansas, USA. Model selection revealed that the smallest spatial scale (0.81 ha) best explained grassland bird occupancy. Tree cover alone was a poor predictor of grassland bird occupancy compared to models that included spatial covariance at the 0.81- and 7.29-ha scales. Grassland bird occupancy declined at tree-grass boundaries (negative spatial covariance at the 0.81-ha scale) and increased in grassland cores (near-zero or slightly positive spatial covariance at the 0.81-ha scale). At low tree cover, Dickcissel (Spiza americana), Eastern Kingbird (Tyrannus tyrannus), Loggerhead Shrike (Lanius ludovicianus), Northern Bobwhite (Colinus virginianus), and Scissor-tailed Flycatcher (Tyrannus forficatus) occupancy probability more than doubled in grassland cores (where spatial covariance approached zero). Eastern Meadowlark (Sturnella magna) had the weakest relationship with spatial covariance. Our results suggest that spatial covariance can identify grassland cores and serve as a powerful predictor of grassland bird community occupancy, even in highly fragmented grasslands. Identifying grassland cores empowers defending core grasslands from woody plant encroachment and then growing cores via active restoration.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70515","usgsCitation":"Berry, L.L., DeGregorio, B.A., Uden, D.R., and Roberts, C.P., 2026, Finding the (small) cores: Spatial covariance tracks grassland bird community occupancy in fragmented grasslands: Ecosphere, v. 17, no. 3, e70515, 12 p., https://doi.org/10.1002/ecs2.70515.","productDescription":"e70515, 12 p.","ipdsId":"IP-167761","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70515","text":"Publisher Index Page"},{"id":502026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Bald Knob National Wildlife Refuge, Cache River National Wildlife Refuge, Camp Robinson Special Use Area, Holla Bend National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.55982952388516,\n 35.69384138213361\n ],\n [\n -91.55982952388516,\n 35.08983573060459\n ],\n [\n -90.1524295998061,\n 35.08983573060459\n ],\n [\n -90.1524295998061,\n 35.69384138213361\n ],\n [\n -91.55982952388516,\n 35.69384138213361\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Lauren L.","contributorId":369145,"corporation":false,"usgs":false,"family":"Berry","given":"Lauren","middleInitial":"L.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uden, Daniel R.","contributorId":369146,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","middleInitial":"R.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":958534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958535,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274216,"text":"70274216 - 2026 - Groundwater drought in the United States: Spatial and temporal variability","interactions":[],"lastModifiedDate":"2026-03-13T15:11:23.354627","indexId":"70274216","displayToPublicDate":"2026-03-11T10:03:16","publicationYear":"2026","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":"Groundwater drought in the United States: Spatial and temporal variability","docAbstract":"Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.
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distribution","interactions":[],"lastModifiedDate":"2026-04-02T18:44:26.919721","indexId":"70274632","displayToPublicDate":"2026-03-10T11:32:48","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution","title":"Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution","docAbstract":"Hydrologic variation is a primary driver of stream ecosystems. Changing hydrology can lead to assemblage shifts and alterations in suitable habitat for freshwater species. As climate change is predicted to alter flow patterns in addition to increasing water temperatures, insight into relationships between species occupancy, hydrology, and temperature is critical for understanding current and future distributions. We examined how hydrologic variability, temperature, and other environmental variables interact to influence Micropterus dolomieu (Smallmouth Bass) occurrence. We used Spatial Stream Network models, allowing for the incorporation of spatial autocorrelation along streams' unique dendritic network, to examine Smallmouth Bass occupancy across a range of hydrologic variation in the Ozark-Ouachita Interior Highlands, USA. Hydrologic variation was the main driver of Smallmouth Bass occurrence, with occurrence more likely in groundwater streams with low hydrologic variation and high flow permanence. For groundwater streams, occurrence was positively associated with summer stream temperature and negatively associated with annual stream temperature. As variation increased, more variables showed significant relationships with occurrence. Distance metrics were important for all models, however as hydrologic disturbance increased, flow connected distance played a lesser role and stream distance played a greater role. Hydrologic variability was the overarching determinant of Smallmouth Bass occurrence and strongly influenced the predictive importance of environmental variables and geospatial relationships. Greater hydrologic variability resulted in stronger statistical relationships between occurrence and environmental variables and an increased importance of system connectivity. As climate change alters hydrologic processes and streams become more variable, understanding and accounting for these shifting relationships is essential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2026.181562","usgsCitation":"Sorensen, S.F., Fox, J.T., and Magoulick, D.D., 2026, Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution: Science of the Total Environment, v. 1025, 181562, 9 p., https://doi.org/10.1016/j.scitotenv.2026.181562.","productDescription":"181562, 9 p.","ipdsId":"IP-176491","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2026.181562","text":"Publisher Index Page"},{"id":502032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark-Ouachita Interior Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.06835076729865,\n 37.54392591075137\n ],\n [\n -95.58753725694291,\n 35.616131979244244\n ],\n [\n -96.86430792379102,\n 34.30485408838467\n ],\n [\n -95.13839254168458,\n 34.13182914589751\n ],\n [\n -93.02844126052034,\n 33.84485206480821\n ],\n [\n -91.20526644252189,\n 35.93662462412837\n ],\n [\n -90.46426221649432,\n 38.03635872039271\n ],\n [\n -95.06835076729865,\n 37.54392591075137\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1025","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sorensen, Sarah F.","contributorId":369126,"corporation":false,"usgs":false,"family":"Sorensen","given":"Sarah","middleInitial":"F.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, J. Tyler","contributorId":369127,"corporation":false,"usgs":false,"family":"Fox","given":"J.","middleInitial":"Tyler","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","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":958497,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274135,"text":"70274135 - 2026 - Alternative future vegetation pathways reveal potential transformations of western US ecosystems","interactions":[],"lastModifiedDate":"2026-03-12T16:41:27.715088","indexId":"70274135","displayToPublicDate":"2026-03-09T11:34:07","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Alternative future vegetation pathways reveal potential transformations of western US ecosystems","docAbstract":"Managing ecosystems in an era of rapid change is inherently challenging not only because of uncertainty in future climate but also due to diverse responses of ecosystems to climate. Projections of ecological transformation alongside information about plausible vegetation trajectories can help land managers explore divergent scenarios and consider how modeled outcomes match their observations. Climate-analog impact models (AIMs) compare environmental information (e.g., vegetation types) between sets of climatically similar locations to infer change and can be used to identify multiple outcomes. We used AIMs to project changes in vegetation across the western United States under a mid-21st century climate scenario, characterize ecological transformation vulnerability based on projection divergence, and demonstrate how AIMs can inform decision-making. We projected high or very high vulnerability to ecological transformation across 29% of the western US, nearly 1 M km2. Vulnerability varied among vegetation groups; 75% of alpine vegetation had high or very high vulnerability vs. 6% of desert scrub. We estimate that 9% of the study area faces a high likelihood of transformation based on combined measures of vulnerability and projection agreement. Transformation at the vegetation type (n = 50) level is projected for 40% (1.4 M km2) of the study area, based on primary projections. As vegetation shifts towards types supported by a more arid climate, forested area is expected to contract by 9% and subalpine forests specifically by 54%. Elsewhere, vulnerability is low or trajectories are uncertain, implying opportunities for managers to intervene. Dry forests, for example, could be stabilized through vegetation management and intentional fire use. Our findings suggest likely ecological transformations with significant downstream consequences for ecosystem services and natural resources. They are best used within decision-making frameworks that draw on multiple lines of evidence including local expertise and complementary knowledge systems.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.70795","usgsCitation":"Hoecker, T.J., Davis, K.T., Littlefield, C.E., Chandler, J.C., Parks, S.A., Maguire, A., Kemp, K., Yegorova, S., and Dobrowski, S., 2026, Alternative future vegetation pathways reveal potential transformations of western US ecosystems: Global Change Biology, v. 32, no. 3, e70795, 15 p., https://doi.org/10.1111/gcb.70795.","productDescription":"e70795, 15 p.","ipdsId":"IP-182529","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":501100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.70795","text":"Publisher Index Page"},{"id":500990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","volume":"32","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoecker, Tyler J. 0000-0001-8680-8809","orcid":"https://orcid.org/0000-0001-8680-8809","contributorId":367051,"corporation":false,"usgs":false,"family":"Hoecker","given":"Tyler","middleInitial":"J.","affiliations":[{"id":84304,"text":"Vibrant Planet","active":true,"usgs":false}],"preferred":false,"id":956646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Kimberley T. 0000-0001-9727-374X","orcid":"https://orcid.org/0000-0001-9727-374X","contributorId":355031,"corporation":false,"usgs":false,"family":"Davis","given":"Kimberley","middleInitial":"T.","affiliations":[{"id":84700,"text":"USDA - Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":956647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Littlefield, Caitlin E. 0000-0003-3771-7956","orcid":"https://orcid.org/0000-0003-3771-7956","contributorId":220623,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":956648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandler, Jeffrey C","contributorId":223870,"corporation":false,"usgs":false,"family":"Chandler","given":"Jeffrey","email":"","middleInitial":"C","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":956649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parks, Sean A. 0000-0002-2982-5255","orcid":"https://orcid.org/0000-0002-2982-5255","contributorId":225035,"corporation":false,"usgs":false,"family":"Parks","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":41024,"text":"Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":956650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maguire, Andy John 0000-0002-6334-0497","orcid":"https://orcid.org/0000-0002-6334-0497","contributorId":358945,"corporation":false,"usgs":true,"family":"Maguire","given":"Andy John","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":956651,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kemp, Kerry","contributorId":367059,"corporation":false,"usgs":false,"family":"Kemp","given":"Kerry","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":956652,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yegorova, Svetlana 0000-0003-3228-7379","orcid":"https://orcid.org/0000-0003-3228-7379","contributorId":367060,"corporation":false,"usgs":false,"family":"Yegorova","given":"Svetlana","affiliations":[{"id":34255,"text":"Wilfred Laurier University","active":true,"usgs":false}],"preferred":false,"id":956653,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dobrowski, Solomon","contributorId":229621,"corporation":false,"usgs":false,"family":"Dobrowski","given":"Solomon","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":956654,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274277,"text":"70274277 - 2026 - Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","interactions":[],"lastModifiedDate":"2026-03-24T17:12:07.583859","indexId":"70274277","displayToPublicDate":"2026-03-07T10:02:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","docAbstract":"Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2026.114741","usgsCitation":"Norman, L., Petrakis, R.E., Wilson, N.R., Middleton, B.R., Villarreal, M.L., Pollock, M., Minckley, T.A., and Hendrickson, D., 2026, Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach: Ecological Indicators, v. 184, 114741, 16 p., https://doi.org/10.1016/j.ecolind.2026.114741.","productDescription":"114741, 16 p.","ipdsId":"IP-179305","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":501684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2026.114741","text":"Publisher Index Page"},{"id":501477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, New Mexico","otherGeospatial":"Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.05152972005978,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 33.0768867725987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":214982,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, Barry R.","contributorId":367728,"corporation":false,"usgs":false,"family":"Middleton","given":"Barry","middleInitial":"R.","affiliations":[{"id":36921,"text":"Ret. USGS","active":true,"usgs":false}],"preferred":false,"id":957550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":214980,"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":957551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pollock, Michael","contributorId":367729,"corporation":false,"usgs":false,"family":"Pollock","given":"Michael","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":957552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minckley, Thomas A.","contributorId":367730,"corporation":false,"usgs":false,"family":"Minckley","given":"Thomas","middleInitial":"A.","affiliations":[{"id":87617,"text":"University of Wyoming, Department of Geology and Geophysics, Laramie, WY 82071-2000","active":true,"usgs":false}],"preferred":false,"id":957553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hendrickson, Dean","contributorId":367731,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Dean","affiliations":[{"id":87618,"text":"University of Texas at Austin, College of Natural Sciences, Austin, TX 78712","active":true,"usgs":false}],"preferred":false,"id":957554,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274196,"text":"ofr20261063 - 2026 - Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","interactions":[],"lastModifiedDate":"2026-03-06T21:45:10.353284","indexId":"ofr20261063","displayToPublicDate":"2026-03-06T11:20:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-1063","displayTitle":"Evaluation of Turbidity Corrections for EXO Fluorescent Dissolved Organic Matter (fDOM) Sensors","title":"Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","docAbstract":"The use of field-deployable fluorescence sensors to better understand dissolved organic matter concentrations and composition has grown immensely in recent years. Applications of these sensors to critical monitoring efforts have also grown, encompassing post-fire monitoring, wastewater tracking, and use as a proxy for various contaminants. Despite the growth, it is well known that these sensors require corrections for temperature (Watras and others, 2011) and are subject to many light-field interferences caused by both scattering and absorbance due to dissolved and particulate substances (Downing and others, 2012; Lee and others, 2015; Booth and others, 2023). The most common fluorescence sensors used by the U.S. Geological Survey (USGS) include those targeting fluorescent dissolved organic matter (fDOM) and chlorophylls. Because fDOM sensors primarily measure fluorescence in the dissolved to colloidal phases, corrections to the interferences caused by particulates can be made relatively easily. By the end of 2024, the USGS had 69 fDOM sensors deployed within official water quality monitoring networks included on the USGS National Water Dashboard (https://dashboard.waterdata.usgs.gov/app/nwd/en/) and numerous others used in surveys and research applications across the Nation.
Although temperature corrections are widely applicable across sensor models, interference corrections can be model specific due to differences in design specifications across manufacturers and models (Booth and others, 2023). The corrections are also potentially subject to changes in manufacturing within a specific sensor model. Recently, USGS staff obtained information regarding possible changes in the manufacturing of its most widely-used fDOM sensor model, raising concerns about data consistency and quality in the USGS fDOM sensor networks.
Furthermore, changes in turbidity sensors since the corrections guidance was performed may also affect the performance of the corrections. The turbidity sensor used in the original experiments (Downing and others, 2012) was determined to have a signal output approximately 1.3 times higher than the output of the turbidity sensor currently used in an extensive field comparison study (Messner and others, 2023). With these changes, it is imperative that the corrections be reevaluated to maintain data consistency and continuity across the USGS.
In this study, we evaluated turbidity corrections for fDOM sensors over a range of serial numbers covering manufacturing dates 2015 through 2022 and turbidity serial numbers covering the range 2013 through 2022. The goal was to determine whether reported changes in the manufacturing process of the fDOM and turbidity sensors affected the correction approach developed by Downing and others (2012) such that additional guidance would be required to address this manufacturing change. To evaluate, we repeated a laboratory-based test similar to that performed by Downing and others (2012) in which a series of tank experiments with multiple sensors were deployed in a suspension of Elliot Silt Loam (ESL). High turbidities of the ESL suspension were maintained throughout the tank by turbulent recirculation using submersible pumps. Particulates were removed using a recirculated line equipped with a capsule filter (0.45 micron). Measurements were collected throughout the filtration until turbidities reached approximately 5 formazin nephelometric units (FNU; data available in Baxter and others, 2023). Each experimental run included a mixture of unique sensor combinations to account for variability imposed by the turbidity and temperature sensors. The fDOM correction factor was calculated for each combination of fDOM and turbidity sensors included in the test.
We observed no systematic change in fDOM correction coefficients across serial numbers representing manufacturing years 2015 through 2022. However, the results highlighted questions raised about the corrections for high-turbidity samples, as noted in USGS Techniques and Methods (Booth and others, 2023). Applying the inverse of the commonly-used fDOM ratio with a quadratic fit performed better than the exponential fits when correcting fDOM data for turbidity in the ESL laboratory filtration test and generated a simple scale factor correction equation. This approach also served as a better indicator of data quality than the exponential fit approach. Similar to fDOM, more rigorous quality assurance measures may be necessary to evaluate turbidity sensor calibrations and performance. Sensors exceeding a certain age may need to be replaced despite passing quality assurance checks during calibration. Further testing of the turbidity corrections for different sediment and water types is warranted to better understand the variations in the fits and correctable ranges of turbidity in different systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261063","programNote":"Water Resources Mission Area","usgsCitation":"Fleck, J.A., Baxter, T.J., and Hansen, A.M., 2026, Evaluation of turbidity corrections for fluorescent dissolved organic matter (fDOM) sensors: U.S. Geological Survey Open-File Report 2026–1063, 30 p., https://doi.org/10.3133/ofr20261063.","productDescription":"Report: vi, 30 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-171907","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500842,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1063/coverthb.jpg"},{"id":500843,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1063 PDF"},{"id":500844,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261063/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1063 HTML"},{"id":500845,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1063 XML"},{"id":500846,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1063/images"},{"id":500847,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OB430E","text":"USGS data release","linkHelpText":"Fluorescence sensor measurements in sediment suspensions to evaluate turbidity corrections"}],"contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates.
We identified two machine learning approaches that shared the ability to model overdispersed probabilities. We compared the BBR and BMF machine learning models to a BME model to evaluate precision in detection probability prediction and model performance on bias in parameter estimation. We tested our three candidate models using a simulation study to understand the specific advantages and disadvantages of each when the data set was increasingly sparse and the capture probabilities were realistically small. We then applied the models to a case study of RST data from the Klamath River in California, United States.
The BME and BMF outperformed BBR in all simulated scenarios, although the BMF displayed poor explanatory power. In the case study, the BME and BMF identified environmental covariates that predicted RST efficiency.
Using the BME as a benchmark for comparing machine learning approaches to trap efficiency modeling, our simulations and case study demonstrated that the BMF performed well and is a viable modeling approach with strong predictive power. The BME model would be the preferred modeling approach when its strong explanatory power is desired.
Restoration of impaired floodplains is an increasingly prevalent strategy for alleviating water quality concerns and reducing downstream flooding at watershed scales. Floodplains temporarily store water and slow flow velocity to promote sedimentation during overbank flooding and remove inorganic nitrogen from floodwater and groundwater via denitrification. Evaluating the impacts of different restoration strategies on denitrification can inform more strategic investments into floodplain modifications that improve water quality outcomes. Our research investigates how denitrification rates in floodplains respond to environmental factors that are actionable from an engineering perspective through design and water resources management. We seasonally measured soil denitrification enzyme activity and various environmental characteristics in 4 floodplains with different restoration design and management approaches at the confluence of the Wabash and Tippecanoe Rivers in Indiana, United States. Our results showed that denitrification rates in an agricultural floodplain were significantly lower than in restored floodplains with native vegetation. Certain soil conditions characteristic of floodplain wetlands were associated with higher denitrification, particularly elevated total nitrogen, moisture, silt, and organic matter contents. Vegetation species composition was correlated with denitrification rates. This link may reflect the direct effects of vegetation on soil conditions, such as supplying labile organic carbon, or indirect effects, such as vegetation acting as an indicator of hydrologic regime and land use. Denitrification seasonally varied, peaking in winter when nitrate supply from rivers draining agricultural watersheds in the region is also high. Substrate limitation of soil denitrification enzyme activity was most significant during the summer when overbank flooding, which replenishes soil nitrogen stocks, rarely occurs. Our findings indicate that denitrification capacity will likely be maximized in riverine floodplains that are restored as wetlands with diverse native vegetation and enhanced hydrologic connectivity. Such restoration activities promote higher denitrification rates via elevated moisture, fine sediment deposition, and soil organic matter.
","language":"English","publisher":"University of Vermont Press","doi":"10.70793/jeed.13","usgsCitation":"Lay, D.W., McMillan, S.W., Hosen, J.D., Dey, S., and Noe, G.E., 2026, Assessing environmental drivers of denitrification in restored riverine floodplains: Journal of Ecological Engineering Design, v. 4, no. 1, 17 p., https://doi.org/10.70793/jeed.13.","productDescription":"17 p.","ipdsId":"IP-179949","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.70793/jeed.13","text":"Publisher Index Page"},{"id":502206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Tippecanoe River, Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.73421342718163,\n 40.54578001376902\n ],\n [\n -86.77854172394578,\n 40.56893409536539\n ],\n [\n -86.85226822336595,\n 40.486689152088985\n ],\n [\n -86.83272067583472,\n 40.46994703338217\n ],\n [\n -86.73421342718163,\n 40.54578001376902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lay, Danielle Winter","contributorId":369252,"corporation":false,"usgs":false,"family":"Lay","given":"Danielle","middleInitial":"Winter","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMillan, Sara W.","contributorId":369253,"corporation":false,"usgs":false,"family":"McMillan","given":"Sara","middleInitial":"W.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":958692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hosen, Jacob D.","contributorId":369254,"corporation":false,"usgs":false,"family":"Hosen","given":"Jacob","middleInitial":"D.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dey, Sayan","contributorId":369255,"corporation":false,"usgs":false,"family":"Dey","given":"Sayan","affiliations":[{"id":30787,"text":"Saint Louis University","active":true,"usgs":false}],"preferred":false,"id":958694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":958695,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70275205,"text":"70275205 - 2026 - Stream macroinvertebrate responses vary with region, land use and management practice type","interactions":[],"lastModifiedDate":"2026-04-22T14:34:11.340833","indexId":"70275205","displayToPublicDate":"2026-03-06T09:21:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Stream macroinvertebrate responses vary with region, land use and management practice type","docAbstract":"Intensive land use alters hydrology and water quality, threatening freshwater benthic macroinvertebrates. Over 200,000 management practices (MPs) have been implemented across the Chesapeake Bay watershed since the 1980s, yet biological responses remain inconsistent. We synthesized 29 studies from 4 physiographic provinces covering 8 MP categories and evaluated macroinvertebrate responses along MP gradients using structural (richness), functional (biomass), tolerance, and biotic metrics. We hypothesized that MPs enhancing habitat complexity or restoring flow regimes would benefit taxa sensitive to sediment, hydrologic instability and organic pollution, with outcomes shaped by regional context, land use, and chosen metrics. Four themes emerged. (i) Agricultural Riparian Forest Buffers (RFBs) consistently improved sensitive metrics related to abundance, biomass and richness. (ii) Urban streams with Stream Habitat Improvement and Management (SHIM) showed improved richness and diversity, but biomass and tolerance metrics declined or remained neutral, indicating unresolved hydrologic and pollutant stress. (iii) Structural and functional responses diverged: effect sizes for total and feeding-group biomasses (functional metrics) were negative, whereas genus-level Ephemeroptera-Plecoptera-Trichoptera (EPT) richness (structural metric) was positive, indicating that structural shifts may not track underlying production changes. (iv) Physiographic comparisons showed counterintuitive patterns, as RFBs improved EPT richness in Piedmont streams but had negative effects in the Coastal Plain. Evaluating MP effectiveness requires distinguishing a no-MP pathway (stressors → instream conditions → assemblages → responses) from an MP-mediated pathway (practice regime → modified stressors → instream conditions → assemblages → responses), underscoring the need for region-specific, multi-metric monitoring and improved understanding of MP density thresholds and recovery lags.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2026.129172","collaboration":"Virginia Tech, USGS","usgsCitation":"Sabat-Bonilla, S.A., Belvin, A.C., Noe, G.E., Maloney, K.O., Frimpong, E.A., Angermeier, P., and Entrekin. Sally E., 2026, Stream macroinvertebrate responses vary with region, land use and management practice type: Journal of Environmental Management, v. 403, 129172, 14 p., https://doi.org/10.1016/j.jenvman.2026.129172.","productDescription":"129172, 14 p.","ipdsId":"IP-181470","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":503441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2026.129172","text":"Publisher Index Page"},{"id":503299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.0436249,\n 29.3328733\n ],\n [\n -99.6425005,\n 27.5074215\n ],\n [\n -98.8640981,\n 26.211199\n ],\n [\n -97.3591867,\n 25.8848423\n ],\n [\n -96.9440387,\n 27.8291374\n ],\n [\n -93.6747485,\n 29.4233134\n ],\n [\n -89.2638014,\n 28.9703115\n ],\n [\n -85.7350438,\n 29.5136731\n ],\n [\n -84.0744519,\n 29.6490614\n ],\n [\n -81.7392446,\n 25.0885121\n ],\n [\n -80.1824398,\n 24.9003778\n ],\n [\n -79.8191853,\n 26.5366431\n ],\n [\n -81.1684162,\n 31.3037444\n ],\n [\n -75.0968772,\n 35.2926383\n ],\n [\n -75.4082382,\n 37.5061126\n ],\n [\n -73.2287114,\n 40.0542222\n ],\n [\n -71.9832675,\n 41.0008498\n ],\n [\n -69.3885927,\n 41.5856895\n ],\n [\n -70.0632082,\n 42.2421607\n ],\n [\n -72.450309,\n 41.1573197\n ],\n [\n -73.5400724,\n 41.1963789\n ],\n [\n -73.6438594,\n 43.1574323\n ],\n [\n -77.6396585,\n 43.1574323\n ],\n [\n -81.7911381,\n 41.2744275\n ],\n [\n -86.3577657,\n 34.4838855\n ],\n [\n -88.5372925,\n 37.5884002\n ],\n [\n -92.5849851,\n 34.5266496\n ],\n [\n -97.9300151,\n 34.6121119\n ],\n [\n -98.8122046,\n 31.4809337\n ],\n [\n -101.0436249,\n 29.3328733\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"403","noUsgsAuthors":false,"publicationDate":"2026-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Sabat-Bonilla, Sergio A.","contributorId":370289,"corporation":false,"usgs":false,"family":"Sabat-Bonilla","given":"Sergio","middleInitial":"A.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belvin, Abigail C.","contributorId":370290,"corporation":false,"usgs":false,"family":"Belvin","given":"Abigail","middleInitial":"C.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":960118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":960119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frimpong, Emmanuel A.","contributorId":370293,"corporation":false,"usgs":false,"family":"Frimpong","given":"Emmanuel","middleInitial":"A.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Angermeier, Paul L. 0000-0003-2864-170X","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":204519,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":960121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Entrekin. Sally E.","contributorId":370299,"corporation":false,"usgs":false,"family":"Entrekin. Sally E.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":960122,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274543,"text":"70274543 - 2026 - Working group on American Eel (WGAMEEL; outputs from 2024 meeting)","interactions":[],"lastModifiedDate":"2026-03-31T14:32:51.150531","indexId":"70274543","displayToPublicDate":"2026-03-06T08:54:37","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":23776,"text":"ICES Scientific Reports","active":true,"publicationSubtype":{"id":3}},"title":"Working group on American Eel (WGAMEEL; outputs from 2024 meeting)","docAbstract":"The Working Group on American Eel (WGAMEEL) met virtually three times in 2022-2024 to address the five Terms of Reference (ToRs) of its three-year term. The first two ToRs tasked WGAMEEL with listing and evaluating data on American eel landings, abundance indices, and spatial and habitat data and also to describe assessment methods used in Canada and the US. Canada-wide American eel trajectory was estimated using 12 fishery-independent datasets. Generally, longer datasets had more negative trends than shorter ones. Limiting data to the post-2000 years produced fewer negative trends that did not differ from zero, suggesting the observed declines occurred pre-2000. Spatial modelling for American eel requires knowledge and mapping that covers the breadth of habitat types occupied by the species, including freshwater, estuarine, and marine environments. In recent years there has been an expansion of online databases with data from the aquatic environment, particularly in freshwater, with estuarine and marine data less consistently documented. This report broadly compiles abiotic data series of relevance to American eel. A larger challenge for spatial modelling will be acquiring enough high quality, georeferenced biological data sets with suitable observations to assess occurrence, abundance, and trends over time in a spatial framework.
The third ToR considered Indigenous Knowledge Systems for American eel. A survey reaching First Nations representatives from four Canadian provinces confirmed the cultural importance of eels in Indigenous communities, and that Indigenous knowledge possessed by the participant groups are place-based and contextual, especially regarding threats impacting eels.
The final two ToRs focused on identifying stock assessment modelling approaches applicable to American and European eel, and assessing whether any of these approaches might be appropriate for American eel management moving forward. WGAMEEL evaluated the various approaches for assessing American eel or providing management advice. Two approaches that could be completed in the next few years because of their minimal data needs are index-based methods and catch-only method. A suite of approaches considered by WGAMEEL that would take more time and data were spatial or habitat models, management strategy evaluation, and spawner-per-recruit (SPR) models potentially paired with meta-population models.
","language":"English","publisher":"International Council for the Exploration of the Sea","doi":"10.17895/ices.pub.31538731","usgsCitation":"April, J., Anstead, K., Brodeur, P., Cairns, D., Castonguay, M., Cieri, M., Jessop, B., D'Astous, A., Denny, S., Dumont, J., Eyler, S., Koops, M.A., Lee, L., Landry-Massicote, L., Maxwell, R., Pratt, T., Reid, S.M., Roloson, S., Schlueter, S.L., Snyder, S., and Young, J.A., 2026, Working group on American Eel (WGAMEEL; outputs from 2024 meeting): ICES Scientific Reports, v. 8, no. 15, 68 p., https://doi.org/10.17895/ices.pub.31538731.","productDescription":"68 p.","ipdsId":"IP-174599","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":501856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"15","noUsgsAuthors":false,"publicationDate":"2026-03-06","publicationStatus":"PW","contributors":{"editors":[{"text":"Anstead, Kristen A.","contributorId":329847,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":958372,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Pratt, Thomas","contributorId":347389,"corporation":false,"usgs":false,"family":"Pratt","given":"Thomas","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":958373,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"April, Julien","contributorId":369015,"corporation":false,"usgs":false,"family":"April","given":"Julien","affiliations":[{"id":87698,"text":"Ministère de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs","active":true,"usgs":false}],"preferred":false,"id":958374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anstead, Kristen 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M.","contributorId":347268,"corporation":false,"usgs":false,"family":"Reid","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":83120,"text":"Ontario Ministry of Natural Resources and Forestry.","active":true,"usgs":false}],"preferred":false,"id":958389,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Roloson, Scott","contributorId":368986,"corporation":false,"usgs":false,"family":"Roloson","given":"Scott","affiliations":[{"id":87695,"text":"Canadian Department of Fisheries and Oceans.","active":true,"usgs":false}],"preferred":false,"id":958213,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Schlueter, Scott L.","contributorId":197961,"corporation":false,"usgs":false,"family":"Schlueter","given":"Scott","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":958390,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Snyder, Shawn","contributorId":302899,"corporation":false,"usgs":false,"family":"Snyder","given":"Shawn","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":958391,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Young, John A. 0000-0002-4500-3673","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":360717,"corporation":false,"usgs":true,"family":"Young","given":"John","middleInitial":"A.","affiliations":[{"id":85817,"text":"EESC (retired)","active":true,"usgs":false}],"preferred":true,"id":958212,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70274250,"text":"70274250 - 2026 - A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping","interactions":[],"lastModifiedDate":"2026-03-19T19:31:01.642826","indexId":"70274250","displayToPublicDate":"2026-03-05T14:20:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping","docAbstract":"Understanding how climatic variables impact host-pathogen relationships in temperature-sensitive ectothermic host organisms is crucial under global change. Few studies have explored how temperature gradients generate inter-individual variation in epidemiological traits like host susceptibility or pathogen replication. Here, we develop a mathematical model to explore a novel hypothesis: stochastic within-host dynamics and simulated thermal mismatches between host and pathogen traits generate predictable variation in infection outcomes among hosts and across thermal gradients. Our model demonstrates that varying trait thermal optima in host immunity and pathogen replication, and stochastic within-host processes produced variation in infection outcomes. Variability was low when temperatures strongly favored host or pathogen traits, but high and diverse when their performance was similar across a broad thermal range. In contrast, when trait performance was equal across all temperatures (no mismatch) variability remained low at all temperatures. Further, the magnitude of variation, quantified by entropy, exhibited predictable patterns depending on host-pathogen thermal mismatches. We conclude that interactions between trait thermal mismatches and within-host stochasticity provide a theoretical framework to improve ectotherm disease models under climate change, providing a valuable tool for exploring the impacts of environmental change on epizootic or epidemic dynamics, particularly in vulnerable marine ecosystems.
","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rstb.2024.0328","usgsCitation":"Carlino, A., Loeher, M.M., Páez, D.J., Hershberger, P., Wolf, N., and Mihaljevic, J., 2026, Stochastic within-host dynamics and climate-sensitive traits generate predictable patterns of variation in disease outcomes: Philosophical Transactions of the Royal Society, Series B, v. 381, no. 1945, 20240328, 12 p., https://doi.org/10.1098/rstb.2024.0328.","productDescription":"20240328, 12 p.","ipdsId":"IP-180151","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2024.0328","text":"Publisher Index Page"},{"id":502167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"381","issue":"1945","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlino, Andrew","contributorId":369232,"corporation":false,"usgs":false,"family":"Carlino","given":"Andrew","affiliations":[{"id":83041,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":958655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loeher, Malina Mariko 0000-0001-9589-5641","orcid":"https://orcid.org/0000-0001-9589-5641","contributorId":365991,"corporation":false,"usgs":true,"family":"Loeher","given":"Malina","middleInitial":"Mariko","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolf, Nathan","contributorId":350132,"corporation":false,"usgs":false,"family":"Wolf","given":"Nathan","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":958659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mihaljevic, Joseph R.","contributorId":352200,"corporation":false,"usgs":false,"family":"Mihaljevic","given":"Joseph R.","affiliations":[{"id":84130,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":958660,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274254,"text":"70274254 - 2026 - Fragmentation as a population rate-changer: A field experiment","interactions":[],"lastModifiedDate":"2026-03-23T14:48:49.820811","indexId":"70274254","displayToPublicDate":"2026-03-05T09:20:51","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fragmentation as a population rate-changer: A field experiment","docAbstract":"Experimental and observational studies of effects of fragmentation on biodiversity and population dynamics have produced mixed results, with some reviews concluding strong evidence of negative effects and others concluding small positive effects. In addition, many factors (e.g., interspecific interactions, edge effects, nutrient cycling) have been identified as potential explanations underlying the various results. We carried out a population-level fragmentation study on meadow voles, focusing on changes in vital rates caused by reduced movements in experimental 2-patch systems. We developed predictions of fragmentation effects by decomposing rates of apparent survival and recruitment (parameters directly estimated using capture–recapture models) into components that do and do not include movement. Fragmentation was predicted to reduce movement rates, and reduced movement was predicted to increase apparent survival rates, decrease immigration rates, and slightly increase population growth rates. We found evidence of increased adult and juvenile apparent survival and adult population growth rate on fragmented grids, whereas results for recruitment were ambiguous and did not support our predictions. The recruitment results led to the hypothesis that immigration into suitable habitat may not be reduced by fragmentation as much as permanent emigration from that habitat. A focus on effects of reduced movement on vital rates should be a reasonable starting point for investigations of fragmentation effects. This focus suggests that explanations underlying fragmentation effects will require additional effort devoted to isolating movement components of vital rates.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.70327","usgsCitation":"Nichols, J.D., Hines, J.E., Hinz, R.L., and Hinz, J., 2026, Fragmentation as a population rate-changer: A field experiment: Ecology, v. 107, no. 3, e70327, 19 p., https://doi.org/10.1002/ecy.70327.","productDescription":"e70327, 19 p.","ipdsId":"IP-183793","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":501391,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ja/70274254/images"},{"id":501390,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ja/70274254/70274254.XML"},{"id":501389,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/publication/70274254/full"},{"id":501388,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":957200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":957201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinz, Robert L.","contributorId":43454,"corporation":false,"usgs":true,"family":"Hinz","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":957202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hinz, Janet","contributorId":367241,"corporation":false,"usgs":false,"family":"Hinz","given":"Janet","affiliations":[],"preferred":false,"id":957203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274205,"text":"70274205 - 2026 - Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar","interactions":[],"lastModifiedDate":"2026-03-13T13:23:53.855171","indexId":"70274205","displayToPublicDate":"2026-03-05T09:16:28","publicationYear":"2026","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar","docAbstract":"A new oceanographic radar instrument package was developed by the U.S. Geological Survey (USGS) to measure storm waves and water levels in the nearshore, capable of being deployed rapidly and transmitting data in near real-time. To test the performance and accuracy of the sensor, multiple years of data were collected over various hydrodynamic conditions and compared to long-term monitoring data collected at the U.S. Army Corps of Engineers (USACE) Field Research Facility in Duck, North Carolina, USA. The oceanographic radars were highly reliable, with less than 1% of the record being erroneous spikes or missing data points. At the end of the pier, the radar was highly accurate, with nearly perfect agreement in water level (r2 = 0.997) compared to a nearby National Oceanic and Atmospheric Administration (NOAA) tide gauge, and good agreement in significant wave height (r2 = 0.98) and peak wave period (r2 = 0.65) compared to a nearby USACE sensor. This work demonstrates the potential of the USGS radar for rapid response storm deployments and collecting reliable and accurate hydrodynamic measurements in the nearshore for validating coastal impact models.
","conferenceTitle":"Coastal Dynamics 2025","conferenceDate":"April 7-11, 2025","conferenceLocation":"Aveiro, Portugal","language":"English","publisher":"Springer","doi":"10.1007/978-3-032-15473-6_106","usgsCitation":"Brown, J., McClenney, B.J., and Dickhudt, P., 2026, Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar, Coastal Dynamics 2025, Aveiro, Portugal, April 7-11, 2025, p. 696-702, https://doi.org/10.1007/978-3-032-15473-6_106.","productDescription":"7 p.","startPage":"696","endPage":"702","ipdsId":"IP-174186","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":500987,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501099,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-032-15473-6_106","text":"Publisher Index Page"}],"country":"United States","state":"North Carolina","city":"Duck","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":956978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClenney, Bryce J 0009-0007-6454-2078","orcid":"https://orcid.org/0009-0007-6454-2078","contributorId":367183,"corporation":false,"usgs":true,"family":"McClenney","given":"Bryce","middleInitial":"J","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dickhudt, Patrick J. ","contributorId":169593,"corporation":false,"usgs":false,"family":"Dickhudt","given":"Patrick J. ","affiliations":[{"id":25562,"text":"(former) Woods Hole Coastal and Marine Science Center employee","active":true,"usgs":false}],"preferred":false,"id":956980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274668,"text":"70274668 - 2026 - Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling","interactions":[],"lastModifiedDate":"2026-04-03T15:59:32.312798","indexId":"70274668","displayToPublicDate":"2026-03-05T08:51:53","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling","docAbstract":"Removal sampling is an important method for estimating abundance, but nearly all removal models assume closure during sampling. Yet, closure may be difficult to assume, evaluate, or enforce in many settings. To address situations where populations are geographically open between each removal sample, we incorporated a Markovian availability process into an N-mixture model framework. This model relates local abundance available for sampling to a superpopulation through recruitment of new individuals to the sampling area. To test the model, we (1) conducted parameter identifiability analysis, (2) fit the model to removal data generated from a random walk movement model, and (3) analyzed a case study of empirical removal data. Parameters were increasingly identifiable as capture probability exceeded 0.25 and removal samples increased from 3 to 6. Abundance estimates were unbiased when parameters were identifiable, except for scenarios that simulated a behavioral response to sampling. For our case study, the model estimated negligible recruitment for benthic-oriented fishes, indicating closure, but we found evidence against closure for juvenile Chinook salmon, a highly mobile species. Our removal model allows researchers to formally test closure assumptions, to estimate the degree of closure, and to estimate abundance without bias when closure is violated.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.70289","usgsCitation":"Perry, R.W., Pope, A.C., Hendrix, A.N., Kirsch, J.E., Matthias, B.G., and Dodrill, M.J., 2026, Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling: Ecology, v. 107, no. 3, e70289, 17 p., https://doi.org/10.1002/ecy.70289.","productDescription":"e70289, 17 p.","ipdsId":"IP-173976","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502464,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.70289","text":"Publisher Index Page"},{"id":502169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.83191369646093,\n 38.29270493761476\n ],\n [\n -121.83191369646093,\n 37.97384682645672\n ],\n [\n -121.44796698911333,\n 37.97384682645672\n ],\n [\n -121.44796698911333,\n 38.29270493761476\n ],\n [\n -121.83191369646093,\n 38.29270493761476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220177,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrix, A. Noble","contributorId":369226,"corporation":false,"usgs":false,"family":"Hendrix","given":"A.","middleInitial":"Noble","affiliations":[{"id":87738,"text":"QEDA Consulting, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":958641,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirsch, Joseph E.","contributorId":369227,"corporation":false,"usgs":false,"family":"Kirsch","given":"Joseph","middleInitial":"E.","affiliations":[{"id":87739,"text":"U.S. Fish and Wildlife Service, Lodi Fish and Wildlife Office, Lodi, CA","active":true,"usgs":false}],"preferred":false,"id":958642,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matthias, Bryan G.","contributorId":369228,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","middleInitial":"G.","affiliations":[{"id":87739,"text":"U.S. Fish and Wildlife Service, Lodi Fish and Wildlife Office, Lodi, CA","active":true,"usgs":false}],"preferred":false,"id":958643,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":958644,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274672,"text":"70274672 - 2026 - The impacts of co-circulating pathogens in Pacific herring depend on interactions between viral life-cycle traits and transmission parameters, highlighting interdependencies between pathogen epizootics","interactions":[],"lastModifiedDate":"2026-04-03T15:48:44.185294","indexId":"70274672","displayToPublicDate":"2026-03-05T08:37:06","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23782,"text":"Philosophical Transactions of the Royal Society, Series B","active":true,"publicationSubtype":{"id":10}},"title":"The impacts of co-circulating pathogens in Pacific herring depend on interactions between viral life-cycle traits and transmission parameters, highlighting interdependencies between pathogen epizootics","docAbstract":"The average host susceptibility decreases as the epizootic progresses because easily infected hosts are first removed from the population. While host susceptibility is pathogen-specific, it is likely that host susceptibility is correlated between different pathogens, so that co-circulating pathogens may have reciprocal impacts on their epidemics. However, despite well-documented examples of concomitant infections in marine hosts, reciprocal epizootic effects have not been documented in wild marine organisms. We quantify reciprocal impacts between viral haemorrhagic septicaemia and viral erythrocytic necrosis in Pacific herring (Clupea pallasii) using field and laboratory work. We show that the causative viruses for both diseases circulate through herring populations and that infection with one pathogen has negative impacts on the epizootic and infection characteristics of the other pathogen, suggesting positive correlations in the susceptibility to infection between pathogens. We then use simulations of a two-strain pathogen model to show that the impact of the correlation is modulated by transmission parameters, such as the incubation period and the initial transmission rate. Our work shows that co-occurring epizootics pose a management challenge because single-pathogen management actions may amplify the epizootics of the non-targeted pathogen. This study provides a framework to evaluate the consequences of reciprocal epizootic impacts through field, experimental and modelling work.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rstb.2024.0329","usgsCitation":"Páez, D.J., Grady, C.A., Gregg, J.L., Batts, W.N., Ferreiro-Luce, S., Herron, V.L., Loeher, M.M., Williamson, S., and Hershberger, P., 2026, The impacts of co-circulating pathogens in Pacific herring depend on interactions between viral life-cycle traits and transmission parameters, highlighting interdependencies between pathogen epizootics: Philosophical Transactions of the Royal Society, Series B, v. 381, no. 1945, 20240329, 12 p., https://doi.org/10.1098/rstb.2024.0329.","productDescription":"20240329, 12 p.","ipdsId":"IP-180382","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2024.0329","text":"Publisher Index Page"},{"id":502168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Port Angeles Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.46245173833248,\n 48.14469328364527\n ],\n [\n -123.46245173833248,\n 48.11631496533502\n ],\n [\n -123.39897354240969,\n 48.11631496533502\n ],\n [\n -123.39897354240969,\n 48.14469328364527\n ],\n [\n -123.46245173833248,\n 48.14469328364527\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"381","issue":"1945","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grady, Courtney Ann 0009-0001-9079-2501","orcid":"https://orcid.org/0009-0001-9079-2501","contributorId":369229,"corporation":false,"usgs":true,"family":"Grady","given":"Courtney","middleInitial":"Ann","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregg, Jacob L. 0000-0001-5328-5482 jgregg@usgs.gov","orcid":"https://orcid.org/0000-0001-5328-5482","contributorId":203912,"corporation":false,"usgs":true,"family":"Gregg","given":"Jacob","email":"jgregg@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batts, William N.","contributorId":369230,"corporation":false,"usgs":false,"family":"Batts","given":"William","middleInitial":"N.","affiliations":[{"id":87741,"text":"Formerly U.S. Geological Survey, Western Fisheries Research Center, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":958651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferreiro-Luce, Shayla","contributorId":369245,"corporation":false,"usgs":false,"family":"Ferreiro-Luce","given":"Shayla","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":958682,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herron, V. L.","contributorId":369247,"corporation":false,"usgs":false,"family":"Herron","given":"V.","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":958683,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loeher, Malina Mariko 0000-0001-9589-5641","orcid":"https://orcid.org/0000-0001-9589-5641","contributorId":365991,"corporation":false,"usgs":true,"family":"Loeher","given":"Malina","middleInitial":"Mariko","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958652,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williamson, Sarah","contributorId":369231,"corporation":false,"usgs":false,"family":"Williamson","given":"Sarah","affiliations":[{"id":87742,"text":"Alaska Pacific University, Fisheries, Aquatic Science, and Technology Laboratory, 4101 University Drive, Anchorage, AK","active":true,"usgs":false}],"preferred":false,"id":958653,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958654,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274267,"text":"70274267 - 2026 - Comparison of nonlethal techniques as indicators of lipid content in Lake Whitefish","interactions":[],"lastModifiedDate":"2026-05-07T15:45:14.615095","indexId":"70274267","displayToPublicDate":"2026-03-05T08:20:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of nonlethal techniques as indicators of lipid content in Lake Whitefish","docAbstract":"Objective
Energetic reserves are important indicators of the relative health of fish and fish populations. Body condition indices that relate fish weight to length are commonly used as quick, noninvasive methods for approximating lipid content and condition. A microwave meter (i.e., fat meter or energy meter) is a noninvasive method found to be more accurate in some species. The objective of this study was to evaluate the suitability of nonlethal techniques for estimating muscle lipid content in Lake Whitefish Coregonus clupeaformis.
Methods
We compared the sensitivity of three nonlethal indicators of lipid content to laboratory-extracted muscle lipid content in Lake Whitefish, including readings from a handheld microwave meter at several positions, Fulton’s condition factor, and relative weight.
Results
We found significant, positive relationships between lipid content and each estimation method, except relative weight, with weak to moderate correlations. The microwave meter was moderately correlated to lipid content when positioned anterior to the dorsal fin above the lateral line (r2 = 0.50), while other positions and combinations of positions had weaker correlations (r2 range = 0.27–0.45). Correlation was only slightly improved by including additional model variables (i.e., length and weight). Fulton’s condition factor was weakly correlated with lipid content (r2 = 0.19), while relative weight was not significantly correlated with lipid content.
Conclusion
The microwave meter provides an improvement to muscle lipid estimation compared with length–weight body condition indices; however, microwave meter readings alone do not constitute a reliable predictive measure for true muscle lipid content under the conditions tested here. We hypothesize that the low strength of correlation may be due to low muscle lipid content or the presence of thick scales in Lake Whitefish. Further investigation is needed to understand the mechanisms negatively affecting the predictive performance of the microwave meter in Lake Whitefish and other species.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/tafafs/vnag002","usgsCitation":"Funnell, T.R., Shrestha, J., Leads, R.R., Holbrook, C.M., Sano, K., and Murphy, C.A., 2026, Comparison of nonlethal techniques as indicators of lipid content in Lake Whitefish: Transactions of the American Fisheries Society, v. 155, no. 3, p. 300-308, https://doi.org/10.1093/tafafs/vnag002.","productDescription":"9 p.","startPage":"300","endPage":"308","ipdsId":"IP-164860","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":501453,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501674,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/tafafs/vnag002","text":"Publisher Index Page"}],"country":"United States","state":"Michigan","city":"St. Ignace","otherGeospatial":"northern Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.68726826607411,\n 45.970689451800894\n ],\n [\n -84.68726826607411,\n 45.75821537500693\n ],\n [\n -84.21785270269774,\n 45.75821537500693\n ],\n [\n -84.21785270269774,\n 45.970689451800894\n ],\n [\n -84.68726826607411,\n 45.970689451800894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"155","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Funnell, Tyler Reid 0000-0002-9074-3531","orcid":"https://orcid.org/0000-0002-9074-3531","contributorId":334195,"corporation":false,"usgs":true,"family":"Funnell","given":"Tyler","email":"","middleInitial":"Reid","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":957473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shrestha, Jenus","contributorId":367695,"corporation":false,"usgs":false,"family":"Shrestha","given":"Jenus","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":957474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leads, Rachel R.","contributorId":367696,"corporation":false,"usgs":false,"family":"Leads","given":"Rachel","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":957475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":957476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sano, Koji","contributorId":367697,"corporation":false,"usgs":false,"family":"Sano","given":"Koji","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":957477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Cheryl A.","contributorId":367698,"corporation":false,"usgs":false,"family":"Murphy","given":"Cheryl","middleInitial":"A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":957478,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274669,"text":"70274669 - 2026 - From understanding to action: Integrating new and old methodologies to manage marine infectious disease","interactions":[],"lastModifiedDate":"2026-04-03T15:12:56.68187","indexId":"70274669","displayToPublicDate":"2026-03-05T08:08:04","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23782,"text":"Philosophical Transactions of the Royal Society, Series B","active":true,"publicationSubtype":{"id":10}},"title":"From understanding to action: Integrating new and old methodologies to manage marine infectious disease","docAbstract":"Marine diseases can have far-reaching effects on population, community and ecosystem health; however, our ability to track, predict and manage these diseases has, historically, been poor. As a result, the fields of disease ecology and epidemiology have developed at a slower pace for marine than terrestrial systems [1]. New methodologies, including genomic tools for diagnostics [2,3], transcriptomic tools for measuring host and pathogen responses to infection (e.g. [4,5]), regional oceanic modelling systems that estimate environmental conditions influencing pathogen dispersal and disease progression [6], artificial intelligence methods for quantifying pathology from images (e.g. [7]) and advanced disease modelling techniques [8,9] are precipitating a rapid increase in our understanding of marine pathosystems. In 2016, these efforts led to the first special issue of Philosophical Transactions of the Royal Society B (Marine diseases, volume 371, issue 1689) focused entirely on marine disease ecology and evolution, and in 2020, the first book, Marine disease ecology, was devoted to this topic [10].
This special issue, focused on marine disease management, is being published a decade after the first Philosophical Transactions special issue on marine diseases. The shift to a management focus reflects an urgent need for management strategies to address high-impact diseases and the rapid methodological advances that have resulted. The papers included in this issue demonstrate the value of combining classical approaches (e.g. routine disease surveillance, reductionistic pathogen challenge trials, rapid throughput diagnostics) with cutting-edge technologies (e.g. high-resolution oceanographic models, Bayesian models, replicated transcriptomic studies) to identify drivers of disease, quantify impacts and suggest management strategies.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rstb.2024.0318","usgsCitation":"Groner, M.L., Paez, D.J., and Gehman, A.M., 2026, From understanding to action: Integrating new and old methodologies to manage marine infectious disease: Philosophical Transactions of the Royal Society, Series B, v. 381, no. 1945, 20240318, 4 p., https://doi.org/10.1098/rstb.2024.0318.","productDescription":"20240318, 4 p.","ipdsId":"IP-185260","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2024.0318","text":"Publisher Index Page"},{"id":502162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"381","issue":"1945","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":292708,"corporation":false,"usgs":false,"family":"Groner","given":"Maya","middleInitial":"L.","affiliations":[{"id":62985,"text":"Senior Research Scientist, Bigelow Laboratory for Ocean Sciences, 60 Bigelow Drive, East Boothbay, ME 04544","active":true,"usgs":false}],"preferred":false,"id":958645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gehman, Alyssa-Lois M.","contributorId":335110,"corporation":false,"usgs":false,"family":"Gehman","given":"Alyssa-Lois","middleInitial":"M.","affiliations":[{"id":80312,"text":"Hakai Institute; University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":958647,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273889,"text":"70273889 - 2026 - Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA","interactions":[],"lastModifiedDate":"2026-03-23T14:02:07.561392","indexId":"70273889","displayToPublicDate":"2026-03-04T08:59:53","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA","docAbstract":"Large magnitude snow avalanches (destructive size ≥ D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a two-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. However, placing this 2019 large magnitude avalanche cycle in historic context requires data prior to the instrumental record. Here, we paired tree disturbance data from dendrochronology (1698 to 2020) with meteorological data from the modeled and instrumental record (1901 to 2020) to understand the frequency and climate drivers of large magnitude snow avalanche cycles. The extensive number of downed trees from the 2019 avalanche cycle allowed us to collect 1,188 cross-sections and cores from 1023 individual trees within 24 avalanche paths across the state. From these samples we identified 4135 avalanche-related growth disturbances. We employed a strategic nested sampling design to spatially aggregate avalanche frequency from individual avalanche paths, to counties, to three major sub-regions (i.e., north, central, and south), and across the entire region (i.e., state of Colorado). Over a period spanning more than three centuries (1698 to 2020), we identified 76 avalanche years within 24 individual avalanche paths. Large magnitude avalanche event frequency varied across paths and sub-regions with several notable region-wide avalanche cycles. Both tree-ring and historical written records highlighted 1899 as a year with widespread and large magnitude avalanche activity similar to the March 2019 avalanche cycle. Since the early-20th century (1900 to 2020) regional avalanche probability declined significantly in parallel with decreasing snowpack throughout Colorado. Similarly, dominant avalanche regimes shifted from large magnitude regional cycles driven by above average snowfall years over most of the record, to regional avalanche cycles occurring more commonly in average to low snow years since 1988. In recent decades, a lack of December precipitation and above average March precipitation characterized years with regional large magnitude avalanche activity. Even with declining snow water equivalent, truly extreme regional large magnitude avalanche cycles remain possible – as demonstrated by the 2019 cycle. This underscores that rare but high-impact events are not eliminated by long-term trends. Understanding the changing snow and weather drivers and subsequent behavior of large magnitude avalanche cycles across multiple spatial scales may improve avalanche forecasting and the products and mitigations strategies developed by structural engineers to mitigate avalanche danger. This can decrease the avalanche risk to the public and improve infrastructure design in avalanche terrain.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/nhess-26-1059-2026","usgsCitation":"Peitzsch, E.H., Martin, J.T., Greene, E.M., Eckert, N., Favillier, A., Konigsberg, J., Kichas, N., Stahle, D.K., Birkeland, K.W., Elder, K., and Pederson, G.T., 2026, Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA: Natural Hazards and Earth System Sciences, v. 26, p. 1059-1074, https://doi.org/10.5194/nhess-26-1059-2026.","productDescription":"16 p.","startPage":"1059","endPage":"1074","ipdsId":"IP-175486","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":501654,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-26-1059-2026","text":"Publisher Index Page"},{"id":499809,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.129166385724,\n 41.04962213955744\n ],\n [\n -109.129166385724,\n 36.99334376580887\n ],\n [\n -102.04655644997314,\n 36.99334376580887\n ],\n [\n -102.04655644997314,\n 41.04962213955744\n ],\n [\n -109.129166385724,\n 41.04962213955744\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2026-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greene, Ethan M.","contributorId":330958,"corporation":false,"usgs":false,"family":"Greene","given":"Ethan","middleInitial":"M.","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":955442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eckert, Nicolas","contributorId":330971,"corporation":false,"usgs":false,"family":"Eckert","given":"Nicolas","email":"","affiliations":[{"id":27334,"text":"Universite Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":955443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Favillier, Adrien","contributorId":330970,"corporation":false,"usgs":false,"family":"Favillier","given":"Adrien","email":"","affiliations":[{"id":66013,"text":"University of Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":955444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Konigsberg, Jason","contributorId":330955,"corporation":false,"usgs":false,"family":"Konigsberg","given":"Jason","email":"","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":955445,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kichas, Nickolas","contributorId":366210,"corporation":false,"usgs":false,"family":"Kichas","given":"Nickolas","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":955446,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stahle, Daniel K.","contributorId":210004,"corporation":false,"usgs":true,"family":"Stahle","given":"Daniel","middleInitial":"K.","affiliations":[],"preferred":false,"id":955447,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Birkeland, Karl W.","contributorId":173366,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","middleInitial":"W.","affiliations":[{"id":27213,"text":"USDA Forest Service National Avalanche Center, Bozeman, MT, USA","active":true,"usgs":false}],"preferred":false,"id":955448,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Elder, Kelly","contributorId":346220,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":955449,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":955450,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70274161,"text":"ofr20261065 - 2026 - Evaluation of pathogen risks and testing considerations for Chinook salmon egg movements between New Zealand and California","interactions":[],"lastModifiedDate":"2026-04-10T15:34:37.991321","indexId":"ofr20261065","displayToPublicDate":"2026-03-03T12:16:41","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-1065","displayTitle":"Evaluation of Pathogen Risks and Testing Considerations for Chinook Salmon Egg Movements Between New Zealand and California","title":"Evaluation of pathogen risks and testing considerations for Chinook salmon egg movements between New Zealand and California","docAbstract":"Oncorhynchus tshawytscha (Walbaum in Artedi, 1792; Chinook salmon) were historically abundant in the McCloud River but are now extirpated from this tributary owing to dam construction and lack of passage. Planning efforts to restore populations above Shasta and Keswick Dams are currently underway, including an evaluation of potential source populations. One potential source is New Zealand Chinook salmon, which are believed to have originated from tributaries of the Sacramento River. These fish could be returned to California if reintroduction risks, including risks of pathogen introduction, could be sufficiently mitigated. The U.S. Geological Survey was contracted to provide scientific support for reintroduction efforts, including evaluating the risks of pathogen transmission via the movement of Chinook salmon eggs from New Zealand to the McCloud River. This report estimates pathogen risks associated with egg movement and considers epidemiological and biosecurity measures to minimize these risks.
Pathogen risks associated with the movement of Chinook salmon eggs from New Zealand were evaluated based on pathogen virulence, transmission route, and geographic distribution. These criteria identified 14 moderate- and high-risk pathogens out of the 30 pathogens evaluated. Pathogen species and strains were considered high risk if they have the potential for vertical transmission (that is, transmission from parent to offspring), are moderately or highly virulent, and are exotic to the Sacramento River Basin. According to these criteria, we identified the following pathogens as high risk:
Strategic use of testing and biosecurity measures can minimize pathogen risks associated with the movement of eggs. The most effective measures include iodophor treatment of eggs to remove external pathogens, testing of all the adult fish from which gametes are obtained, and a quarantine period after transport to confirm pathogen testing results. Additional measures to enhance biosecurity could include testing the quarantined fish following emergence and (or) developing a fish health history of the source population through pathogen monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261065","collaboration":"Prepared in cooperation with California Department of Fish and Wildlife, Anchor QEA, and HDR","programNote":"Land Management Research Program and Species Management Research Program","usgsCitation":"Couch, C.E., Powell, D.B., and Lovy, J., 2026, Evaluation of pathogen risks and testing considerations for Chinook salmon egg movements between New Zealand and California: U.S. Geological Survey Open-File Report 2026–1065, 18 p., https://doi.org/10.3133/ofr20261065.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-182977","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":500703,"rank":5,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
5501-A Cook Underwood Road
Cook, Washington 98605-9717
This report presents the results of a cooperative study by the U.S. Geological Survey and Missouri Department of Natural Resources to quantify sediment transport source contributions in the Medicine Creek drainage basin. Understanding relative source contributions provides valuable information for selecting the conservation practices that may be most effective in reducing sediment and sediment-associated nutrient transport in the Medicine Creek drainage basin and similar areas of the Lower Grand River drainage basin. Sediment samples were collected from potential contributing areas (source samples) and from fluvial-transported samples (target samples). Source sample types included streambanks, row crop fields, and a combined pastures and forests category. Samples were analyzed for particle size and quantity of carbon, nitrogen, stable isotopes of carbon and nitrogen, and 49 mineral elements as potential tracers. Results for the carbon stable isotope ratio of carbon-13/carbon-12 (δ13C) and concentrations of total carbon, total nitrogen, calcium, potassium, and copper were selected by discriminant function analysis as the best combination of multiple tracers to differentiate each source type. The discriminant function analysis poorly differentiated pastures and forests, so these source types were combined. The sources defined by the discriminant function analysis were then used in an unmixing model to apportion sources for each target sample.
In the study area, transported sediment was predominantly bank sediment, with an overall average of 86.9 percent of suspended-sediment samples and depositional streambed samples attributed to bank material. Suspended-sediment samples from the mainstem of Medicine Creek were dominated by bank sediments (average of 95.8 percent), and depositional streambed samples from throughout the drainage basin had more variable source contributions with an average of 71.1 percent attributed to bank material. The relative importance of upland sources (row crop fields and the combined pastures and forests category) varied seasonally and with streamflow but was not related to land use or drainage basin size. Relative contributions from upland sources were greater in the summer through winter rather than spring and during lower streamflow, though this may be driven by the seasonality of streamflow. These results indicate management practices that reduce bank erosion could be effective strategies for managing the dominant source of sediment and sediment-associated phosphorus.
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U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240