In 2001 and 2002, 52 elk (Cervus canadensis; 21 males, 31 females), originally obtained from Elk Island National Park, Alberta, Canada, were transported and released into Cataloochee Valley in the northeastern portion of Great Smoky Mountains National Park (GRSM, Park), North Carolina, USA. The annual population growth rate (λ) was negative (0.996, 95% CI = 0.945–1.047) and predation by black bears (Ursus americanus) on elk calves was identified as an important determinant of population growth. From 2006 to 2008, 49 bears from the primary elk calving area (i.e., Cataloochee Valley) were trapped and translocated about 70 km to the southwestern portion of the Park just prior to elk calving. Per capita recruitment (i.e., the number of calves produced per adult female that survive to 1 year of age) increased from 0.306 prior to bear translocation (2001–2005) to 0.544 during years when bears were translocated (2006–2008) and λ increased to 1.118 (95% CI = 1.096–1.140). Our objective was to determine whether per capita calf recruitment rates after bear removal (2009–2019) at Cataloochee were similar to the higher rates estimated during bear removal (i.e., long-term response) or if they returned to rates before bear removal (i.e., short-term response), and how those rates compared with recruitment from portions of our study area where bears were not relocated. We documented 419 potential elk calving events and monitored 129 yearling and adult elk from 2001 to 2019. Known-fate models based on radio-telemetry and observational data supported calf recruitment returning to pre-2006 levels at Cataloochee (short-term response); recruitment of Cataloochee elk before and after bear relocation was lower (0.184) than during bear relocation (0.492). Recruitment rates of elk outside the removal area during the bear relocation period (0.478) were similar to before and after rates (0.420). In the Cataloochee Valley, cause-specific annual calf mortality rates due to predation by bears were 0.319 before, 0.120 during, and 0.306 after bear relocation. In contrast, the cause-specific annual mortality rate of calves in areas where bears were not relocated was 0.033 after the bear relocation period, with no bear predation on calves before or during bear relocation. The mean annual population growth rate for all monitored elk was 1.062 (95% CI = 0.979–1.140) after bear relocation based on the recruitment and survival data. Even though the effects of bear removal were temporary, the relocations were effective in achieving a short-term increase in elk recruitment, which was important for the reintroduction program given that the elk population was small and vulnerable to extirpation.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22522","usgsCitation":"Yarkovich, J.G., Braunstein, J.L., Mullinax, J.M., and Clark, J.D., 2024, No long-term effect of black bear removal on elk calf recruitment in the southern Appalachians: Journal of Wildlife Management, v. 88, e22522, 19 p., https://doi.org/10.1002/jwmg.22522.","productDescription":"e22522, 19 p.","ipdsId":"IP-151115","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":441068,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22522","text":"Publisher Index Page"},{"id":432859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.39446616307593,\n 35.34908542134272\n ],\n [\n -83.0636158648346,\n 35.47482669066018\n ],\n [\n -82.86476283583953,\n 35.658891970547856\n ],\n [\n -83.0207596085856,\n 35.74798343179633\n ],\n [\n -83.14761412708188,\n 35.64914155318964\n ],\n [\n -83.38589491182597,\n 35.51530150997276\n ],\n [\n -83.54360593482207,\n 35.45388343943314\n ],\n [\n -83.44760792082488,\n 35.32251496983187\n ],\n [\n -83.39446616307593,\n 35.34908542134272\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","noUsgsAuthors":false,"publicationDate":"2023-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Yarkovich, Joseph G.","contributorId":244820,"corporation":false,"usgs":false,"family":"Yarkovich","given":"Joseph","email":"","middleInitial":"G.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":910494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braunstein, Jessica L.","contributorId":342231,"corporation":false,"usgs":false,"family":"Braunstein","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":910495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":910496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":910497,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249890,"text":"70249890 - 2024 - Improved computational methods for probabilistic liquefaction hazard analysis","interactions":[],"lastModifiedDate":"2023-11-04T13:43:45.070558","indexId":"70249890","displayToPublicDate":"2023-11-03T08:42:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3418,"text":"Soil Dynamics and Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Improved computational methods for probabilistic liquefaction hazard analysis","docAbstract":"Current procedures for analysis of and design against liquefaction hazards focus primarily on the use of probabilistic ground motions at a single ground-shaking hazard level, with the cyclic loading represented by a peak ground acceleration (PGA) corresponding to a target return period and a single representative moment magnitude Mw. These parameters are typically used in conjunction with deterministic simplified procedures for estimating liquefaction triggering and consequences that largely neglect the considerable uncertainties inherent to liquefaction problems. Motivated by these limitations and the resulting inconsistencies in liquefaction design levels, early methods for probabilistic liquefaction hazard analysis (PLHA) were proposed that incorporate the full ground motion hazard space, integrated with probabilistic liquefaction triggering models. Such methods provide liquefaction factor of safety (FSL) hazard curves for standard penetration test (SPT) data. Recognizing the increased use of higher-resolution cone penetrometer test (CPT) data in engineering analysis and design and the potential computational challenges it presents, an expanded suite of probabilistic triggering models, and wider availability of more detailed seismic hazard data, an improved PLHA computational methodology is presented in this study. The methods described utilize the U.S. Geological Survey National Seismic Hazard Model directly through web services to obtain PGA hazard and disaggregation calculations for any site and average shear wave velocity in the upper 30 m of the site (Vs30) in the conterminous United States, to reconstruct the PGA hazard space for use in PLHA calculations, and to employ array calculations for efficient liquefaction hazard curve estimates for the thousands of CPT measurements in a given profile. The framework presented here is modular in nature, and discussion on the use of alternative models and extension to probabilistic liquefaction consequence evaluation is presented. The importance of appropriate representation of probabilistic liquefaction model uncertainties is also highlighted, along with the impacts of different levels of uncertainty on the PLHA calculation. Finally, a potential roadmap for incorporation of the PLHA framework in seismic provisions is presented, with an illustration of how it can address current limitations and impacts in liquefaction hazard analysis and design.
The United States (U.S.) has over 90,000 dams listed in the National Inventory of Dams that provide vital infrastructure to support water management for municipal and industrial uses including irrigation, hydropower, flood control, navigation, recreation, and habitat, among other uses (NID 2023). The Bureau of Reclamation (Reclamation) and U.S. Army Corps of Engineers (USACE) operate and maintain approximately 489 and 740 dams, respectively, as well as associated structures which provide flood risk management, navigation, water supply, hydropower, environmental stewardship, fish and wildlife conservation, and recreation benefits. As dams age, structural and operational maintenance investments increase until a time when decisions on whether to rehabilitate, replace, or decommission the dam need to be made. While most dams continue to provide important value even with maintenance requirements, at least 2,000 dams have been removed in the U.S. during the past 110 years, with an upward trend in the last few decades (American Rivers 2023). Decommissioning a dam may be considered when the purpose of the dam is no longer needed or other factors such as dam safety, fish passage, recreation safety, or river restoration goals take higher priority and are more economically feasible for the dam owner long-term.
Dam safety programs, river restoration programs, and asset class management programs need cost estimating methods to consider dam decommissioning when appropriate. Traditional cost estimating approaches in planning stages focus mainly on dam removal construction and may leave out or have uncertainty on important complexities that can have substantial effects on total costs and be critical for project success. As the numbers of dam removal case studies increase, a growing set of cost data has become available (Duda et al. 2023a; Tullos and Bountry 2023; American Rivers 2022). However, total costs vary over five orders of magnitude for similar size dams, and it was unclear why. We evaluated three sets of cost data that had varying level of details regarding elements contributing to dam removal costs reported by project managers working on the dam removal studies and construction means and methods.
We created planning-level cost estimating tools to assist with projects needing to consider the dam removal alternative: (1) new databases of case studies (Duda et al. 2023a; Tullos and Bountry 2023); (2) scoping questions to help determine if complexity cost drivers will be present; (3) machine learning based regression trees to estimate a potential cost range; and (4) a Computation Guide for Cost Estimating that can be used to inform discussions on potential dam removal cost items, quantities, and unit costs (appendix A). The collected data showed that dam height is important but is not a reliable predictor of the removal cost without considering other elements. However, knowing some basic characteristics about the average annual flow and geographic location of the dam site, in addition to dam size, can improve the ability to use past case studies for planning-level cost estimating. By additionally incorporating scoping questions related to sediment removal, mitigation, or other infrastructure, the likelihood of complexity cost drivers and the initial uncertainty of a cost estimate can be further reduced especially for small dams. Applying the Computation Guide for Cost Estimating requires more robust information but helps users reduce cost uncertainty. This step further refines the dam removal objective, removal approach (partial or full; phased or instantaneous), engineering design, construction means and methods, quantities, and unit costs, and results in a quantitative cost estimate.
","language":"English","publisher":"Bureau of Reclamation","collaboration":"Army Corps of Engineers, Bureau of Reclamation, Oregon State University","usgsCitation":"Bountry, J.A., Randle, T.J., Jansen, A., Duda, J.J., Jumani, S., Tullos, D.D., McKay, K., and Bailey, S., 2024, Dam removal cost databases and drivers: Final Report ST-2023-21084 and ENV-2023-002, 60 p.","productDescription":"60 p.","ipdsId":"IP-156982","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":427267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427257,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://data.usbr.gov/catalog/7975/item/128527"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bountry, Jennifer A.","contributorId":30114,"corporation":false,"usgs":false,"family":"Bountry","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":897732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Randle, Timothy J.","contributorId":90994,"corporation":false,"usgs":false,"family":"Randle","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":897733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jansen, Alvin","contributorId":317292,"corporation":false,"usgs":false,"family":"Jansen","given":"Alvin","email":"","affiliations":[{"id":68995,"text":"Technical Service Center, Bureau of Reclamation, Denver, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":897734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jumani, Suman 0000-0002-2292-7996","orcid":"https://orcid.org/0000-0002-2292-7996","contributorId":305995,"corporation":false,"usgs":false,"family":"Jumani","given":"Suman","email":"","affiliations":[{"id":66338,"text":"Network for Engineering with Nature, Georgia, USA","active":true,"usgs":false}],"preferred":false,"id":897736,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tullos, Desiree D.","contributorId":176667,"corporation":false,"usgs":false,"family":"Tullos","given":"Desiree","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":897737,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKay, Kyle","contributorId":335212,"corporation":false,"usgs":false,"family":"McKay","given":"Kyle","email":"","affiliations":[{"id":80343,"text":"Engineer Research and Development Center – Environmental Laboratory, U.S. Army Corps of Engineers, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":897738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bailey, Susan","contributorId":317293,"corporation":false,"usgs":false,"family":"Bailey","given":"Susan","email":"","affiliations":[{"id":68996,"text":"Engineer Research and Development Center - Environmental Laboratory, U.S. Army Corps of Engineers, Vicksburg, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":897739,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70251333,"text":"70251333 - 2024 - Systematic process for determining field-sampling effort required to know vegetation changes in large, disturbed rangelands where management treatments have been applied","interactions":[],"lastModifiedDate":"2024-02-07T01:17:06.668798","indexId":"70251333","displayToPublicDate":"2023-10-29T19:15:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Systematic process for determining field-sampling effort required to know vegetation changes in large, disturbed rangelands where management treatments have been applied","docAbstract":"Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.
Seasonal migration, driven by shifts in annual climate cycles and resources, is a key part of the life history and ecology of species across taxonomic groups. By influencing the amount of energy needed to move, external forces such as wind and ocean currents are often key drivers of migratory pathways exposing individuals to varying resources, environmental conditions, and competition pressures impacting individual fitness and population dynamics. Although wildlife movements in connection with wind and ocean currents are relatively well understood, movements within sea-ice fields have been much less studied, despite sea ice being an integral part of polar ecology. Adélie penguins (Pygoscelis adeliae) in the southern Ross Sea, Antarctica, currently exist at the southernmost edge of their range and undergo the longest (~12,000 km) winter migration known for the species. Within and north of the Ross Sea, the Ross Gyre drives ocean circulation and the large-scale movement of sea ice. We used remotely sensed sea-ice movement data together with geolocation-based penguin movement data to test the hypothesis that penguins use gyre-driven sea-ice movement to aid their migration. We found that penguins traveled greater distances when their movement vectors were aligned with those of sea ice (i.e., ice support) and the amount of ice support received depended on which route a penguin took. We also found that birds that took an eastern route traveled significantly further north in two of the 3 years we examined, coinciding with higher velocities of sea ice in those years. We compare our findings to patterns observed in migrating species that utilize air or water currents for their travel and with other studies showing the importance of ocean/sea-ice circulation patterns to wildlife movement and life history patterns within the Ross Sea. Changes in sea ice may have consequences not only for energy expenditure but, by altering migration and movement pathways, to the ecological interactions that exist in this region.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.4196","usgsCitation":"Jongsomjit, D., Lescroël, A., Schmidt, A., Lisovski, S., Ainley, D.G., Hines, E., Elrod, M., Dugger, K., and Ballard, G., 2024, Going with the floe: Sea-ice movement affects distance and destination during Adélie penguin winter movements: Ecology, v. 105, e4196, 17 p., https://doi.org/10.1002/ecy.4196.","productDescription":"e4196, 17 p.","ipdsId":"IP-150837","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4196","text":"Publisher Index Page"},{"id":430041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n -60\n ],\n [\n -179.9,\n -80\n ],\n [\n -140,\n -80\n ],\n [\n -140,\n -60\n ],\n [\n -179.9,\n -60\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n -60\n ],\n [\n 130,\n -60\n ],\n [\n 130,\n -80\n ],\n [\n 179.9,\n -80\n ],\n [\n 179.9,\n -60\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"105","noUsgsAuthors":false,"publicationDate":"2024-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Jongsomjit, Dennis","contributorId":276370,"corporation":false,"usgs":false,"family":"Jongsomjit","given":"Dennis","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":903221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lescroël, Amelie","contributorId":275177,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amelie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Annie","contributorId":338340,"corporation":false,"usgs":false,"family":"Schmidt","given":"Annie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lisovski, Simeon","contributorId":337936,"corporation":false,"usgs":false,"family":"Lisovski","given":"Simeon","affiliations":[{"id":62783,"text":"Alfred Wegener Institute","active":true,"usgs":false}],"preferred":false,"id":903224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ainley, David G.","contributorId":32039,"corporation":false,"usgs":false,"family":"Ainley","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":34154,"text":"Point Reyes Bird Observatory, Stinson Beach, CA","active":true,"usgs":false}],"preferred":false,"id":903225,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hines, Ellen","contributorId":111908,"corporation":false,"usgs":true,"family":"Hines","given":"Ellen","affiliations":[],"preferred":false,"id":903226,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Elrod, Megan","contributorId":197717,"corporation":false,"usgs":false,"family":"Elrod","given":"Megan","affiliations":[],"preferred":false,"id":903227,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903228,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ballard, Grant","contributorId":276385,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":903229,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249773,"text":"70249773 - 2024 - Spatial distribution and diet of Lake Michigan juvenile lake trout","interactions":[],"lastModifiedDate":"2024-02-07T17:09:21.53758","indexId":"70249773","displayToPublicDate":"2023-10-25T06:44:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution and diet of Lake Michigan juvenile lake trout","docAbstract":"Most studies of Lake Michigan lake trout (Salvelinus namaycush) have focused on adults, with scant attention to juveniles (<400 mm). We explored the spatial distribution and diet of juvenile lake trout using U.S. Geological Survey September bottom trawl data (2015–2022) and stomach content information opportunistically collected since 2012 by various agencies using multiple gear types. Most juvenile lake trout in the September bottom trawl survey were caught at 37–64 m bottom depths. Length frequency data from the bottom trawl survey identified three size classes likely associated with wild juvenile lake trout age: < 85 mm (∼age-0), 85–170 mm (∼age-1) and > 170 mm (∼age-2+). Largest catches of wild lake trout < 170 mm occurred along a northeastern transect (near Frankfort, Michigan), whereas most > 170 mm were collected along southern transects. Mysis diluviana was the dominant prey for juvenile lake trout < 170 mm, and > 250 mm were primarily piscivorous, while 170–250 mm appeared to be a transitional period of switching from Mysis to fish. Species composition of prey fishes consumed by lake trout varied spatially and we found evidence of seasonal and annual diet variation within Grand Traverse Bay. Diporeia, once an important component of juvenile lake trout diet, appears to no longer be consumed by juvenile lake trout in Lake Michigan to any measurable degree. Continued research on the ecology of juvenile lake trout may provide insight into the effects of a changing ecosystem on juvenile lake trout diet and growth, thereby contributing to the effort to rehabilitate the Lake Michigan lake trout population.
Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017–2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (ChlBS). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (ChlBS) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of ChlBS were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between ChlBS and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.
Genetic and genomic data are increasingly used to aid conservation management of endangered species by providing insights into evolutionary histories, factors associated with extinction risks, and potential for future adaptation. For the ‘Alalā, or Hawaiian crow (Corvus hawaiiensis), genetic concerns include negative correlations between inbreeding and hatching success. However, it is unclear if low genetic diversity and inbreeding depression are consequences of a historical population bottleneck, or if ‘Alalā had historically low genetic diversity that predated human influence, perhaps as a result of earlier declines or founding events. In this study, we applied a hybridization-based sequence capture to generate a genome-wide single nucleotide polymorphism (SNP) dataset for comparing historical specimens collected in the 1890s, when ‘Alalā were more numerous, to samples taken between 1973 and 1998, when ‘Alalā population densities were near the lowest documented levels in the wild, prior to all individuals being collected for captive rearing. We found low genome-wide diversity in both sample groups, however, the modern sample group (1973 to 1998 cohort) exhibited relatively fewer polymorphic alleles, a lower proportion of polymorphic loci, and lower observed heterozygosity, consistent with a population decline and potential bottleneck effects. These results combined with a current low population size highlight the importance of continued efforts by conservation managers to mitigate inbreeding and maintain founder representation to preserve what genetic diversity remains.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esad063","usgsCitation":"Blanchet, G., Bellinger, M.R., Kearns, A., Cortes-Rodriguez, N., Masuda, B., Campana, M.G., Rutz, C., Fleischer, R., and Sutton, J., 2024, Reduction of genetic diversity in ‘Alalā (Hawaiian crow; Corvus hawaiiensis) between the late 1800s and the late 1900s: Journal of Heredity, v. 115, no. 1, p. 32-44, https://doi.org/10.1093/jhered/esad063.","productDescription":"13 p.","startPage":"32","endPage":"44","ipdsId":"IP-153519","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":482510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.66271325336368,\n 18.886427440489825\n ],\n [\n -154.73075561200312,\n 19.494541117345733\n ],\n [\n -155.18826209048927,\n 20.004873708176405\n ],\n [\n -155.86040123789476,\n 20.301808200772868\n ],\n [\n -156.07503390681424,\n 19.718012347687846\n ],\n [\n -155.9394764317072,\n 19.02531729996751\n ],\n [\n -155.66271325336368,\n 18.886427440489825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Blanchet, Geneviève","contributorId":351501,"corporation":false,"usgs":false,"family":"Blanchet","given":"Geneviève","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":928727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellinger, Mona Renee 0000-0001-5274-9572","orcid":"https://orcid.org/0000-0001-5274-9572","contributorId":301018,"corporation":false,"usgs":true,"family":"Bellinger","given":"Mona","email":"","middleInitial":"Renee","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":928728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kearns, Anna M. 0000-0002-8502-7442","orcid":"https://orcid.org/0000-0002-8502-7442","contributorId":351502,"corporation":false,"usgs":false,"family":"Kearns","given":"Anna M.","affiliations":[{"id":84000,"text":"Smithsonian Institution, Washington DC, USA","active":true,"usgs":false}],"preferred":false,"id":928729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cortes-Rodriguez, Nandadevi 0000-0002-2922-8012","orcid":"https://orcid.org/0000-0002-2922-8012","contributorId":351503,"corporation":false,"usgs":false,"family":"Cortes-Rodriguez","given":"Nandadevi","affiliations":[{"id":84001,"text":"Ithaca College, Ithaca, New York, USA","active":true,"usgs":false}],"preferred":false,"id":928730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Masuda, Bryce M.","contributorId":351504,"corporation":false,"usgs":false,"family":"Masuda","given":"Bryce M.","affiliations":[{"id":65735,"text":"San Diego Zoo Wildlife Alliance","active":true,"usgs":false}],"preferred":false,"id":928731,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Campana, Michael G.","contributorId":258060,"corporation":false,"usgs":false,"family":"Campana","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":52221,"text":"Center for Conservation Genomics, Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":928732,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rutz, Christian 0000-0001-5187-7417","orcid":"https://orcid.org/0000-0001-5187-7417","contributorId":351505,"corporation":false,"usgs":false,"family":"Rutz","given":"Christian","affiliations":[{"id":84002,"text":"University of St Andrews, St Andrews, Scotland, UK","active":true,"usgs":false}],"preferred":false,"id":928733,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fleischer, Robert C.","contributorId":258062,"corporation":false,"usgs":false,"family":"Fleischer","given":"Robert C.","affiliations":[{"id":52221,"text":"Center for Conservation Genomics, Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":928734,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sutton, Jolene T.","contributorId":351506,"corporation":false,"usgs":false,"family":"Sutton","given":"Jolene T.","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":928735,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250323,"text":"70250323 - 2024 - Target and suspect per- and polyfluoroalkyl substances in fish from an AFFF-impacted waterway","interactions":[],"lastModifiedDate":"2023-12-04T16:10:14.574234","indexId":"70250323","displayToPublicDate":"2023-10-16T09:45:38","publicationYear":"2024","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":"Target and suspect per- and polyfluoroalkyl substances in fish from an AFFF-impacted waterway","title":"Target and suspect per- and polyfluoroalkyl substances in fish from an AFFF-impacted waterway","docAbstract":"A major source of toxic per- and polyfluoroalkyl substances (PFAS) is aqueous film-forming foams (AFFF) used in firefighting and training at airports and military installations, however, PFAS have many additional sources in consumer products and industrial processes. A field study was conducted on fish tissues from three reaches of the Columbia Slough, located near Portland International Airport, OR, that are affected by AFFF and other PFAS sources. Fishes including largescale sucker (Catostomus macrocheilus), goldfish (Carassius auratus), and largemouth bass (Micropterus salmoides) were collected in 2019 and 2020. Fish blood, liver, and fillet (muscle) were analyzed for target and suspect PFAS by liquid chromatography high resolution mass spectrometry (LC-HRMS). Data were analyzed for patterns by fish species, tissue type, and river reach. Thirty-three out of 50 target PFAS and additional suspect compounds were detected at least once during the study, at concentrations up to 856 ng/g. Seven carboxylic acids (PFOA, PFNA, PFDA, PFUdA, PFDoA, PFTrDA, PFTeDA), three sulfonates (PFHxS, PFOS, PFDS), three electrofluorination-based compounds (FBSA, FHxSA, FOSA), and two fluorotelomer-based compounds (8:2 FTS, 10:2 FTS) were the most frequently detected compounds in all tissue types. The C6 (PFHxS) to C10 (PFDS) homologs were detected with PFOS and FHxSA at concentrations 1–3 orders of magnitude greater than the other PFAS detected. This is the first report of Cl-PFOS, FPeSA, and FHpSA detected in fish tissue. In all fish samples, fillet concentrations of PFAS were the lowest, followed by liver, and blood concentrations of PFAS were the highest. Differences in PFAS concentrations were driven primarily by tissue types and to a lesser extent fish species, but weakly by river reach. The Oregon Health Authority modified an existing fish consumption advisory on the Columbia Slough to recommend no whole-body consumption of most fish to avoid elevated levels of PFOS in fish liver. Measured PFAS concentrations in fish tissues indicate the potential for adverse ecological effects.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.167798","usgsCitation":"Nilsen, E., Muensterman, D.J., Carini, L., Waite, I.R., Payne, S.E., Field, J., Peterson, J.L., Hafley, D., Farrer, D., and Jones, G.D., 2024, Target and suspect per- and polyfluoroalkyl substances in fish from an AFFF-impacted waterway: Science of the Total Environment, v. 906, 167798, 11 p., https://doi.org/10.1016/j.scitotenv.2023.167798.","productDescription":"167798, 11 p.","ipdsId":"IP-134199","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":441111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2023.167798","text":"Publisher Index Page"},{"id":423178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Columbia Slough","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.77377259108432,\n 45.6551878154539\n ],\n [\n -122.77377259108432,\n 45.38008686160234\n ],\n [\n -122.35368994878024,\n 45.38008686160234\n ],\n [\n -122.35368994878024,\n 45.6551878154539\n ],\n [\n -122.77377259108432,\n 45.6551878154539\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"906","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nilsen, Elena 0000-0002-0104-6321","orcid":"https://orcid.org/0000-0002-0104-6321","contributorId":212096,"corporation":false,"usgs":true,"family":"Nilsen","given":"Elena","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":889447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muensterman, Derek J. 0000-0002-7911-3563","orcid":"https://orcid.org/0000-0002-7911-3563","contributorId":298775,"corporation":false,"usgs":false,"family":"Muensterman","given":"Derek","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":889448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carini, Lya 0009-0002-2884-7118","orcid":"https://orcid.org/0009-0002-2884-7118","contributorId":332100,"corporation":false,"usgs":false,"family":"Carini","given":"Lya","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":889449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":889450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Payne, Sean E. 0000-0003-1836-1886 spayne@usgs.gov","orcid":"https://orcid.org/0000-0003-1836-1886","contributorId":292581,"corporation":false,"usgs":true,"family":"Payne","given":"Sean","email":"spayne@usgs.gov","middleInitial":"E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":889451,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Field, Jennifer 0000-0002-9346-4693","orcid":"https://orcid.org/0000-0002-9346-4693","contributorId":223447,"corporation":false,"usgs":false,"family":"Field","given":"Jennifer","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":889452,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterson, Jennifer L 0009-0008-6762-3188","orcid":"https://orcid.org/0009-0008-6762-3188","contributorId":332101,"corporation":false,"usgs":false,"family":"Peterson","given":"Jennifer","email":"","middleInitial":"L","affiliations":[{"id":27064,"text":"Oregon Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":889453,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hafley, Daniel","contributorId":332103,"corporation":false,"usgs":false,"family":"Hafley","given":"Daniel","email":"","affiliations":[{"id":27064,"text":"Oregon Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":889454,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Farrer, David 0009-0009-5814-2363","orcid":"https://orcid.org/0009-0009-5814-2363","contributorId":332123,"corporation":false,"usgs":false,"family":"Farrer","given":"David","email":"","affiliations":[],"preferred":false,"id":889455,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Gerrad D 0000-0002-1529-9506","orcid":"https://orcid.org/0000-0002-1529-9506","contributorId":332105,"corporation":false,"usgs":false,"family":"Jones","given":"Gerrad","email":"","middleInitial":"D","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":889456,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249964,"text":"70249964 - 2024 - Recent, widespread nitrate decreases may be linked to persistent dissolved organic carbon increases in headwater streams recovering from past acidic deposition","interactions":[],"lastModifiedDate":"2023-11-09T12:37:16.746256","indexId":"70249964","displayToPublicDate":"2023-10-11T06:36:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Recent, widespread nitrate decreases may be linked to persistent dissolved organic carbon increases in headwater streams recovering from past acidic deposition","docAbstract":"Long-term monitoring of water quality responses to natural and anthropogenic perturbation of watersheds informs policies for managing natural resources. Dissolved organic carbon (DOC) and nitrate (NO3−) in streams draining forested landscapes provide valuable information on ecosystem function due to their biogeochemical reactivity and solubility in water. Here we evaluate a 20-year record (2001−2021) of biweekly stream-water samples (n > 3000) and continuous discharge in three forested catchments in the Adirondack region of New York to investigate and interpret long-term trends in DOC and NO3− concentrations. Results from the intensively monitored catchments were compared with data from synoptic surveys of streams throughout the Adirondack region. A weighted regressions on time, discharge, and season (WRTDS) model, used to estimate daily flow-normalized concentrations, determined that DOC increased by ~30 to 50 % while NO3− decreased by ~50 to 70 % over the study period. The large amount of data from catchments with different soil properties permitted us to assess the relative effects of hydrology, season, and land cover factors on temporal trends in DOC and NO3− concentrations. We found weak evidence of climatic forcing of long-term increases in DOC, and instead contend that declining ionic strength in precipitation linked to declining anthropogenic acid deposition is driving DOC trends in stream waters. Nitrate concentrations were more variable but clearly decreased in recent years possibly related to declining N deposition. The recent increase in DOC:NO3− in all catchments indicates a major shift in stream stoichiometry that reflects changes in ecosystem functioning that may have important biogeochemical implications for terrestrial as well as aquatic ecosystems.
Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.4836","usgsCitation":"Rowland, F.E., Kotalik, C.J., Marcot, B.G., Hinck, J.E., and Walters, D., 2024, A novel approach to assessing natural resource injury with Bayesian networks: Integrated Environmental Assessment and Management, v. 20, no. 2, p. 562-573, https://doi.org/10.1002/ieam.4836.","productDescription":"12 p.","startPage":"562","endPage":"573","ipdsId":"IP-151776","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":441142,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.4836","text":"Publisher Index Page"},{"id":421749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rowland, Freya Elizabeth 0000-0002-1041-5301","orcid":"https://orcid.org/0000-0002-1041-5301","contributorId":302395,"corporation":false,"usgs":true,"family":"Rowland","given":"Freya","email":"","middleInitial":"Elizabeth","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":885533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kotalik, Christopher James 0000-0001-6739-6036","orcid":"https://orcid.org/0000-0001-6739-6036","contributorId":301847,"corporation":false,"usgs":true,"family":"Kotalik","given":"Christopher","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":885534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marcot, Bruce G.","contributorId":140456,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":885535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hinck, Jo Ellen 0000-0002-4912-5766 jhinck@usgs.gov","orcid":"https://orcid.org/0000-0002-4912-5766","contributorId":2743,"corporation":false,"usgs":true,"family":"Hinck","given":"Jo","email":"jhinck@usgs.gov","middleInitial":"Ellen","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":885536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":203410,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":885537,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255294,"text":"70255294 - 2024 - Identifying demographic and environmental drivers of population dynamics and viability in an endangered top predator using an integrated model","interactions":[],"lastModifiedDate":"2024-06-14T12:07:19.042021","indexId":"70255294","displayToPublicDate":"2023-10-06T07:01:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Identifying demographic and environmental drivers of population dynamics and viability in an endangered top predator using an integrated model","docAbstract":"Knowledge about the demographic and environmental factors underlying population dynamics is fundamental to designing effective conservation measures to recover depleted wildlife populations. However, sparse monitoring data or persistent knowledge gaps about threats make it difficult to identify the drivers of population dynamics. In situations where small, declining, or depleted populations show continued evidence of decline for unknown reasons, integrated population models can make efficient use of available data to improve our understanding of demography, provide fundamental insights into factors that may be limiting recovery, and support conservation decisions. We used mark-resight and aerial survey data from 2004 to 2018 to build a Bayesian integrated population model for the Cook Inlet population of beluga whales (Delphinapterus leucas), which is listed as endangered under the U.S. Endangered Species Act. We examined the effects of prey availability and oceanographic conditions on beluga vital rates and conducted a population viability analysis to predict extinction risk across a range of hypothetical changes in beluga survival and reproduction. Our results indicated that while the survival of breeding females (0.97; 95% CI: 0.95–0.99) and young calves (0.92; 0.80–0.98) was relatively high, the survival of nonbreeders (0.94; 0.91–0.97) and fecundity (0.28; 0.22–0.36) may be depressed. Furthermore, our analysis indicates that the population will likely continue to decline, with a 17–32% probability of extinction in 150 years. Our model highlights the utility of integrated population modeling for maximizing the usefulness of available data and identifying factors contributing to the failure of protected populations to recover. This framework can be used to evaluate proposed conservation and recovery efforts for this and other endangered species.
Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.
","language":"English","publisher":"Society of Exploration Geophysicists and American Association of Petroleum Geologists","doi":"10.1190/int-2023-0056.1","usgsCitation":"Chong, L., Collett, T., Creason, C.G., Seol, Y., and Myshakin, E., 2024, Machine learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India: Journal Interpretation, v. 12, p. T63-T75, https://doi.org/10.1190/int-2023-0056.1.","productDescription":"13 p.","startPage":"T63","endPage":"T75","ipdsId":"IP-151255","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467055,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2059620","text":"External Repository"},{"id":422953,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 81,\n 16.75\n ],\n [\n 81,\n 15.4\n ],\n [\n 83.25,\n 15.4\n ],\n [\n 83.25,\n 16.75\n ],\n [\n 81,\n 16.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2023-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Chong, Leebyn","contributorId":331733,"corporation":false,"usgs":false,"family":"Chong","given":"Leebyn","email":"","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":888643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":888644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creason, C. Gabriel","contributorId":331734,"corporation":false,"usgs":false,"family":"Creason","given":"C.","email":"","middleInitial":"Gabriel","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":888645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seol, Yongkoo","contributorId":195139,"corporation":false,"usgs":false,"family":"Seol","given":"Yongkoo","email":"","affiliations":[],"preferred":false,"id":888646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myshakin, E.M.","contributorId":229601,"corporation":false,"usgs":false,"family":"Myshakin","given":"E.M.","email":"","affiliations":[{"id":41691,"text":"Office of Research and Development, National Energy Technology Laboratory, Morgantown, WV, USA","active":true,"usgs":false}],"preferred":false,"id":888647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250643,"text":"70250643 - 2024 - Episodic evolution of a protracted convergent margin revealed by detrital zircon geochronology in the Greater Caucasus","interactions":[],"lastModifiedDate":"2024-01-25T14:53:03.954794","indexId":"70250643","displayToPublicDate":"2023-09-30T06:58:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":972,"text":"Basin Research","active":true,"publicationSubtype":{"id":10}},"title":"Episodic evolution of a protracted convergent margin revealed by detrital zircon geochronology in the Greater Caucasus","docAbstract":"Convergent margins play a fundamental role in the construction and modification of Earth's lithosphere and are characterized by poorly understood episodic processes that occur during the progression from subduction to terminal collision. On the northern margin of the active Arabia-Eurasia collision zone, the Greater Caucasus Mountains provide an opportunity to study a protracted convergent margin that spanned most of the Phanerozoic and culminated in Cenozoic continental collision. However, the main episodes of lithosphere formation and deformation along this margin remain enigmatic. Here, we use detrital zircon U–Pb geochronology from Paleozoic and Mesozoic (meta)sedimentary rocks in the Greater Caucasus, along with select zircon U–Pb and Hf isotopic data from coeval igneous rocks, to link key magmatic and depositional episodes along the Caucasus convergent margin. Devonian to Early Carboniferous rocks were deposited prior to Late Carboniferous accretion of the Greater Caucasus crystalline core onto the Laurussian margin. Permian to Triassic rocks document a period of northward subduction and forearc deposition south of a continental margin volcanic arc in the Northern Caucasus and Scythian Platform. Jurassic rocks record the opening of the Caucasus Basin as a back-arc rift during southward migration of the arc front into the Lesser Caucasus. Cretaceous rocks have few Jurassic-Cretaceous zircons, indicating a period of relative magmatic quiescence and minimal exhumation within this basin. Late Cenozoic closure of the Caucasus Basin juxtaposed the Lesser Caucasus arc to the south against the crystalline core of the Greater Caucasus to the north and led to the formation of a hypothesized terminal suture. We expect this suture to be within ~20 km of the southern range front of the Greater Caucasus because all analysed rocks to the north exhibit a provenance affinity with the crystalline core of the Greater Caucasus.
Insights from conservation genomics have dramatically improved recovery plans for numerous endangered species. However, most taxa have yet to benefit from the full application of genomic technologies. The mountain yellow-legged frog species complex, Rana muscosa and Rana sierrae, inhabits the Sierra Nevada mountains and Transverse/Peninsular Ranges of California and Nevada. Both species have declined precipitously throughout their historical distributions. Conservation management plans outline extensive ongoing recovery efforts but are still based on the genetic structure determined primarily using a single mitochondrial sequence. Our study used two different sequencing strategies – amplicon sequencing and exome capture – to refine our understanding of the population genetics of these imperiled amphibians. We used buccal swabs, museum tissue samples, and archived skin swabs to genotype frog populations across their range. Using the amplicon sequencing and exome capture datasets separately and combined, we document five major genetic clusters. Notably, we found evidence supporting previous species boundaries within Kings Canyon National Park with some exceptions at individual sites. Though we see evidence of genetic clustering, especially in the R. muscosa clade, we also found evidence of some admixture across cluster boundaries in the R. sierrae clade, suggesting a stepping-stone model of population structure. We also find that the southern R. muscosa cluster had large runs of homozygosity and the lowest overall heterozygosity of any of the clusters, consistent with previous reports of marked declines in this area. Overall, our results clarify management unit designations across the range of an endangered species and highlight the importance of sampling the entire range of a species, even when collecting genome-scale data.
The fundamental goal of a rare plant translocation is to create self-sustaining populations with the evolutionary resilience to persist in the long-term. Yet most plant translocation syntheses focus on a few factors influencing short-term benchmarks of success (e.g., survival and reproduction). Short-term benchmarks can be misleading when trying to infer future growth and viability because the factors that promote establishment may differ from those required for long-term persistence. We assembled a large (n = 275) and broadly representative dataset of well-documented and monitored (7.9 years on average) at-risk plant translocations to identify the most important site attributes, management techniques, and species traits for six life-cycle benchmarks and population metrics of translocation success. Using the random forest algorithm, we found that drivers of translocation outcomes varied across timeframes and metrics of success. Management techniques had the greatest relative influence on the attainment of life-cycle benchmarks and short-term population trends while site attributes and species traits were more important for population persistence and longer-term trends. Specifically, large founder sizes increased the potential for reproduction and recruitment into the next generation, while declining habitat quality and the outplanting of species with low seed production led to increased extinction risks and a reduction in potential reproductive output in the long-term, respectively. We also detected novel interactions between some of the most important drivers, such as an increased probability of next-generation recruitment in species with greater seed production rates, but only when coupled with large founder sizes. Since most significant barriers to plant translocation success can be overcome by improving techniques or resolving site-level issues through early intervention and management, we suggest that by combining long-term monitoring with adaptive management, translocation programs can enhance the prospects of achieving long-term success.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.14190","usgsCitation":"Bellis, J., Osazuwa-Peters, O.L., Maschinski, J., Keir, M.J., Parsons, E.W., Kaye, T., Kunz, M., Possley, J., Menges, E., Smith, S.A., Roth, D., Brewer, D., Brumback, W.E., Lange, J.J., Niederer, C., Turner-Skoff, J.B., Bontrager, M., Braham, R., Coppoletta, M., Holl, K., Williamson, P., Bell, T.J., Jonas, J., McEachern, K., Robertson, K.L., Birnbaum, S.J., Dattilo, A., Dollard, J.J., Fant, J., Kishida, W., Lesica, P., Link, S.O., Pavlovic, N., Poole, J., Reemts, C.M., Stiling, P., Taylor, D.D., Titus, J.H., Titus, P.J., Adkins, E.D., Chambers, T., Paschke, M.W., Heinman, K.D., and Albrecht, M.A., 2024, Identifying predictors of translocation success in rare plant species: Conservation Biology, v. 38, no. 2, e14190, 14 p., https://doi.org/10.1111/cobi.14190.","productDescription":"e14190, 14 p.","ipdsId":"IP-153276","costCenters":[{"id":651,"text":"Western Ecological 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Kearney","active":true,"usgs":false}],"preferred":false,"id":885839,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"McEachern, Kathryn 0000-0003-2631-8247 kathryn_mceachern@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-8247","contributorId":146324,"corporation":false,"usgs":true,"family":"McEachern","given":"Kathryn","email":"kathryn_mceachern@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":885840,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Robertson, Kathy L.","contributorId":330772,"corporation":false,"usgs":false,"family":"Robertson","given":"Kathy","email":"","middleInitial":"L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":885841,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Birnbaum, Sandra 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paddlefish and evaluate depth use in a reservoir","interactions":[],"lastModifiedDate":"2024-08-07T16:15:44.408223","indexId":"70256476","displayToPublicDate":"2023-09-26T11:15:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Using down-scan capabilities from recreational-grade side-scan sonar systems to sample paddlefish and evaluate depth use in a reservoir","docAbstract":"Recreational-grade side-scan sonar (SSS) has only recently been applied to estimate abundance of Paddlefish Polyodon spathula, a large pelagic planktivore, in reservoirs. Current recreational-grade SSS units also have a dedicated down-scan channel, which may be useful for detecting Paddlefish in reservoirs because the range of depths they inhabit. We investigated the utility of down-scan images using SSS data from a previously published study of Paddlefish in Keystone Lake, Oklahoma. Two readers counted Paddlefish and estimated the depth of each fish and the water column. We used proximity functions in a geographic information system to find individual Paddlefish that were common between the readers. We further used proximity functions to identify common fish observed from the SSS survey conducted previously as an aid to compare and refine down-scan estimates. Depth of Paddlefish averaged approximately 7 m, but fish were deeper when water was deeper. Density estimates from down-scan were comparable to side-scan, but only after utilizing the side-scan data to adjust for detection-by-distance in a dual-gear approach. Down-scan data thus appear to be useful for not only estimating density of Paddlefish, but also for incorporating depth use, creating a three-dimensional database of locations that can inform managers of optimal depths for sampling gear (e.g., gill nets), improve monitoring efficiency, and facilitate better management of reservoir populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2023.106872","usgsCitation":"Long, J.M., Joyce, P., Bruckerhoff, L., Lonsinger, R.C., and Wolfenkoehler, W., 2024, Using down-scan capabilities from recreational-grade side-scan sonar systems to sample paddlefish and evaluate depth use in a reservoir, v. 269, 106872, 8 p., https://doi.org/10.1016/j.fishres.2023.106872.","productDescription":"106872, 8 p.","ipdsId":"IP-155831","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Keystone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.46168930943298,\n 36.327912527967385\n ],\n [\n -96.46168930943298,\n 36.10858643895318\n ],\n [\n -96.21251987229255,\n 36.10858643895318\n ],\n [\n -96.21251987229255,\n 36.327912527967385\n ],\n [\n -96.46168930943298,\n 36.327912527967385\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"269","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Joyce, P.","contributorId":340781,"corporation":false,"usgs":false,"family":"Joyce","given":"P.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bruckerhoff, L.","contributorId":340782,"corporation":false,"usgs":false,"family":"Bruckerhoff","given":"L.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":907550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolfenkoehler, W.","contributorId":340783,"corporation":false,"usgs":false,"family":"Wolfenkoehler","given":"W.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907552,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250088,"text":"70250088 - 2024 - Using multiple metal mixture models to predict toxicity of riverine sediment porewater to the benthic life stage of juvenile white sturgeon (Acipenser transmontanus)","interactions":[],"lastModifiedDate":"2024-01-04T14:52:22.03027","indexId":"70250088","displayToPublicDate":"2023-09-26T06:35:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17090,"text":"Environmental Toxicology & Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Using multiple metal mixture models to predict toxicity of riverine sediment porewater to the benthic life stage of juvenile white sturgeon (Acipenser transmontanus)","docAbstract":"Five metal mixture dose–response models were used to predict the toxicity of porewater to young sturgeon at areas of interest in the Upper Columbia River (WA, USA/BC, Canada) and to evaluate these models as tools for risk assessments. Dose components of metal mixture models included exposure to free metal ion activities or metal accumulation by biotic ligands or humic acid, and links of dose to response used logistic equations, independent joint action equations, or additive toxicity functions. Laboratory bioassay studies of single metal exposures to juvenile sturgeon, porewater collected in situ in the fast-flowing Upper Columbia River, and metal mixture models were used to evaluate toxicity. The five metal mixture models were very similar in their predictions of adverse response of juvenile sturgeon and in identifying copper (Cu) as the metal responsible for the most toxic conditions. Although the modes of toxic action and the 20% effective concentration values were different among the dose models, predictions of adverse response were consistent among models because all doses were tied to the same biological responses. All models indicated that 56% ± 5% of 122 porewater samples were predicted to have <20% adverse response, 25% ± 5% of samples were predicted to have 20% to 80% adverse response, and 20% ± 4% were predicted to have >80% adverse response in juvenile sturgeon. The approach of combining bioassay toxicity data, compositions of field porewater, and metal mixture models to predict lack of growth and survival of aquatic organisms due to metal toxicity is an important tool that can be integrated with other information (e.g., survey studies of organism populations, life cycle and behavior characteristics, sediment geochemistry, and food sources) to assess risks to aquatic organisms in metal-enriched ecosystems. Environ Toxicol Chem 2023;00:1–12. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.
Extreme winter temperatures govern the northern range limit of black mangroves (Avicennia germinans) in southeastern North America. There is a pressing need for studies that advance our understanding of how extreme cold temperature events affect mangroves near their range limits. However, such events are infrequent and challenging to study at regional scales. Here, we compared the damage to mangroves from extreme freeze events in 2018 and 2021, using local data from sites in USA (Florida, Louisiana, and Texas) and northeastern Mexico (Tamaulipas). In 2018, mangrove damage was concentrated in Louisiana and the upper Texas coast, where minimum temperatures ranged from -4 °C to -7 °C. In 2021, damage from a more severe freeze event was concentrated along the central to northern coasts of Texas, where minimum temperatures ranged from -4 °C to -10 °C. We used regional temperature and vegetation data from these events to quantify temperature thresholds for A. germinans leaf damage. Our results indicate that A. germinans leaf damage is likely to occur when temperatures are between -4 °C and -6 °C. These findings help refine temperature thresholds for A. germinans leaf damage and advance understanding of the effects of extreme freeze events on mangrove range expansion. This information is valuable for anticipating future range dynamics in a warming world.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-023-01279-7","usgsCitation":"Kaalstad, S., Osland, M., Devlin, D.J., Proffitt, C., Feher, L., Armitage, A.R., Day, R., Swanson, K., Anderson, G., Berger, B., Cebrian, J., Cummins, K.L., Dunton, K., Feller, I.C., Fierro-Cabo, A., Flores, E.A., From, A., Hughes, A.R., Kaplan, D.A., Langston, A.K., Martinez, M., Martinez, B., Miller, C., Reaver, N.G., Sanspree, C.R., Snyder, C.M., Stetter, A.P., Thompson, J., and Zamora-Tovar, C., 2024, Temperature thresholds for leaf damage from two extreme freeze events (2018 and 2021) near the northern range limit of black mangroves (Avicennia germinans) in southeastern North America: Estuaries and Coasts, v. 47, p. 292-300, https://doi.org/10.1007/s12237-023-01279-7.","productDescription":"9 p.","startPage":"292","endPage":"300","ipdsId":"IP-149138","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":421750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Ocean, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.7093082379138,\n 31.07061973414541\n ],\n [\n -97.7093082379138,\n 24.265788006919635\n ],\n [\n -97.09407386291379,\n 24.14554317176828\n ],\n [\n -79.25227698791403,\n 23.98504066162903\n ],\n [\n -80.83430823791386,\n 30.844508558676267\n ],\n [\n -97.7093082379138,\n 31.07061973414541\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2023-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaalstad, Simen","contributorId":328569,"corporation":false,"usgs":false,"family":"Kaalstad","given":"Simen","email":"","affiliations":[{"id":34838,"text":"Texas A&M Corpus Christi","active":true,"usgs":false}],"preferred":false,"id":885719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":222814,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":885720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devlin, Donna J.","contributorId":305364,"corporation":false,"usgs":false,"family":"Devlin","given":"Donna","email":"","middleInitial":"J.","affiliations":[{"id":34838,"text":"Texas A&M Corpus Christi","active":true,"usgs":false}],"preferred":false,"id":885721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Proffitt, C. 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Randall","contributorId":177827,"corporation":false,"usgs":false,"family":"Hughes","given":"A.","email":"","middleInitial":"Randall","affiliations":[],"preferred":false,"id":885736,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kaplan, David A.","contributorId":218915,"corporation":false,"usgs":false,"family":"Kaplan","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":39937,"text":"University of Florida, Gainesville, FL USA","active":true,"usgs":false}],"preferred":false,"id":885737,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Langston, Amy K.","contributorId":218916,"corporation":false,"usgs":false,"family":"Langston","given":"Amy","email":"","middleInitial":"K.","affiliations":[{"id":39937,"text":"University of Florida, Gainesville, FL USA","active":true,"usgs":false}],"preferred":false,"id":885738,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Martinez, Melinda 0000-0001-6652-9220","orcid":"https://orcid.org/0000-0001-6652-9220","contributorId":290467,"corporation":false,"usgs":true,"family":"Martinez","given":"Melinda","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":885739,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Martinez, Briana","contributorId":330727,"corporation":false,"usgs":false,"family":"Martinez","given":"Briana","email":"","affiliations":[{"id":78995,"text":"University of Texas Marine Science Institute, Port Aransas, TX, USA","active":true,"usgs":false}],"preferred":false,"id":885740,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Miller, Christopher J.","contributorId":287682,"corporation":false,"usgs":false,"family":"Miller","given":"Christopher J.","affiliations":[{"id":61624,"text":"Saint Leo University, Saint Leo, FL USA","active":true,"usgs":false}],"preferred":false,"id":885741,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Reaver, Nathan G.F.","contributorId":328571,"corporation":false,"usgs":false,"family":"Reaver","given":"Nathan","email":"","middleInitial":"G.F.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":885742,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sanspree, Colt R.","contributorId":274816,"corporation":false,"usgs":false,"family":"Sanspree","given":"Colt","email":"","middleInitial":"R.","affiliations":[{"id":56661,"text":"U.S. Fish and Wildlife Service, Austwell, TX USA","active":true,"usgs":false}],"preferred":false,"id":885743,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Snyder, Caitlin M.","contributorId":218921,"corporation":false,"usgs":false,"family":"Snyder","given":"Caitlin","email":"","middleInitial":"M.","affiliations":[{"id":39940,"text":"Apalachicola National Estuarine Research Reserve, Eastpoint, FL USA","active":true,"usgs":false}],"preferred":false,"id":885744,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Stetter, Andrew P.","contributorId":274818,"corporation":false,"usgs":false,"family":"Stetter","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":56661,"text":"U.S. Fish and Wildlife Service, Austwell, TX USA","active":true,"usgs":false}],"preferred":false,"id":885745,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Thompson, Jamie E.","contributorId":328582,"corporation":false,"usgs":false,"family":"Thompson","given":"Jamie E.","affiliations":[{"id":78411,"text":"Texas A&M University at Galveston","active":true,"usgs":false}],"preferred":false,"id":885746,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Zamora-Tovar, Carlos","contributorId":328579,"corporation":false,"usgs":false,"family":"Zamora-Tovar","given":"Carlos","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":885747,"contributorType":{"id":1,"text":"Authors"},"rank":29}]}} ,{"id":70249372,"text":"70249372 - 2024 - Long-term trends of local bird populations based on monitoring schemes: Are they suitable for justifying management measures?","interactions":[],"lastModifiedDate":"2024-03-26T14:25:25.119235","indexId":"70249372","displayToPublicDate":"2023-09-25T06:47:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2409,"text":"Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends of local bird populations based on monitoring schemes: Are they suitable for justifying management measures?","docAbstract":"Local biodiversity monitoring is important to assess the effects of global change, but also to evaluate the performance of landscape and wildlife protection, since large-scale assessments may buffer local fluctuations, rare species tend to be underrepresented, and management actions are usually implemented on local scales. We estimated population trends of 58 bird species using open-population N-mixture models based on count data in two localities in southeastern Spain, which have been collected according to a citizen science monitoring program (SACRE, Monitoring Common Breeding Birds in Spain) over 21 and 15 years, respectively. We performed different abundance models for each species and study area, accounting for imperfect detection of individuals in replicated counts. After selecting the best models for each species and study area, empirical Bayes methods were used for estimating abundances, which allowed us to calculate population growth rates (λ) and finally population trends. We also compared the two local population trends and related them with national and European trends, and species functional traits (phenological status, dietary, and habitat specialization characteristics). Our results showed increasing trends for most species, but a weak correlation between populations of the same species from both study areas. In general, local population trends were consistent with the trends observed at national and continental scales, although contrasting patterns exist for several species, mainly with increasing local trends and decreasing Spanish and European trends. Moreover, we found no evidence of a relationship between population trends and species traits. We conclude that using open-population N-mixture models is an appropriate method to estimate population trends, and that citizen science-based monitoring schemes can be a source of data for such analyses. This modeling approach can help managers to assess the effectiveness of their actions at the local level in the context of global change.
Survival estimates are critical components of avian ecology. In well-intentioned efforts to maximize the utility of one's research, survival estimates often derive from data that were not originally collected for survival assessments, and such post hoc analyses may include unintentional biases. We estimated the survival of Whimbrels captured and marked at two breeding sites in Alaska using divergent data streams that in isolation were subject to methodological biases. Although both capture sites were chosen to study the migration ecology of Alaska-breeding Whimbrels, maximizing the conservation value of the data we collected was obviously desirable. We used multi-year telemetry information to infer survival from one site (Colville River) and mark-resight techniques to estimate survival from a second site (Kanuti River). At the Colville River, we could not feasibly include a control group of birds to assess potential survival effects of externally mounted transmitters, while at Kanuti River we were unable to accurately account for potential emigration events because we used resightings alone. We integrated these datasets in a Bayesian hierarchical framework, an approach that permitted insights across sites that moderated methodological biases within sites. Using telemetry enabled us to detect permanent emigration events from breeding sites in two of ten birds; results that informed estimates for birds without tracking devices. These datasets yielded point estimates of true survival of Whimbrels from Colville River equipped with solar-powered satellite transmitters that were higher (0.83) than true survival estimates of Whimbrels from Kanuti River marked with leg flags alone (0.74) or equipped with surgically implanted satellite transmitters (0.50), but the 95% credible intervals on these estimates overlapped across groups. For species like Whimbrels that are difficult and costly to study, combining information from disparate data streams allowed us to derive novel demographic estimates, an approach with clear application to other similar studies.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.13273","usgsCitation":"Ruthrauff, D.R., Harwood, C.M., Tibbitts, T.L., and Patil, V.P., 2024, Disparate data streams together yield novel survival estimates of Alaska-breeding Whimbrels: Ibis, v. 166, no. 2, p. 622-632, https://doi.org/10.1111/ibi.13273.","productDescription":"11 p.","startPage":"622","endPage":"632","ipdsId":"IP-141546","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":498851,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1111/ibi.13273","text":"Publisher Index Page"},{"id":420836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Colville River, Kanuti River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.03574093661132,\n 68.75774490548673\n ],\n [\n -160.03574093661132,\n 65.24300636559764\n ],\n [\n -145.19869372665494,\n 65.24300636559764\n ],\n [\n -145.19869372665494,\n 68.75774490548673\n ],\n [\n -160.03574093661132,\n 68.75774490548673\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"166","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruthrauff, Daniel R. 0000-0003-1355-9156 druthrauff@usgs.gov","orcid":"https://orcid.org/0000-0003-1355-9156","contributorId":4181,"corporation":false,"usgs":true,"family":"Ruthrauff","given":"Daniel","email":"druthrauff@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":883058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harwood, Christopher M.","contributorId":260398,"corporation":false,"usgs":false,"family":"Harwood","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":52582,"text":"US Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":883059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":102185,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":883060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":883061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251046,"text":"70251046 - 2024 - Using geospatial analysis to guide marsh restoration in Chesapeake Bay and beyond","interactions":[],"lastModifiedDate":"2024-01-19T13:20:31.871283","indexId":"70251046","displayToPublicDate":"2023-09-13T07:19:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Using geospatial analysis to guide marsh restoration in Chesapeake Bay and beyond","docAbstract":"Coastal managers are facing imminent decisions regarding the fate of coastal wetlands, given ongoing threats to their persistence. There is a need for objective methods to identify which wetland parcels are candidates for restoration, monitoring, protection, or acquisition due to limited resources and restoration techniques. Here, we describe a new spatially comprehensive data set for Chesapeake Bay salt marshes, which includes the unvegetated-vegetated marsh ratio, elevation metrics, and sediment-based lifespan. Spatial aggregation across regions of the Bay shows a trend of increasing deterioration with proximity to the seaward boundary, coherent with conceptual models of coastal landscape response to sea-level rise. On a smaller scale, the signature of deterioration is highly variable within subsections of the Bay: fringing, peninsular, and tidal river marsh complexes each exhibit different spatial patterns with regards to proximity to the seaward edge. We then demonstrate objective methods to use these data for mapping potential management options on to the landscape, and then provide methods to estimate lifespan and potential changes in lifespan in response to restoration actions as well as future sea level rise. We account for actions that aim to increase sediment inventories, revegetate barren areas, restore hydrology, and facilitate salt marsh migration into upland areas. The distillation of robust geospatial data into simple decision-making metrics, as well as the use of those metrics to map decisions on the landscape, represents an important step towards science-based coastal management.
Umbrella species and other surrogate species approaches to conservation provide an appealing framework to extend the reach of conservation efforts beyond single species. For the umbrella species concept to be effective, populations of multiple species of concern must persist in areas protected on behalf of the umbrella species. Most assessments of the concept, however, focus exclusively on geographic overlap among umbrella and background species, and not measures that affect population persistence (e.g. habitat quality or fitness). We quantified the congruence between the habitat preferences and nesting success of a high-profile umbrella species (greater sage-grouse, Centrocercus urophasianus, hereafter ‘sage-grouse’), and three sympatric species of declining songbirds (Brewer's sparrow Spizella breweri, sage thrasher Oreoscoptes montanus and vesper sparrow Pooecetes gramineus) in central Wyoming, USA during 2012–2013. We used machine-learning methods to create data-driven predictions of sage-grouse nest-site selection and nest survival probabilities by modeling field-collected sage-grouse data relative to habitat attributes. We then used field-collected songbird data to assess whether high-quality sites for songbirds aligned with those of sage-grouse. Nest sites selected by songbirds did not coincide with sage-grouse nesting preferences, with the exception that Brewer's sparrows preferred similar nest sites to sage-grouse in 2012. Moreover, the areas that produced higher rates of songbird nest survival were unrelated to those for sage-grouse. Our findings suggest that management actions at local scales that prioritize sage-grouse nesting habitat will not necessarily enhance the reproductive success of sagebrush-associated songbirds. Measures implemented to conserve sage-grouse and other purported umbrella species at broad spatial scales likely overlap the distribution of many species, however, broad-scale overlap may not translate to fine-scale conservation benefit beyond the umbrella species itself. The maintenance of microhabitat heterogeneity important for a diversity of species of concern will be critical for a more holistic application of the umbrella species concept.
In the face of accelerating climate change and rising sea levels, quantifying surface elevation change dynamics in coastal wetlands can help to develop a more complete understanding of the implications of sea-level rise on coastal wetland stability. The surface elevation table-marker horizon (SET-MH) approach has been widely used to quantify and characterize surface elevation change dynamics in coastal marshes and mangrove forests. Whereas past studies that utilized the SET-MH approach have most often quantified rates of surface elevation change using simple linear regression analyses, several recent studies have shown that elevation patterns can include a diverse combination of linear and non-linear patterns. Generalized additive models (GAMs) are an extension of generalized linear models (GLMs) that have previously been used to analyze a variety of complex ecological processes such as cyclical changes in water quality, species distributions, long-term patterns in wetland area change, and palaeoecological time series. Here, we use long-term SET data to demonstrate the value of generalized additive models for analyzing non-linear patterns of surface elevation change in coastal wetlands. Additionally, we illustrate how the GAM approach can be used to effectively quantify rates of elevation change at both landscape- and local site-level scales.