To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.
No abstract available.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230003","usgsCitation":"Pollitz, F., Garza-Giron, R., and Lay, T., 2023, Comment on \"Multi-Event explosive seismic source for the 2022 Mw 6.3 Hunga Tonga submarine volcanic eruption\" by Julien Thurin, Carl Tape, and Ryan Modrak: The Seismic Record, v. 3, no. 3, p. 210-2014, https://doi.org/10.1785/0320230003.","productDescription":"5 p.","startPage":"210","endPage":"2014","ipdsId":"IP-149376","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":442457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230003","text":"Publisher Index Page"},{"id":426238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tonga","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n 15.707465011804047\n ],\n [\n 139.84794943809027,\n 20.668627791297567\n ],\n [\n 143.4623513761436,\n -40.646297636757645\n ],\n [\n 179.9,\n -39.74397803815904\n ],\n [\n 179.9,\n 15.707465011804047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 16.290847903689155\n ],\n [\n -179.9,\n -39.1693171545614\n ],\n [\n -150,\n -39.1693171545614\n ],\n [\n -150,\n 17.303337805258792\n ],\n [\n -179.9,\n 16.290847903689155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":895781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garza-Giron, Ricardo","contributorId":334466,"corporation":false,"usgs":false,"family":"Garza-Giron","given":"Ricardo","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":895782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":334467,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":895783,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247678,"text":"70247678 - 2023 - Conservation decision support for Silver Chub habitat in Lake Erie","interactions":[],"lastModifiedDate":"2023-11-20T17:35:54.449622","indexId":"70247678","displayToPublicDate":"2023-08-10T09:43:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation decision support for Silver Chub habitat in Lake Erie","docAbstract":"Conservation and restoration of aquatic species is difficult, especially for rare species, because their habitats are typically disturbed, obscuring the natural ability of the habitat to support each species. The Lake Erie population of Silver Chub Macrhybopsis storeriana struggles to sustain itself in a habitat disturbed by a wide spectrum of anthropogenic factors. Application of multiple model predictions can provide indications of conservation or restoration opportunities for this species.
A combination of models that predict the best potential for Lake Erie habitat to support Silver Chub and the effects of anthropogenic disturbances on that population were used to identify habitat conditions throughout the western aquatic lake unit.
As many as 76 combinations of best habitat potential and disturbance conditions were present, but the best opportunities occurred in <12% of the study area. Some of the best protection opportunities were farthest offshore, and extensive areas of least disturbed habitat for restoration were near the southern and western shores. The location-specific model predictions provide fine-scale decision support for Silver Chub habitat protection or restoration.
The approach applied here may help identify compatibilities among species to achieve the desirable fish community for Lake Erie and reconcile conflicting management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10843","usgsCitation":"McKenna, J.E., 2023, Conservation decision support for Silver Chub habitat in Lake Erie: North American Journal of Fisheries Management, v. 43, no. 5, p. 1151-1165, https://doi.org/10.1002/nafm.10843.","productDescription":"15 p.","startPage":"1151","endPage":"1165","ipdsId":"IP-137742","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":419749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ohio, Ontario","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.90370229732227,\n 41.50076537943562\n ],\n [\n -82.56348384906306,\n 41.989542296201876\n ],\n [\n -82.63835267762897,\n 42.04516549693972\n ],\n [\n -82.95186589725073,\n 41.98606423085357\n ],\n [\n -83.12032076152461,\n 42.093795884914755\n ],\n [\n -83.10160355438312,\n 42.26024574328264\n ],\n [\n -83.17179308116394,\n 42.235999094028784\n ],\n [\n -83.190510288306,\n 42.08337822384061\n ],\n [\n -83.46190979185857,\n 41.87118148731366\n ],\n [\n -83.53677862042505,\n 41.68624000731879\n ],\n [\n -83.30749283294051,\n 41.59531904873529\n ],\n [\n -83.0454519329587,\n 41.38150113199248\n ],\n [\n -82.62899407405826,\n 41.34286871970326\n ],\n [\n -81.90370229732227,\n 41.50076537943562\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":880015,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70248406,"text":"70248406 - 2023 - Evaluation of hydrodynamic mixing in an afterbay reservoir","interactions":[],"lastModifiedDate":"2023-09-12T14:01:11.219703","indexId":"70248406","displayToPublicDate":"2023-08-10T08:54:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2255,"text":"Journal of Environmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of hydrodynamic mixing in an afterbay reservoir","docAbstract":"This study focused on the mixing of a solute, assumed to be conservative, introduced to one arm of an afterbay reservoir, between Keswick and Shasta Dams on the Sacramento River near Redding, California. Rhodamine water tracer (WT) dye served as the solute in a field experiment, and was introduced over 4.5 h and monitored for 4 days by sondes moored in the reservoir. The scenario was modeled numerically using the Delft3D flexible mesh (FM) hydrodynamic and mixing model, with measured inflows, outflows, water level, water temperatures, and bathymetry as input. Manning’s
Diet analysis is a vital tool for understanding trophic interactions and is frequently used to inform conservation and management. Molecular approaches can identify diet items that are impossible to distinguish using more traditional visual-based methods. Yet, our understanding of how different variables, such as predator species or prey ration size, influence molecular diet analysis is still incomplete. Here, we conducted a large feeding trial to assess the impact that ration size, predator species, and temperature had on digestion rates estimated with visual identification, qPCR, and metabarcoding. Our trial was conducted by feeding two rations of Chinook salmon (Oncorhynchus tshawytscha) to two piscivorous fish species (largemouth bass [Micropterus salmoides] and channel catfish [Ictalurus punctatus]) held at two different temperatures (15.5 and 18.5°C) and sacrificed at regular intervals up to 120 h from the time of ingestion to quantify the prey contents remaining in the digestive tract. We found that ration size, temperature, and predator species all influenced digestion rate, with some indication that ration size had the largest influence. DNA-based analyses were able to identify salmon smolt prey in predator gut samples for much longer than visual analysis (~12 h for visual analysis vs. ~72 h for molecular analyses). Our study provides evidence that modelling the persistence of prey DNA in predator guts for molecular diet analyses may be feasible using a small set of controlling variables for many fish systems.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13849","usgsCitation":"Dick, C., Larson, W., Karpan, K., Baetscher, D.S., Shi, Y., Sethi, S., Fangue, N., and Henderson, M., 2023, Prey ration, temperature, and predator species influence digestion rates of prey DNA inferred from qPCR and metabarcoding: Molecular Ecology Resources, v. 00, p. 1-17, https://doi.org/10.1111/1755-0998.13849.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-148203","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":442471,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1755-0998.13849","text":"External Repository"},{"id":433116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"00","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dick, Cory","contributorId":342431,"corporation":false,"usgs":false,"family":"Dick","given":"Cory","email":"","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":910100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Wesley A.","contributorId":342433,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley A.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpan, Kirby","contributorId":342435,"corporation":false,"usgs":false,"family":"Karpan","given":"Kirby","email":"","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baetscher, Diana S.","contributorId":342437,"corporation":false,"usgs":false,"family":"Baetscher","given":"Diana","email":"","middleInitial":"S.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Yue","contributorId":342439,"corporation":false,"usgs":false,"family":"Shi","given":"Yue","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910105,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fangue, Nann A.","contributorId":342441,"corporation":false,"usgs":false,"family":"Fangue","given":"Nann A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910106,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":910107,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247499,"text":"70247499 - 2023 - SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","interactions":[],"lastModifiedDate":"2023-08-10T11:47:28.20107","indexId":"70247499","displayToPublicDate":"2023-08-09T06:45:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","docAbstract":"Native pinyon (Pinus spp.) and juniper (Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (www.SageSTEP.org), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.
Increased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min−1 in mechanical and 106 m min−1 in prescribed fire treatments by 10 years post-treatment compared to 5 m min−1 in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.
While prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-023-00201-7","usgsCitation":"Williams, C.L., Ellsworth, L., Strand, E., Reeves, M.C., Shaff, S.E., Short, K., Chambers, J., Newingham, B., and Tortorelli, C., 2023, Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior: Fire Ecology, v. 19, 46, 21 p., https://doi.org/10.1186/s42408-023-00201-7.","productDescription":"46, 21 p.","ipdsId":"IP-154672","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":442492,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-023-00201-7","text":"Publisher Index Page"},{"id":419748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.242321499979,\n 37.26614922694803\n ],\n [\n -111.73150627380747,\n 40.33982762810214\n ],\n [\n -117.02080093108165,\n 40.473878098349985\n ],\n [\n -116.74789132368525,\n 38.56409342781134\n ],\n [\n -112.242321499979,\n 37.26614922694803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.35613130086793,\n 41.4092957538588\n ],\n [\n -117.52494090332212,\n 43.41419744406673\n ],\n [\n -120.12652755099501,\n 45.42305748463039\n ],\n [\n -122.07668627870967,\n 43.86124338845653\n ],\n [\n -121.35613130086793,\n 41.4092957538588\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Claire L.","contributorId":328374,"corporation":false,"usgs":false,"family":"Williams","given":"Claire","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":880021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, Lisa M.","contributorId":328375,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":880022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Strand, Eva","contributorId":328376,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":880023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeves, Matt C.","contributorId":328377,"corporation":false,"usgs":false,"family":"Reeves","given":"Matt","email":"","middleInitial":"C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":880025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Short, Karen","contributorId":328378,"corporation":false,"usgs":false,"family":"Short","given":"Karen","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880026,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chambers, Jeanne C.","contributorId":328379,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880027,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newingham, Beth","contributorId":328380,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":880028,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tortorelli, Claire","contributorId":328381,"corporation":false,"usgs":false,"family":"Tortorelli","given":"Claire","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":880029,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249617,"text":"70249617 - 2023 - Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","interactions":[],"lastModifiedDate":"2024-09-16T16:08:11.965666","indexId":"70249617","displayToPublicDate":"2023-08-07T09:15:32","publicationYear":"2023","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":"Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","docAbstract":"Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16905","usgsCitation":"DeVilbiss, S.E., Taylor, J.M., and Hicks, M.B., 2023, Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams: Global Change Biology, v. 29, no. 19, p. 5615-5633, https://doi.org/10.1111/gcb.16905.","productDescription":"19 p.","startPage":"5615","endPage":"5633","ipdsId":"IP-147797","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":442494,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16905","text":"Publisher Index Page"},{"id":421998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi Alluvial Plain ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.5140953224701,\n 31.05055950304515\n ],\n [\n -90.8551102985312,\n 32.36507651262579\n ],\n [\n -89.83254733034994,\n 33.34811466354185\n ],\n [\n -89.71892922277418,\n 33.94399111104305\n ],\n [\n -90.08250716701652,\n 35.020996431931664\n ],\n [\n -90.25293432837982,\n 34.97445988995358\n ],\n [\n -90.4347233005013,\n 34.82536524774929\n ],\n [\n -90.54834140807708,\n 34.67600021318641\n ],\n [\n -90.61651227262215,\n 34.376461081479576\n ],\n [\n -90.76421581247044,\n 34.29202179657514\n ],\n [\n -90.95736659534946,\n 34.13229367636508\n ],\n [\n -91.0255374598945,\n 33.91570969239025\n ],\n [\n -91.15051737822778,\n 33.54719821260997\n ],\n [\n -91.11643194595524,\n 33.06291844822118\n ],\n [\n -91.13915556747027,\n 32.709900045262685\n ],\n [\n -90.95736659534946,\n 32.38426808119267\n ],\n [\n -91.57090437625834,\n 31.361530547535793\n ],\n [\n -91.6731606730759,\n 31.0213536083742\n ],\n [\n -91.5140953224701,\n 31.05055950304515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"19","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVilbiss, Stephen E.","contributorId":316291,"corporation":false,"usgs":false,"family":"DeVilbiss","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":886463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Jason M.","contributorId":100678,"corporation":false,"usgs":true,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":886464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247508,"text":"70247508 - 2023 - A spatially explicit modeling framework to guide management of subsidized avian predator densities","interactions":[],"lastModifiedDate":"2023-08-10T11:43:08.209226","indexId":"70247508","displayToPublicDate":"2023-08-07T06:39:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A spatially explicit modeling framework to guide management of subsidized avian predator densities","docAbstract":"Anthropogenic resource subsidization across western ecosystems has contributed to widespread increases in generalist avian predators, including common ravens (Corvus corax; hereafter, raven). Ravens are adept nest predators and can negatively impact species of conservation concern. Predation effects from ravens are especially concerning for greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), which have experienced prolonged population decline. Our objectives were to quantify spatiotemporal patterns in raven density, evaluate sage-grouse nest success concurrent with fluctuating raven densities, and demonstrate a spatially explicit decision support tool to guide management applications to appropriate conflict areas. We combined ~28,000 raven point count surveys with data from more than 900 sage-grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage-grouse nest survival using a hierarchical frailty model as a function of raven density and other environmental covariates that influence the risk of nest failure. Raven density commonly exceeded 0.5 ravens km−2 and increased at low elevations with more anthropogenic development and/or agriculture. Reduced sage-grouse nest survival was strongly associated with elevated raven density (e.g., >0.5 ravens km−2) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers within the Great Basin to help direct management actions to localized areas where sage-grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available.
Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104328","usgsCitation":"Erdogan Erten, G., Erten, O., Karacan, C.O., Boisvert, J., and Deutsch, C.V., 2023, Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources: International Journal of Coal Geology, v. 276, 104328, 16 p., https://doi.org/10.1016/j.coal.2023.104328.","productDescription":"104328, 16 p.","ipdsId":"IP-149602","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":419585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.33693408888138,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.563642070051216\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erdogan Erten, Gamze","contributorId":317909,"corporation":false,"usgs":false,"family":"Erdogan Erten","given":"Gamze","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erten, Oktay","contributorId":300145,"corporation":false,"usgs":false,"family":"Erten","given":"Oktay","email":"","affiliations":[],"preferred":false,"id":879701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boisvert, Jeff","contributorId":317910,"corporation":false,"usgs":false,"family":"Boisvert","given":"Jeff","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deutsch, Clayton V.","contributorId":317911,"corporation":false,"usgs":false,"family":"Deutsch","given":"Clayton","email":"","middleInitial":"V.","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879704,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256432,"text":"70256432 - 2023 - Effects of sucker gigging on fish populations in Oklahoma scenic rivers","interactions":[],"lastModifiedDate":"2024-09-09T15:31:56.213633","indexId":"70256432","displayToPublicDate":"2023-08-04T10:26:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-151-2023","title":"Effects of sucker gigging on fish populations in Oklahoma scenic rivers","docAbstract":"Suckers (Catostomidae) are ecologically important, and some support popular fisheries, despite not being considered ‘sport fish’ in most states. Gigging suckers is a popular and culturally significant pastime in the Ozark Highlands, but little is known about the effect of gigging harvest on population dynamics of suckers. Therefore, research is needed to determine safe levels of sucker harvest that ensure sustainability of sucker gigging and protect overall ecosystem function. The objectives of this study were to: 1) determine the spatial distribution of common sucker species during spawning season (when sucker gigging is most effective), 2) determine the population size, age structure , and total mortality rate for common sucker species, and 3) model the effects of different harvest rates on sucker populations to determine the harvest rate at which growth overfishing and recruitment overfishing begin. Suckers were sampled using electrofishing, modified fyke netting, gillnetting, hoop netting, and seining and marked with passive integrated transponder (PIT) tags to provide information about population size, demographics, and coarse-scale movement patterns. A subset of fish sampled using the above gears and additional fish collected during gigging tournaments in 2017-2019 and 2021-2022 (no tournament was held in 2020) were used for age analyses. Tournament data collected prior to the initiation of this project were obtained from the state agency. Data from gigging tournaments indicated Golden Redhorse Moxostoma erythrurum, Black Redhorse M. duquesnei, White Sucker Catostomus commersonii, and Spotted Sucker Minytrema melanops were vulnerable to gigging harvest. Selection by giggers for larger individuals was apparent for all species except Golden Redhorse in 2019. Spotted Suckers constituted most fish harvested, but the proportion of each species harvested still varied among years. A total of 943 fish were aged from samples obtained from 2017 to 2022 and results from subsequent analyses indicated a high degree of variation in growth rates within and among species. Over 4,700 suckers were tagged with PIT tags and over 400 recaptures of these tagged fish were made since autumn 2018. Preliminary analyses indicate survival was consistent across samples and species, and detection rates varied by sampling event (3-month periods). Our most likely top multistrata model suggested that a large portion of fish within the upper Spavinaw, lower Spavinaw, and reservoir sections remain in these locations year-round (means: 0.46 – 0.67). Despite this, transition probabilities are still high for movement from upper Spavinaw to lower Spavinaw (mean: 0.32) and from lower Spavinaw to upper Spavinaw (mean: 0.38). Likewise, transition probabilities were high for movement from lower Spavinaw to the reservoir (mean: 0.15) and from the reservoir to lower Spavinaw (mean: 0.32). Transition probabilities between upper Spavinaw and the reservoir were low in both directions (means < 0.01). Population sizes, growth trajectories and length-weight relationships varied among species. Preliminary harvest models suggest species-specific regulation may be scientifically appropriate; however, it may be difficult for giggers to identify species while gigging. Based on our model results, there appears to be little risk of recruitment or growth overfishing for any species at current exploitation levels.
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E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907355,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247436,"text":"70247436 - 2023 - DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","interactions":[],"lastModifiedDate":"2023-08-08T13:37:18.248038","indexId":"70247436","displayToPublicDate":"2023-08-04T08:30:10","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","docAbstract":"Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ACM SIGKDD Conference on Knowledge Discovery & Data Mining","conferenceDate":"August 6-10, 2023","conferenceLocation":"Long Beach, CA","language":"English","publisher":"Association for Computing Machinery","doi":"10.1145/3580305.3599807","usgsCitation":"Li, X., Burgi, P.M., Ma, W., Noh, H., Wald, D.J., and Xu, S., 2023, DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards, in KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, v. 29, Long Beach, CA, August 6-10, 2023, p. 4391-4403, https://doi.org/10.1145/3580305.3599807.","productDescription":"13 p.","startPage":"4391","endPage":"4403","ipdsId":"IP-149697","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":419595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","noUsgsAuthors":false,"publicationDate":"2023-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Xuechun","contributorId":317874,"corporation":false,"usgs":false,"family":"Li","given":"Xuechun","email":"","affiliations":[{"id":69176,"text":"Stonybrook University","active":true,"usgs":false}],"preferred":false,"id":879620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgi, Paula Madeline 0000-0003-3001-5759","orcid":"https://orcid.org/0000-0003-3001-5759","contributorId":317875,"corporation":false,"usgs":true,"family":"Burgi","given":"Paula","email":"","middleInitial":"Madeline","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Wei","contributorId":317876,"corporation":false,"usgs":false,"family":"Ma","given":"Wei","email":"","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":879622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Haeyoung","contributorId":317877,"corporation":false,"usgs":false,"family":"Noh","given":"Haeyoung","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":879623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879624,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":879625,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247383,"text":"sir20235073 - 2023 - Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","interactions":[],"lastModifiedDate":"2026-03-12T20:43:24.534885","indexId":"sir20235073","displayToPublicDate":"2023-08-03T14:21:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5073","displayTitle":"Response in the Water Quality of Delavan Lake, Wisconsin, to Changes in Phosphorus Loading—Setting New Goals for Loading from its Drainage Basin","title":"Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","docAbstract":"During 1989–92, an extensive rehabilitation project was completed in and around Delavan Lake, Wisconsin, to improve the lake’s water quality. However, in 2016, the lake was listed by the Wisconsin Department of Natural Resources as impaired for excessive algal growth (high chlorophyll a concentrations), and high phosphorus input was listed as its likely cause. In addition, the recent (2017–21) mean summer water clarity (as measured with a Secchi disk) was shallower than the goal set by the community (3.0 meters). Based primarily on flow and water-quality data collected in Jackson Creek, which is the main tributary of the lake, the mean annual phosphorus loading to the lake during water years (WYs) 2017–21 was 6,570 kilograms per year (kg/yr), and 306 kg/yr came from uncontrollable sources (atmospheric deposition and groundwater). Phosphorus loading during these years was about 48 percent higher than the long-term mean loading from WY 1984 to WY 2021. Based on results from Canfield-Bachmann phosphorus models, Carlson trophic state index relations, and the Jones and Bachmann chlorophyll a relation, external phosphorus loading would need to be decreased from 6,570 to 5,270 kg/yr (a 21-percent reduction in the potentially controllable external phosphorus load from the base period of WYs 2017–21) for chlorophyll a concentrations greater than 20 micrograms per liter to be detected no more than 5.0 percent of the time (the Wisconsin Department of Natural Resources criterion for chlorophyll a impairment for the lake). Based on Carlson trophic state index relations, external loading would need to be decreased from 6,570 to 4,380 kg/yr (a 35-percent reduction in the potentially controllable external phosphorus load) for summer mean Secchi depths to increase to 3.0 meters. Therefore, for Delavan Lake to reach the water-quality criteria for impairment and the goals for all three water-quality constituents, a 35-percent reduction in the potentially controllable phosphorus load is needed, which equates to a reduction in total phosphorus loading from 6,570 to 4,380 kg/yr. A 35-percent reduction in phosphorus loading to improve the water quality of Delavan Lake is less than the 49-percent reduction in phosphorus loading required for the area near Delavan Lake to improve the water quality of the Rock River and its tributaries indicated in the Rock River total maximum daily load.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235073","collaboration":"Prepared in cooperation with the Town of Delavan and the Delavan Lake Sanitary District","usgsCitation":"Robertson, D.M., Siebers, B.J., and Fredrick, R.A., 2023, Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin: U.S. Geological Survey Scientific Investigations Report 2023–5073, 28 p., https://doi.org/10.3133/sir20235073.","productDescription":"Report: viii, 28 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-148703","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115123.htm","linkFileType":{"id":5,"text":"html"}},{"id":419534,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235073/full","text":"Report"},{"id":419467,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":419466,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H85BK0","text":"USGS data release","linkHelpText":"Eutrophication models to simulate changes in the water quality of Green Lake, Wisconsin in response to changes in phosphorus loading, with supporting water-quality data for the lake, its tributaries, and atmospheric deposition"},{"id":419465,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5073/images/"},{"id":419464,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.XML","linkFileType":{"id":8,"text":"xml"}},{"id":419463,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5073"},{"id":419462,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5073/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Delavan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.69602097806326,\n 42.574403384923556\n ],\n [\n -88.52306051926304,\n 42.574403384923556\n ],\n [\n -88.52306051926304,\n 42.66531414833537\n ],\n [\n -88.69602097806326,\n 42.66531414833537\n ],\n [\n -88.69602097806326,\n 42.574403384923556\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Mississippi Alluvial Plain is one of the most important agricultural regions in the United States, and crop productivity relies on groundwater irrigation from an aquifer system whose full capacity is unknown. Groundwater withdrawals from the Mississippi River Valley alluvial aquifer have resulted in substantial groundwater-level declines and reductions in base flow in streams within the Mississippi Alluvial Plain. These effects are limiting well production and threatening future water availability in the region.
A comprehensive assessment of water availability in the Mississippi Alluvial Plain is critically important for making well-informed management decisions about sustainability, establishing best practices for water use, and predicting changes to water levels in the Mississippi Alluvial Plain over the next 50–100 years. The first step in the new regional modeling effort was to run the existing Mississippi Embayment Regional Aquifer Study (MERAS) model and perform data-worth and uncertainty analyses to prioritize data collection efforts to improve model forecasts. Parameter estimation indicated that streambed conductance was one of the variables that the model was most sensitive to, but little data were available to constrain those general estimates.
From this characterization of the existing data, a map of the streams that the MERAS model was most sensitive to was created by the U.S. Geological Survey to guide the collection of 862 kilometers of waterborne resistivity surveys within the Delta region of Mississippi to characterize streambed lithology. This technique characterizes the streambed itself and the 15–30 meters below the streambed that control the exchange of water between the stream and the alluvial aquifer. These data can be used to map changes in the lithology of the streambed and identify areas of potential groundwater/surface-water exchange. Additionally, electrical and nuclear well logs from the study area were compared to facilitate the development of a petrophysical relation between the waterborne resistivity data and hydraulic conductivity. Resistivity values may then be used as a cost-effective way to approximate aquifer hydraulic conductivity distributions for use in regional groundwater models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3500","issn":"2329-132X","collaboration":"Prepared in cooperation with the Arkansas Department of Health, Arkansas Game and Fish Commission, Delta Council, Delta FARM, Delta Sustainable Water Resources Task Force, Delta Wildlife HydroGeophysics Group, Aarhus University, Mississippi Department of Environmental Quality, Mississippi State University, Missouri Department of Natural Resources, The Nature Conservancy, U.S. Army Corps of Engineers, U.S. Department of Agriculture-Agricultural Research Service, University of Arkansas, University of Mississippi, Yazoo Mississippi Delta Joint Water Management District","usgsCitation":"Adams, R.F., Miller, B.V., Kress, W.H., Minsley, B.J., and Rigby, J.R., 2023, Estimating streambed hydraulic conductivity for selected streams in the Mississippi Alluvial Plain using continuous resistivity profiling methods—Delta region: U.S. Geological Survey Scientific Investigations Map 3500, 2 sheets, https://doi.org/10.3133/sim3500.","productDescription":"2 Sheets: 45.00 x 34.25 inches and 45.00 x 34.55 inches","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-115128","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":419523,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WQPRFB","text":"USGS—Waterborne resistivity inverted models, Mississippi Alluvial Plain, 2016–2018"},{"id":419522,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet2.pdf","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 2"},{"id":419521,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet1.pdf","size":"15.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 1"},{"id":419520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3500/coverthb.jpg"},{"id":500206,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115125.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arkansas, Louisiana, Mississippi","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90,\n 35\n ],\n [\n -91.25,\n 35\n ],\n [\n -91.25,\n 31\n ],\n [\n -90,\n 31\n ],\n [\n -90,\n 35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
For additional information, visit
https://www.usgs.gov/centers/lmg-water/
Ophidiomycosis (snake fungal disease) is an infectious disease caused by the fungus Ophidiomyces ophidiicola to which all snake species appear to be susceptible. Significant variation has been observed in clinical presentation, progression of disease, and response to treatment, which may be due to genetic variation in the causative agent. Recent phylogenetic analysis based on whole-genome sequencing identified that O. ophidiicola strains from the United States formed a clade distinct from European strains, and that multiple clonal lineages of the clade are present in the United States. The purpose of this study was to design a qPCR-based genotyping assay for O. ophidiicola, then apply that assay to swab-extracted DNA samples to investigate whether the multiple O. ophidiicola clades and clonal lineages in the United States have specific geographic, taxonomic, or temporal predilections. To this end, six full genome sequences of O. ophidiicola representing different clades and clonal lineages were aligned to identify genomic areas shared between subsets of the isolates. Eleven hydrolysis-based Taqman primer-probe sets were designed to amplify selected gene segments and produce unique amplification patterns for each isolate, each with a limit of detection of 10 or fewer copies of the target sequence and an amplification efficiency of 90–110%. The qPCR-based approach was validated using samples from strains known to belong to specific clades and applied to swab-extracted O. ophidiicola DNA samples from multiple snake species, states, and years. When compared to full-genome sequencing, the qPCR-based genotyping assay assigned 75% of samples to the same major clade (Cohen’s kappa = 0.360, 95% Confidence Interval = 0.154–0.567) with 67–77% sensitivity and 88–100% specificity, depending on clade/clonal lineage. Swab-extracted O. ophidiicola DNA samples from across the United States were assigned to six different clonal lineages, including four of the six established lineages and two newly defined groups, which likely represent recombinant strains of O. ophidiicola. Using multinomial logistic regression modeling to predict clade based on snake taxonomic group, state of origin, and year of collection, state was the most significant predictor of clonal lineage. Furthermore, clonal lineage was not associated with disease severity in the most intensely sampled species, the Lake Erie watersnake (Nerodia sipedon insularum). Overall, this assay represents a rapid, cost-effective genotyping method for O. ophidiicola that can be used to better understand the epidemiology of ophidiomycosis.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0289159","usgsCitation":"Haynes, E., Lorch, J., and Allender, M.C., 2023, Development and application of a qPCR-based genotyping assay for Ophidiomyces ophidiicola to investigate the epidemiology of ophidiomycosis: PLoS ONE, v. 18, no. 8, e0289159, 24 p., https://doi.org/10.1371/journal.pone.0289159.","productDescription":"e0289159, 24 p.","ipdsId":"IP-153457","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":442520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0289159","text":"Publisher Index Page"},{"id":419734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Haynes, Ellen","contributorId":302417,"corporation":false,"usgs":false,"family":"Haynes","given":"Ellen","email":"","affiliations":[{"id":65476,"text":"Southeastern Cooperative Wildlife Disease Study, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":880033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":260164,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":880034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allender, Matthew C.","contributorId":192522,"corporation":false,"usgs":false,"family":"Allender","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":880035,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247412,"text":"70247412 - 2023 - Tracking carbon from subduction to outgassing along the Aleutian-Alaska Volcanic Arc","interactions":[],"lastModifiedDate":"2023-08-03T12:48:44.073761","indexId":"70247412","displayToPublicDate":"2023-08-03T07:37:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Tracking carbon from subduction to outgassing along the Aleutian-Alaska Volcanic Arc","docAbstract":"Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.
Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.
Various interference reactions producing unwanted Ar isotopes from K, Ca, Cl and Ar require correction to satisfy the 40Ar/39Ar age equation. Using GEANT4, we design and build a model Cadmium Lined In Core Irradiation Tube (CLICIT) irradiation facility, as used in the Oregon State TRIGA Reactor (OSTR). We illustrate the complexity of the irradiation of geologic samples within this framework and determine an overlooked production channel of 36Ar. The production of 36Ar is fed from the 39K(n,α)36Cl nuclear channel, 36Cl subsequently decays to 36Ar (39K(n,α,β) 36Ar). Simulations in this work using a 235U fission neutron energy spectrum and modelled CLICIT facility, determine a production ratio for this reaction (36Cl/39Ar)K = 0.40 ± 0.01 (1σ); greater than an order of magnitude larger than any other K interference. The magnitude of the resulting age bias for an unknown sample will be a function of the integrated neutron flux, the length of irradiation (fluence), the time elapsed since irradiation, and the age relationship between the unknown and neutron fluence monitor. We show using the raw data of (Niespolo et al., 2017) that the age of Alder Creek sanidine can be modified to be ca. 0.1% older (1σ), at the 2σ level of current analytical precision for the Alder Creek age for this study. The 39K(n,α,β)36Ar inference should be incorporated into routine data analysis and may be especially important in the intercalibration of the 40Ar/39Ar system with other chronometers (e.g., 206Pb/238U).
The extent and seasonality of Arctic sea ice during the Last Interglacial (129,000 to 115,000 years before present) is poorly known. Sediment-based reconstructions have suggested extensive ice cover in summer, while climate model outputs indicate year-round conditions in the Arctic Ocean ranging from ice free to fully ice covered. Here we use microfossil records from across the central Arctic Ocean to show that sea-ice extent was substantially reduced and summers were probably ice free. The evidence comes from high abundances of the subpolar planktic foraminifera Turborotalita quinqueloba in five newly analysed cores. The northern occurrence of this species is incompatible with perennial sea ice, which would be associated with a thick, low-salinity surface water. Instead, T. quinqueloba’s ecological preference implies largely ice-free surface waters with seasonally elevated levels of primary productivity. In the modern ocean, this species thrives in the Fram Strait–Barents Sea ‘Arctic–Atlantic gateway’ region, implying that the necessary Atlantic Ocean-sourced water masses shoaled towards the surface during the Last Interglacial. This process reflects the ongoing Atlantification of the Arctic Ocean, currently restricted to the Eurasian Basin. Our results establish the Last Interglacial as a prime analogue for studying a seasonally ice-free Arctic Ocean, expected to occur this century.
The goal of this paper was to review the evidence of population-level impacts of the Deepwater Horizon Oil Spill (DWH) on Gulf of Mexico (GOM) continental shelf taxa, as well as evidence of resiliency following the DWH. There is considerable environmental and biological evidence that GOM shelf taxa were exposed to and suffered direct and indirect impacts of the DWH. Numerous assessments, from mesocosm studies to analysis of biopsied tissue or tissue samples from necropsied animals, revealed a constellation of physiological effects related to DWH impacts on GOM biota, some of which clearly or likely resulted in mortality. While the estimated concentrations of hydrocarbons in shelf waters and sediments were orders of magnitude lower than measured in inshore or deep GOM environments, the level of mortality observed or predicted was substantial for many shelf taxa. In some cases, such as for zooplankton, community shifts following the spill were ephemeral, likely reflecting high rates of population turnover and productivity. In other taxa, such as GOM reef fishes, impacts of the spill are confounded with other stressors, such as fishing mortality or the appearance and rapid population growth of invasive lionfish (Pterois spp.). In yet others, such as cetaceans, modeling efforts to predict population-level effects of the DWH made conservative assumptions given the species’ protected status, which post-DWH population assessments either failed to detect or population increases were estimated. A persistent theme that emerged was the lack of precise population-level data or assessments prior to the DWH for many taxa, but even when data or assessments did exist, examining evidence of population resiliency was confounded by other stressors impacting GOM biota. Unless efforts are made to increase the resolution of the data or precision of population assessments, difficulties will likely remain in estimating the scale of population-level effects or resiliency in the case of future large-scale environmental catastrophes.
In this article, we consider modeling arbitrarily censored survival data with spatio-temporal covariates. We demonstrate that under the piecewise constant hazard function, the likelihood for uncensored or right-censored subjects is proportional to the likelihood of multiple conditionally independent Poisson random variables. To address left- or interval-censored subjects, we propose to impute the exact event times and convert them into uncensored subjects, enabling the application of the integrated nested Laplace approximation to update model parameters using the imputed data. We introduce an iterative algorithm that alternates between imputing event times for left- and interval-censored subjects and re-estimating model parameters. The proposed method is assessed through a simulation study and applied to analyze a spatio-temporal survival dataset in a wildlife disease study investigating bovine tuberculosis in white-tailed deer in Michigan.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2823","usgsCitation":"Yao, K., Zhu, J., O'Brien, D., and Walsh, D.P., 2023, Bayesian spatio-temporal survival analysis for all types of censoring with application to a wildlife disease study: Environmetrics, v. 34, no. 8, e2823, 13 p., https://doi.org/10.1002/env.2823.","productDescription":"e2823, 13 p.","ipdsId":"IP-146224","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":442561,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/env.2823","text":"Publisher Index Page"},{"id":430134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yao, Kehui","contributorId":339161,"corporation":false,"usgs":false,"family":"Yao","given":"Kehui","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":903822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Jun","contributorId":73485,"corporation":false,"usgs":true,"family":"Zhu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":903823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Brien, Daniel J.","contributorId":339164,"corporation":false,"usgs":false,"family":"O'Brien","given":"Daniel J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903825,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257347,"text":"70257347 - 2023 - Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands","interactions":[],"lastModifiedDate":"2024-08-28T16:23:55.197568","indexId":"70257347","displayToPublicDate":"2023-08-01T09:11:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands","docAbstract":"Mercury (Hg) is a widespread element and persistent pollutant, harmful to fish, wildlife, and humans in its organic, methylated form. The risk of Hg contamination is driven by factors that regulate Hg loading, methylation, bioaccumulation, and biomagnification. In remote locations, with infrequent access and limited data, understanding the relative importance of these factors can pose a challenge. Here, we assessed Hg concentrations in an apex predator fish species, lake trout (Salvelinus namaycush), collected from 14 lakes spanning two National Parks in southwest Alaska, U.S.A. We then examined factors associated with the variation in fish Hg concentrations using a Bayesian hierarchical model. We found that total Hg concentrations in water were consistently low among lakes (0.11–0.50 ng L− 1). Conversely, total Hg concentrations in lake trout spanned a thirty-fold range (101–3046 ng g− 1 dry weight), with median values at 7 lakes exceeding Alaska’s human consumption threshold. Model results showed that fish age and, to a lesser extent, body condition best explained variation in Hg concentration among fish within a lake, with Hg elevated in older, thinner lake trout. Other factors, including plankton methyl Hg content, fish species richness, volcano proximity, and glacier loss, best explained variation in lake trout Hg concentration among lakes. Collectively, these results provide evidence that multiple, hierarchically nested factors control fish Hg levels in these lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2023.121678","usgsCitation":"Bartz, K.K., Hannam, M.P., Wilson, T.L., Lepak, R., Ogorek, J.M., Young, D.B., Eagles-Smith, C., and Krabbenhoft, D.P., 2023, Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands: Environmental Pollution, v. 330, 121678, 11 p., https://doi.org/10.1016/j.envpol.2023.121678.","productDescription":"121678, 11 p.","ipdsId":"IP-149237","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":442564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2023.121678","text":"Publisher Index Page"},{"id":433253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and Preserve, Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.59006149445355,\n 60.857310763857726\n ],\n [\n -155.59006149445355,\n 58.42599213711503\n ],\n [\n -152.59863331288238,\n 58.42599213711503\n ],\n [\n -152.59863331288238,\n 60.857310763857726\n ],\n [\n -155.59006149445355,\n 60.857310763857726\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"330","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":910037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hannam, Michael P.","contributorId":199775,"corporation":false,"usgs":false,"family":"Hannam","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":910038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":910041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":910042,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":910043,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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The experiments were conducted on the North Carolina coast by a multidisciplinary group of over 30 research scientists from 18 academic and federal institutions supporting over 30 graduate students and deploying over 300 instruments from 2019 to 2021. The overarching goal of DUNEX was to gather information collaboratively to improve understanding of the interactions of coastal water levels, waves, currents, beach and dune evolution, soil behavior, vegetation, and groundwater during major coastal storms that affect infrastructure, habitats, and communities. In the short term, these high-quality field measurements will lead to better understanding of during-storm processes and impacts and will enhance U.S. academic coastal research programs by providing opportunities for students to learn about field data collection and to potentially analyze data as part of their studies. Longer-term, DUNEX data and outcomes will improve the ability to predict extreme event physical processes and impacts, validate coastal processes numerical models, and improve coastal resilience strategies and communication methods for coastal communities impacted by storms. The purpose of this paper is to describe the motivation for and science goals of the experiment, how stakeholder needs led to these goals, collaborations amongst researchers, and the knowledge gained that will lead to tools to improve coastal resilience. Herein, we first describe how researchers worked with stakeholders to structure their community-driven needs into science-based requirements. Next, we summarize how federal, academic, and stakeholder researchers worked together to design and execute a multi-organizational experiment aligned with those requirements. Finally, we articulate early findings and lessons learned from the experiment. This paper does not summarize all the research findings from DUNEX, as analyses are still ongoing. An American Geophysical Union (AGU) Special Collection on Coastal Storm Research will be published in 2025 including outcomes from DUNEX research.
","language":"English","publisher":"American Shore & Beach Preservation Association (ASBPA)","doi":"10.34237/1009133","usgsCitation":"Straub, J.A., Cialone, M.A., Raubenheimer, B., Brown, J., Elko, N., and Brodie, K., 2023, The During Nearshore Event Experiment (DUNEX): A collaborative coastal community experiment to address coastal resilience: Shore & Beach, v. 91, no. 3, p. 23-29, https://doi.org/10.34237/1009133.","productDescription":"7 p.","startPage":"23","endPage":"29","ipdsId":"IP-154913","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":420560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.96092024961042,\n 36.52798524878611\n ],\n [\n -75.70287550901757,\n 35.7225387686793\n ],\n [\n -75.58916087756994,\n 35.32743140299219\n ],\n [\n -76.31955870263806,\n 34.95189496181462\n ],\n [\n -76.57760344323091,\n 34.71855336952575\n ],\n [\n -77.17241843849592,\n 34.682596002484786\n ],\n [\n -77.95092502360447,\n 34.188407272265394\n ],\n [\n -78.06901329472343,\n 33.956547416969514\n ],\n [\n -78.55448729821158,\n 33.89485306093033\n ],\n [\n -78.46264086511943,\n 33.75678659018945\n ],\n [\n -77.87219950952516,\n 33.78223663539556\n ],\n [\n -77.48294557880051,\n 34.383544699373275\n ],\n [\n -76.50325029248162,\n 34.54581268018919\n ],\n [\n -75.39672570300596,\n 35.17026943934029\n ],\n [\n -75.42296896493126,\n 35.83252729550989\n ],\n [\n -75.74661830059003,\n 36.5525741300887\n ],\n [\n -75.96092024961042,\n 36.52798524878611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Straub, Jessamin A. 0000-0001-5630-5741","orcid":"https://orcid.org/0000-0001-5630-5741","contributorId":267195,"corporation":false,"usgs":false,"family":"Straub","given":"Jessamin","email":"","middleInitial":"A.","affiliations":[{"id":55437,"text":"U.S. Army Engineer Research and Development Center, Field Research Facility, Duck, NC, USA","active":true,"usgs":false}],"preferred":false,"id":882088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cialone, Mary A.","contributorId":329373,"corporation":false,"usgs":false,"family":"Cialone","given":"Mary","email":"","middleInitial":"A.","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":882089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raubenheimer, Britt","contributorId":194340,"corporation":false,"usgs":false,"family":"Raubenheimer","given":"Britt","email":"","affiliations":[],"preferred":false,"id":882090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":882091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elko, Nicole","contributorId":287920,"corporation":false,"usgs":false,"family":"Elko","given":"Nicole","affiliations":[{"id":61663,"text":"American Shore and Beach Preservation Association","active":true,"usgs":false}],"preferred":false,"id":882092,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brodie, Katherine L.","contributorId":271224,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine L.","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":882093,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}