Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these challenges, we develop a method for assessing shallow landslide susceptibility based on a probabilistic morphometric analysis of the landscape’s topography, rather than the characteristics of landslides. The model assumes that hillslopes with higher relief and gradient compared to the surrounding landscape are more prone to landslides. We demonstrate the superior performance of this approach over contrasting data-driven models across the northwestern United States. As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adt1541","usgsCitation":"Woodard, J.B., and Mirus, B., 2025, Overcoming the data limitations in landslide susceptibility modelling: Science Advances, v. 11, no. 8, eadt1541, 13 p., https://doi.org/10.1126/sciadv.adt1541.","productDescription":"eadt1541, 13 p.","ipdsId":"IP-170424","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":489957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adt1541","text":"Publisher Index Page"},{"id":482443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.432878344216,\n 48.94761592494433\n ],\n [\n -124.54708923390476,\n 48.94761592494433\n ],\n [\n -124.54708923390476,\n 41.82426100626475\n ],\n [\n -117.432878344216,\n 41.82426100626475\n ],\n [\n -117.432878344216,\n 48.94761592494433\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":928330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":928331,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263777,"text":"70263777 - 2025 - Cancer risk and estimated lithium exposure in drinking groundwater in the US","interactions":[],"lastModifiedDate":"2025-02-24T15:22:49.778702","indexId":"70263777","displayToPublicDate":"2025-02-20T09:17:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20081,"text":"JAMA Network Open","active":true,"publicationSubtype":{"id":10}},"title":"Cancer risk and estimated lithium exposure in drinking groundwater in the US","docAbstract":"Importance Lithium is a naturally occurring element in drinking water and is commonly used as a mood-stabilizing medication. Although clinical studies have reported associations between receiving lithium treatment and reduced cancer risk among patients with bipolar disorder, to our knowledge, the association between environmental lithium exposure and cancer risk has never been studied in the general population.
Objectives To evaluate the association between exposure to lithium in drinking groundwater and cancer risk in the general population.
Design, Setting, and Participants This cohort study included participants with electronic health record and residential address information but without cancer history at baseline from the All of Us Research Program between May 31, 2017, and June 30, 2022. Participants were followed up until February 15, 2023. Statistical analysis was performed from September 2023 through October 2024.
Exposure Lithium concentration in groundwater, based on kriging interpolation of publicly available US Geological Survey data on lithium concentration for 4700 wells across the contiguous US between May 12, 1999, and November 6, 2018.
Main Outcome and Measures The main outcome was cancer diagnosis or condition, obtained from electronic health records. Stratified Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% CIs for risk of cancer overall and individual cancer types for increasing quintiles of the estimated lithium exposure in drinking groundwater, adjusting for socioeconomic, behavioral, and neighborhood-level variables. The analysis was further conducted in the western and eastern halves of the US and restricted to long-term residents living at their current address for at least 3 years.
Results A total of 252 178 participants were included (median age, 52 years [IQR, 36-64 years]; 60.1% female). The median follow-up time was 3.6 years (IQR, 3.0-4.3 years), and 7573 incident cancer cases were identified. Higher estimated lithium exposure was consistently associated with reduced cancer risk. Compared with the first (lowest) quintile of lithium exposure, the HR for all cancers was 0.49 (95% CI, 0.31-0.78) for the fourth quintile and 0.29 (95% CI, 0.15-0.55) for the fifth quintile. These associations were found for all cancer types investigated in both females and males, among long-term residents, and in both western and eastern states. For example, for the fifth vs first quintile of lithium exposure for all cancers, the HR was 0.17 (95% CI, 0.07-0.42) in females and 0.13 (95% CI, 0.04-0.38) in males; for long-term residents, the HR was 0.32 (95% CI, 0.15-0.66) in females and 0.24 (95% CI, 0.11-0.52) in males; and the HR was 0.01 (95% CI, 0.00-0.09) in western states and 0.34 (95% CI, 0.21-0.57) in eastern states.
Conclusions and Relevance In this cohort study of 252 178 participants, estimated lithium exposure in drinking groundwater was associated with reduced cancer risk. Given the sparse evidence and unknown mechanisms of this association, follow-up investigation is warranted.
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reverses decline in imperiled sage-grouse populations","docAbstract":"Conservation translocation (hereafter translocation), the intentional movement of organisms from one location to another as a management tool, can be an extremely useful conservation action to increase the abundance of isolated populations following successful habitat restoration. However, managers seek to weigh the benefits against costs to the source population from which individuals are removed. Using two small and imperiled greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) populations, we demonstrated the usefulness of translocation as a conservation management tool and the value of evaluating the potential consequences of translocation action. Using integrated population models and a before-after-control-impact (BACI) design, we quantified the extent to which translocation influenced the finite rate of change (
Understanding the relationship between biodiversity and ecological stability is increasingly urgent as rapid species extinction continues. Though evidence of positive diversity–stability relationships is accumulating, empirical results are inconsistent, and effect sizes tend to be small, raising questions about relative contributions of intrinsic (i.e., species composition/interactions) and extrinsic (i.e., environmental) drivers of stability. Community stability may be more strongly influenced by environmental conditions than by community diversity in some contexts, yet little is known about the comparative importance of diversity and climate means, extremes, and variability in regulating stability. We used a half-century of continental-scale bird data to quantify avian community temporal variability (a metric often used to approximate ecological stability) at 1379 sites and compared relative effects of climatic variables and species richness. We found that extreme heat and extremely low precipitation at decadal scales are associated with high bird community variability and these climate variables outperformed species richness in terms of variance explained and magnitude of effect. This provides empirical support for the theoretical concept that, at a continental, decadal scale, environmental conditions can play a larger role than intrinsic factors in determining community stability. Our findings also increase understanding of how climate extremes cause diverse ecological responses.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.70005","usgsCitation":"Cady, S., Fuhlendorf, S., Davis, C., Luttbeg, B., Roberts, C.P., and Loss, S., 2025, The relative influence of climate extremes and species richness on the temporal variability of bird communities: Ecology, v. 106, no. 2, e70005, 8 p., https://doi.org/10.1002/ecy.70005.","productDescription":"e70005, 8 p.","ipdsId":"IP-146673","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":498238,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.70005","text":"Publisher Index Page"},{"id":492134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cady, Samantha M.","contributorId":357859,"corporation":false,"usgs":false,"family":"Cady","given":"Samantha M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":942711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuhlendorf, Samuel D.","contributorId":357861,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":942712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Craig A.","contributorId":357863,"corporation":false,"usgs":false,"family":"Davis","given":"Craig A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":942713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luttbeg, Barney","contributorId":357865,"corporation":false,"usgs":false,"family":"Luttbeg","given":"Barney","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":942714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":942715,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, Scott","contributorId":357867,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":942716,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267891,"text":"70267891 - 2025 - Spatially explicit capture-recapture using fecal DNA to estimate elk population abundance and growth in western North Carolina, USA","interactions":[],"lastModifiedDate":"2025-06-06T15:09:09.499879","indexId":"70267891","displayToPublicDate":"2025-02-19T10:05:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit capture-recapture using fecal DNA to estimate elk population abundance and growth in western North Carolina, USA","docAbstract":"In an effort to restore extirpated elk to their historical range, 52 elk were reintroduced to Great Smoky Mountains National Park (GRSM) in North Carolina, USA, during 2001 and 2002. Since their reintroduction, elk numbers have increased, and elk have extended their range beyond GRSM boundaries. We used spatially explicit capture-recapture (SCR) methods based on fecal DNA to identify individual elk and estimate population abundance (N), apparent survival (φ), per capita recruitment (f), and population growth rate (λ) in western North Carolina. We walked a series of transects during 3 winter field seasons (2020–2022) and collected elk pellets encountered along those transects. We created spatially explicit capture histories and incorporated those data into both closed and open population SCR models. The top performing closed SCR models for males and females estimated density by year and as a function of the scaled distance to the nearest field, with densities decreasing as the distance increased. Combined male and female N were 179 elk (95% CI = 149–215) in 2020, 220 elk (95% CI = 188–256) in 2021, and 240 elk (95% CI = 207–279) in 2022. The top open population model estimated both φ and λ as functions of sex and year. The estimate of φ for males was 0.682 (95% CI = 0.317–0.908) during 2020–2021 and 0.339 (95% CI = 0.152–0.596) during 2021–2022 and for females was 0.953 (95% CI = 0.830–1.000) during 2020–2021 and 0.829 (95% CI = 0.601–1.000) during 2021–2022. The annual population growth rate (λ) for males was 1.127 (95% CI = 0.806–1.575) during 2020–2021 and 0.811 (95% CI = 0.566–1.163) during 2021–2022 and for females was 1.559 (95% CI = 1.162–2.091) during 2020–2021 and 1.122 (95% CI = 0.876–1.437) during 2021–2022. Our elk abundance estimates in areas >300 m from fields were negligible, and we suggest that sampling only the areas in and adjacent to fields in the future will result in reliable but more cost-efficient population estimates. Confidence intervals for vital rate parameters were wide for our 3-year dataset, but continued annual pellet sampling will increase sample sizes for vital rate estimation and thus improve precision. If elk herd expansion on public lands is desired, we suggest habitat modification to establish open grasslands adjacent to forests.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22733","usgsCitation":"Braunstein, J.L., Clark, J.D., Augustine, B., Hickman, C., McVey, J., and Yarkovich, J.G., 2025, Spatially explicit capture-recapture using fecal DNA to estimate elk population abundance and growth in western North Carolina, USA: Journal of Wildlife Management, v. 89, no. 4, e22733, 18 p., https://doi.org/10.1002/jwmg.22733.","productDescription":"e22733, 18 p.","ipdsId":"IP-166401","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":497997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22733","text":"Publisher Index Page"},{"id":490200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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Carolina\",\"nation\":\"USA \"}}]}","volume":"89","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"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":939280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":939281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Augustine, Benjamin C.","contributorId":356383,"corporation":false,"usgs":false,"family":"Augustine","given":"Benjamin C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":939282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hickman, Caleb R.","contributorId":356386,"corporation":false,"usgs":false,"family":"Hickman","given":"Caleb R.","affiliations":[{"id":84985,"text":"Eastern Band of Cherokee Indians","active":true,"usgs":false}],"preferred":false,"id":939283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McVey, Justin","contributorId":356387,"corporation":false,"usgs":false,"family":"McVey","given":"Justin","affiliations":[{"id":84988,"text":"North Carolina Wildlife Resources 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nine-banded armadillo (Dasypus novemcinctus) in the United States","title":"Current distribution of the nine-banded armadillo (Dasypus novemcinctus) in the United States","docAbstract":"The nine-banded armadillo (Dasypus novemcinctus: hereafter armadillo) was first recorded in the United States (U.S.) in the state of Texas in 1849 and has been expanding its range northward and eastward since then. With the widespread adoption of participatory science as well as the proliferation of nationwide wildlife game camera studies, occurrence data of armadillos can be compiled more rapidly and thoroughly than at any time in the past. Here, we use disparate data sources to update the current geographic distribution of the armadillo in the United States and use occurrence data from the leading edge of its range expansion to create a species distribution model to understand their relationship with landscape and bioclimatic factors. Since the last report on the geographic distribution of the armadillo in 2014, we show that armadillos have expanded to cover the entirety of Missouri and established in southern Iowa, expanded modestly within Kansas and Illinois, expanded northward and eastward in Indiana, expanded eastward in both Kentucky and Tennessee, established throughout the entirety of South Carolina and Georgia and established in the western third of North Carolina. Our species distribution model indicates that there is substantial opportunity for the species to continue to expand its geographic range, particularly in the Eastern United States. These results provide information to managers who are now or might soon be co-existing with the armadillo to proactively manage the species or inform the public regarding potential conflicts.
","language":"English","publisher":"MDPI","doi":"10.3390/d17020138","usgsCitation":"DeGregorio, B.A., and Deshwal, A., 2025, Current distribution of the nine-banded armadillo (Dasypus novemcinctus) in the United States: Diversity, v. 17, no. 2, 138, 14 p., https://doi.org/10.3390/d17020138.","productDescription":"138, 14 p.","ipdsId":"IP-174450","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d17020138","text":"Publisher Index Page"},{"id":493710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.46443710699266,\n 43.482544866418095\n ],\n [\n -98.46009009198762,\n 41.85543820530121\n ],\n [\n -103.40240986276783,\n 39.849442113752225\n ],\n [\n -106.57489778026832,\n 32.18560045351303\n ],\n [\n -103.13881240687051,\n 29.004932608997777\n ],\n [\n -102.01381568185428,\n 29.684127826701868\n ],\n [\n -100.5588846777174,\n 28.719938827643247\n ],\n [\n -98.90079539428103,\n 26.21067054924025\n ],\n [\n -97.28797850165715,\n 25.80528820642506\n ],\n [\n -95.08083877123178,\n 28.68104484993762\n ],\n [\n -90.13838335666708,\n 29.279435161004997\n ],\n [\n -83.97820912015521,\n 29.550506198188735\n ],\n [\n -81.62615042652806,\n 25.175140663968392\n ],\n [\n -79.20159572289897,\n 24.880530509938623\n ],\n [\n -81.26833756114897,\n 30.9638453530741\n ],\n [\n -75.67958860575126,\n 35.58409243159953\n ],\n [\n -75.80680620450346,\n 38.817069169317364\n ],\n [\n -81.47311820007566,\n 41.86626002921978\n ],\n [\n -87.65021677565505,\n 42.44570766588616\n ],\n [\n -90.16529470749337,\n 42.5611294229177\n ],\n [\n -91.36833985870564,\n 43.39180921040943\n ],\n [\n -96.46443710699266,\n 43.482544866418095\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":944986,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deshwal, Anant","contributorId":350109,"corporation":false,"usgs":false,"family":"Deshwal","given":"Anant","affiliations":[{"id":17862,"text":"Bradley University","active":true,"usgs":false}],"preferred":false,"id":944987,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264161,"text":"70264161 - 2025 - Not just corticosterone: Further characterization of the endocrine response of Kemp’s ridley sea turtles (Lepidochelys kempii) reveals elevated plasma aldosterone concentrations during field capture events","interactions":[],"lastModifiedDate":"2025-03-07T15:13:34.120709","indexId":"70264161","displayToPublicDate":"2025-02-19T08:06:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5762,"text":"Animals","active":true,"publicationSubtype":{"id":10}},"title":"Not just corticosterone: Further characterization of the endocrine response of Kemp’s ridley sea turtles (Lepidochelys kempii) reveals elevated plasma aldosterone concentrations during field capture events","docAbstract":"To develop safe and effective management policies, it is important to understand the physiologic effects of fishing interactions and scientific research methods on endangered marine species. In the present study, validated assays for plasma corticosterone, free thyroxine (fT4), and aldosterone were used to assess the endocrine status of 61 presumed healthy, wild Kemp's ridley sea turtles (Lepidochelys kempii) that were captured for separate ecological studies using two capture methods (trawl net n = 40; manual capture n = 21). Plasma hormone concentrations were also assessed in relation to eight clinical plasma biochemical analytes. Corticosterone and aldosterone concentrations were moderately high after capture, with significantly higher concentrations in turtles captured by trawl net vs. manual capture. Free thyroxine concentrations were within previously published ranges for healthy individuals of this species. Clinical biochemical data revealed moderately elevated potassium and lactate concentrations in many individuals, with significantly greater lactate concentrations in trawl-captured turtles. Aldosterone concentrations were positively correlated with corticosterone. The results of the present study indicate that Kemp's ridley sea turtles have robust adrenocortical activity immediately after capture, resulting in high plasma concentrations of corticosterone and aldosterone. Researchers who use such methods to access sea turtles can consider these results in planning careful and efficient field studies.
","language":"English","publisher":"MDPI","doi":"10.3390/ani15040600","usgsCitation":"Innis, C.J., Graham, K., Mott, C.R., Hart, K., Roche, D., Cherkiss, M., and Burgess, E., 2025, Not just corticosterone: Further characterization of the endocrine response of Kemp’s ridley sea turtles (Lepidochelys kempii) reveals elevated plasma aldosterone concentrations during field capture events: Animals, v. 15, no. 4, 600, 12 p., https://doi.org/10.3390/ani15040600.","productDescription":"600, 12 p.","ipdsId":"IP-172044","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":487745,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ani15040600","text":"Publisher Index Page"},{"id":483050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.70557649872961,\n 30.010739238547117\n ],\n [\n -92.00116115306747,\n 28.339002561929632\n ],\n [\n -82.86230511121795,\n 28.277917524081474\n ],\n [\n -82.85674465474287,\n 29.15185303305006\n ],\n [\n -84.02866560881175,\n 30.139029913622593\n ],\n [\n -85.6277919394617,\n 29.59516287623463\n ],\n [\n -86.1440236623925,\n 30.300606706908166\n ],\n [\n -88.25369972273643,\n 30.00606334692438\n ],\n [\n -88.6098267088585,\n 30.51137384092631\n ],\n [\n -93.70557649872961,\n 30.010739238547117\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Innis, Charles J.","contributorId":117805,"corporation":false,"usgs":false,"family":"Innis","given":"Charles","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":929969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Katherine M.","contributorId":352061,"corporation":false,"usgs":false,"family":"Graham","given":"Katherine M.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mott, Cody R.","contributorId":331398,"corporation":false,"usgs":false,"family":"Mott","given":"Cody","email":"","middleInitial":"R.","affiliations":[{"id":79198,"text":"Inwater Research Group, Jensen Beach, FL","active":true,"usgs":false}],"preferred":false,"id":929971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":929972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roche, David 0000-0002-3329-2746 droche@usgs.gov","orcid":"https://orcid.org/0000-0002-3329-2746","contributorId":204332,"corporation":false,"usgs":true,"family":"Roche","given":"David","email":"droche@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":true,"id":929973,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":222180,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":929974,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burgess, Elizabeth A.","contributorId":352063,"corporation":false,"usgs":false,"family":"Burgess","given":"Elizabeth A.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929975,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70265975,"text":"70265975 - 2025 - Integrated patterns of residence and movement create testable hypotheses about fish feeding migrations","interactions":[],"lastModifiedDate":"2025-04-22T17:51:00.5316","indexId":"70265975","displayToPublicDate":"2025-02-18T12:50:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Integrated patterns of residence and movement create testable hypotheses about fish feeding migrations","docAbstract":"Developing and testing alternate hypotheses about patterns, mechanisms, and consequences of movement in geographically-large, heterogeneous, natural systems can advance the scientific understanding of animal migration and benefit the conservation of most mobile species. Within organismal movement trajectories, different combinations of residence and movement are predicted from existing ecological theories (e.g. long distance migration, site fidelity, central place foraging, ideal free distribution, habitat shifts). However, testing these conceptually-based, spatially-explicit hypotheses about animal movement and migration in the field can be logistically challenging. Here our purpose is to introduce Resmo, a framework of metrics and analyses that integrate site-specific RESidence and across-site MOvements. We illustrate the ecological insights from this framework using the empirical example of coastal Striped Bass (Morone saxatilis) during their seasonal feeding migration. Our use of site-specific Resmo applied to empirical telemetry data enhanced the understanding of feeding behavior of migratory fish, suggested testable ecologically-meaningful hypotheses about foraging, and identified criteria on which to base the selection of future sampling locations. In summary, the Resmo approach provides a useful new direction for thinking about animal migration, animal movement, biological conservation, and future priorities for empirical field data collection related to understanding the distribution of mobile organisms.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-79627-1","usgsCitation":"Mather, M.E., Taylor, R.B., Smith, J., and Boles, K., 2025, Integrated patterns of residence and movement create testable hypotheses about fish feeding migrations: Scientific Reports, v. 15, 5951, 17 p., https://doi.org/10.1038/s41598-024-79627-1.","productDescription":"5951, 17 p.","ipdsId":"IP-167462","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":488492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-79627-1","text":"Publisher Index Page"},{"id":484860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2025-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryland B.","contributorId":288583,"corporation":false,"usgs":false,"family":"Taylor","given":"Ryland","email":"","middleInitial":"B.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":934218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":287830,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":934219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boles, Kayla M.","contributorId":288585,"corporation":false,"usgs":false,"family":"Boles","given":"Kayla M.","affiliations":[{"id":13409,"text":"Kentucky Department of Fish & Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":934220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265933,"text":"70265933 - 2025 - Quantifying regional ecological dynamics using agency monitoring data, ecological site descriptions, and ecological site groups","interactions":[],"lastModifiedDate":"2025-04-22T16:09:28.804094","indexId":"70265933","displayToPublicDate":"2025-02-18T11:02:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying regional ecological dynamics using agency monitoring data, ecological site descriptions, and ecological site groups","docAbstract":"Information about what ecological conditions are likely, causes or drivers of degradation, and potential management actions to restore degraded lands may support land conservation and restoration decisions. State-and-transition models (STMs) describe persistent plant and ecological conditions that are possible (the “state”) within a given abiotic setting and drivers or actions that can cause shifts between states (the “transitions”). These primarily conceptual models are widely used to inform resource and conservation decisions. Data-driven STMs have been developed for some lands, but not at regional or national scales. Here, we demonstrate a new repeatable workflow for developing data-driven STMs in the United States (US). The approach leverages predictive maps of Ecological Site Groups (ESGs), extensive field-based Federal monitoring databases, information from Ecological Site Description (ESD) STMs, soil erosion models, remotely sensed productivity, and other available spatial information (fire, land protection, and drought) to provide context and descriptions of the data-driven states, including likely drivers of transitions. Results of this workflow applied to one dryland ESG in the Upper Colorado River Basin in the southwestern US suggest that an Invaded state (16% of 1352 plots) and some occurrences of a Grassland state (30% of plots) are in a degraded or at-risk condition with reduced ecosystem services. The most common drivers of state transitions in the associated ESDs (n = 26) are related to livestock grazing and fire. The Invaded state in the ESG has evidence of degraded habitat quality and accelerated run-off while the Grassland state occurrences show reduced richness, productivity, and elevated erosion risk by wind. Areas subject to wildfire and with lower protection status had greater probability of Invaded state occurrence, generally supporting drivers in ESDs. The workflow presented here can serve as a template for describing ecological dynamics at regional scales, and support prioritization of land for conservation and climate adaptation activities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2024.12.006","usgsCitation":"Duniway, M.C., Knight, A.C., Nauman, T., Bishop, T., McCord, S.E., Webb, N.P., Williams, C., and Humphries, J.T., 2025, Quantifying regional ecological dynamics using agency monitoring data, ecological site descriptions, and ecological site groups: Rangeland Ecology & Management, v. 99, p. 119-142-142, https://doi.org/10.1016/j.rama.2024.12.006.","productDescription":"24 p.","startPage":"119-142","endPage":"142","ipdsId":"IP-158948","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2024.12.006","text":"Publisher Index Page"},{"id":484842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","noUsgsAuthors":false,"publicationDate":"2025-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":934066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":934067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nauman, Travis W.","contributorId":310519,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis W.","affiliations":[{"id":67201,"text":"USDA-NRCS National Soil Survey Center, 2290 SW Resource Blvd., Moab, UT, 84532, USA","active":true,"usgs":false}],"preferred":false,"id":934068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bishop, Tara B.B.","contributorId":215034,"corporation":false,"usgs":false,"family":"Bishop","given":"Tara B.B.","affiliations":[{"id":39160,"text":"Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT USA","active":true,"usgs":false}],"preferred":false,"id":934069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":934070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Webb, Nicholas P.","contributorId":195924,"corporation":false,"usgs":false,"family":"Webb","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":934071,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, C. Jason","contributorId":292512,"corporation":false,"usgs":false,"family":"Williams","given":"C. Jason","affiliations":[{"id":62926,"text":"Agricultural Research Service, U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":934072,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Humphries, Joel T.","contributorId":270937,"corporation":false,"usgs":false,"family":"Humphries","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":56221,"text":"US Bureau of Land Management, Colorado State Office, Lakewood, CO 80215, USA","active":true,"usgs":false}],"preferred":false,"id":934073,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266086,"text":"70266086 - 2025 - Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes","interactions":[],"lastModifiedDate":"2025-05-12T15:47:36.606655","indexId":"70266086","displayToPublicDate":"2025-02-17T10:08:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes","docAbstract":"As part of the Great Lakes Restoration Initiative, chemical monitoring and surveillance efforts have detected approximately 330 chemicals in surface water of Great Lakes tributaries. There were 140 chemicals for which no empirical toxicity data were available. The aim of this study was to generate transcriptomic points of departure (tPODs) for 10 of these compounds and demonstrate how they could be applied in a screening-level prioritization. Organisms representing three trophic levels of the aquatic food web (Pimephales promelas, Daphnia magna, and Raphidocelis subcapitata) were exposed for 24 hr to a half-log dilution series of nominal exposure concentrations typically ranging from 66.7–0.021 µM of each chemical. In addition to observations of apical effects (e.g., survival and morphology), whole body transcriptomic responses (tPODs) to each chemical were evaluated with targeted analysis using TempO-seq for P. promelas and D. magna and nontargeted RNA-seq for R. subcapitata. The tPODs ranged from 0.18–10.8 µM for P. promelas and 0.32–29 µM for D. magna, with the most potent of the chemicals tested being fipronil carboxamide for both species. For R. subcapitata, the tPODs ranged from 0.04–1.77 µM, with gabapentin as the most potent chemical tested. Empirically derived tPODs from these data-poor chemicals were compared with concentrations detected in the Great Lakes basin. Environmental concentrations were less than the tPODs except for R. subcapitata and 3,4-dichlorophenyl isocyanate. Similarly, tPODs from previously tested data-rich chemicals were compared with environmental concentrations, in which case tPODs from several chemicals overlapped environmental concentrations. This work demonstrates the potential utility of emerging ecological high-throughput transcriptomics assays to support screening and prioritization of data-poor environmental contaminants.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf050","usgsCitation":"Jenna Cavallin, Kendra Bush, Corsi, S., DeCicco, L., Kevin Flynn, Alex Kasparek, Monique Hazimi, Erin Maloney, Peter Schuman, and Daniel Villeneuve, 2025, Application of transcriptomics concentration-response modeling for prioritization of contaminants detected in tributaries of the North American Great Lakes: Environmental Toxicology and Chemistry, v. 44, no. 5, p. 1310-1321, https://doi.org/10.1093/etojnl/vgaf050.","productDescription":"12 p.","startPage":"1310","endPage":"1321","ipdsId":"IP-167135","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":484984,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenna Cavallin","contributorId":353840,"corporation":false,"usgs":false,"family":"Jenna Cavallin","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendra Bush","contributorId":353841,"corporation":false,"usgs":false,"family":"Kendra Bush","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeCicco, Laura 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":215381,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kevin Flynn","contributorId":353846,"corporation":false,"usgs":false,"family":"Kevin Flynn","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alex Kasparek","contributorId":353847,"corporation":false,"usgs":false,"family":"Alex Kasparek","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":934554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monique Hazimi","contributorId":353850,"corporation":false,"usgs":false,"family":"Monique Hazimi","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erin Maloney","contributorId":353852,"corporation":false,"usgs":false,"family":"Erin Maloney","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":934556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peter Schuman","contributorId":353856,"corporation":false,"usgs":false,"family":"Peter Schuman","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":934557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Daniel Villeneuve","contributorId":353857,"corporation":false,"usgs":false,"family":"Daniel Villeneuve","affiliations":[],"preferred":false,"id":934558,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70264973,"text":"70264973 - 2025 - Reevaluating the depositional model of the Cenomanian–Turonian Bridge Creek Limestone Member near Pueblo, Colorado, U.S.A.: Roles of changing sedimentation rate on the formation of limestone–marl bedding couplets","interactions":[],"lastModifiedDate":"2025-03-27T15:05:02.687084","indexId":"70264973","displayToPublicDate":"2025-02-17T09:57:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Reevaluating the depositional model of the Cenomanian–Turonian Bridge Creek Limestone Member near Pueblo, Colorado, U.S.A.: Roles of changing sedimentation rate on the formation of limestone–marl bedding couplets","docAbstract":"Although interbedded limestone–marl couplets in many hemipelagic and pelagic deposits have been commonly attributed to orbital-driven climate cycles, the driving mechanisms of these couplets remain largely controversial. This situation arises from the fact that detailed sedimentologic and petrographic facies characteristics of these fine-grained deposits have rarely been examined closely. In this study we conduct an integrated sedimentologic and petrographic analysis to disentangle causes of the limestone–marl bedding couplets in the Cenomanian–Turonian Bridge Creek Limestone Member (BCL) of the Greenhorn Formation using cores and outcrop near Pueblo, Colorado. By integrating existing geochemical datasets, each of the three general lithologies in the BCL including limestone, marl, and calcareous mudstone can be divided into two facies, a more bioturbated vs. a more laminated facies, in addition to bentonite beds. The variability in sedimentary, bioturbation, and petrographic characteristics of different sedimentary facies types, as well as constraints from the existing orbital time scale, in the BCL indicate changes in sediment accumulation rate or the amount of time recorded by different facies—the limestone, marl, and calcareous facies are interpreted to reflect increasing sedimentation rate. The sedimentary and petrographic facies variations, including but not limited to lithological alternations, in the BCL are interpreted to result from the combined influence of various processes such as bottom currents, bioturbation, early diagenesis, and episodic volcanic input, with some of the above-mentioned processes likely modulated by short-term relative changes in sea level. Results of this study highlight the need for detailed sedimentologic and petrographic studies and consideration of short-term changes in sedimentation rate to fully resolve the causes of the apparent limestone–marl bedding couplets and reliably reconstruct short-term changes in depositional and environmental conditions from the BCL and other similar successions.
","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/jsr.2024.121","usgsCitation":"Zhiyang Li, and Flaum, J.A., 2025, Reevaluating the depositional model of the Cenomanian–Turonian Bridge Creek Limestone Member near Pueblo, Colorado, U.S.A.: Roles of changing sedimentation rate on the formation of limestone–marl bedding couplets: Journal of Sedimentary Research, v. 95, no. 1, p. 186-208, https://doi.org/10.2110/jsr.2024.121.","productDescription":"23 p.","startPage":"186","endPage":"208","ipdsId":"IP-164103","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":483944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Pueblo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.30769287342653,\n 38.43186764103851\n ],\n [\n -105.18611920603225,\n 38.43186764103851\n ],\n [\n -105.18611920603225,\n 38.04895114213508\n ],\n [\n -104.30769287342653,\n 38.04895114213508\n ],\n [\n -104.30769287342653,\n 38.43186764103851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhiyang Li","contributorId":352824,"corporation":false,"usgs":false,"family":"Zhiyang Li","affiliations":[{"id":84293,"text":"Texas A&M International University","active":true,"usgs":false}],"preferred":false,"id":932135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flaum, Jason A. 0000-0003-1251-1142","orcid":"https://orcid.org/0000-0003-1251-1142","contributorId":300809,"corporation":false,"usgs":true,"family":"Flaum","given":"Jason","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":932136,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70267191,"text":"70267191 - 2025 - Does habitat or climate change drive species range shifts?","interactions":[],"lastModifiedDate":"2025-06-12T15:47:11.255411","indexId":"70267191","displayToPublicDate":"2025-02-17T09:00:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Does habitat or climate change drive species range shifts?","docAbstract":"A primary prediction of climate change ecology is that species will track their climate niche poleward and upslope. However, studies have shown species responding in surprising ways. In this study, we aim to understand the impact of global change on species ranges by considering both climate and habitat changes. Using occupancy analysis of acoustic survey data in the mountains of the northeastern United States, we tested specific predictions of range responses to warming (shifting upslope), precipitation change (shifting downslope), and forest composition change (shifting downslope). We found that American red squirrels Tamiasciurus hudsonicus, key nodes in northern North American food webs, are not tracking increasing temperatures upslope, despite substantial warming in recent decades. Structural equation modeling indicates that red squirrel abundance is primarily influenced by red-spruce forest cover, which has shifted downslope with recovery from historical logging and acid deposition. Accounting for the multiple dimensions of global change will enable better predictions and more effective conservation strategies.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.07560","usgsCitation":"Morelli, T.L., Hallworth, M.T., Duclos, T., Ells, A., Faccio, S., Foster, J., McFarland, K.P., Nislow, K., Ralston, J., Ratnaswamy, M., Deluca, W.V., and Siren, A., 2025, Does habitat or climate change drive species range shifts?: Ecography, v. 2025, no. 6, e07560, 11 p., https://doi.org/10.1111/ecog.07560.","productDescription":"e07560, 11 p.","ipdsId":"IP-159616","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486086,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":489021,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07560","text":"Publisher Index Page"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","otherGeospatial":"northeastern United 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]\n}","volume":"2025","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":937215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallworth, Michael T.","contributorId":213805,"corporation":false,"usgs":false,"family":"Hallworth","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":38879,"text":"National Zoological Park, Migratory Bird 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web","interactions":[],"lastModifiedDate":"2025-03-11T15:20:29.672209","indexId":"70263714","displayToPublicDate":"2025-02-17T08:29:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting magnitude and timing of pulsed aqueous methylmercury bioaccumulation across a reservoir food web","docAbstract":"Water column hypoxia is a key process influencing methylmercury (MeHg) production and availability in waterbodies worldwide. During seasonal destratification, large, short-lived pulses of aqueous MeHg may be released into the subsequently mixed water column, but little is known about the fate of these pulses, particularly whether there are concomitant increases in MeHg uptake into aquatic food webs. We examined the magnitude and timing of MeHg uptake across several trophic guilds relative to the reservoir stratification status using biweekly mercury data from water, zooplankton, and fish (Bluegill, Lepomis macrochirus and Smallmouth Bass, Micropterus dolomieu). Zooplankton MeHg concentrations increased by up to 250% during destratification, concurrent with increases in aqueous MeHg concentrations. Zooplankton and filter-passing MeHg concentrations were positively correlated when the reservoir was mixed (R2 = 0.95) and destratifying (R2 = 0.57) but not while the reservoir was stratified (R2 = 0.21). Mercury concentrations in adult bluegill and juveniles of both fish species increased 20–70% following destratification, with responses lagging 4–8 weeks behind those in water and zooplankton MeHg. Mercury concentrations in piscivorous adult bass varied little over the course of the study. Our findings demonstrate the responsiveness of reservoir food webs to pulses in MeHg availability, suggesting that these pulses could play an important role in biotic MeHg exposure within and downstream of reservoirs.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c10719","usgsCitation":"Willacker, J., Eagles-Smith, C., Baldwin, A.K., Tate, M., Poulin, B., Naymik, J., Krabbenhoft, D.P., Myers, R., and Chandler, J.A., 2025, Contrasting magnitude and timing of pulsed aqueous methylmercury bioaccumulation across a reservoir food web: Environmental Science & Technology, v. 59, no. 8, p. 38884-3894, https://doi.org/10.1021/acs.est.4c10719.","productDescription":"11 p.","startPage":"38884","endPage":"3894","ipdsId":"IP-171553","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489859,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c10719","text":"Publisher Index Page"},{"id":482271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Brownlee Reservoir, Hells Canyon Complex, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.84602295639596,\n 45.65736840039159\n ],\n [\n -116.99320578225903,\n 45.295454810899784\n ],\n [\n -117.24983304017238,\n 44.80742011369799\n ],\n [\n -117.30121339023736,\n 44.5717012606722\n ],\n [\n -116.73893003569623,\n 44.60477566164221\n ],\n [\n -116.31503103539632,\n 45.53825313201713\n ],\n [\n -116.42980155677816,\n 45.876926450734544\n ],\n [\n -116.93007961711443,\n 45.79860430308007\n ],\n [\n -116.84602295639596,\n 45.65736840039159\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":927931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":927932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":927933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":927934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulin, Brett A.","contributorId":328488,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":927935,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Naymik, Jesse","contributorId":229386,"corporation":false,"usgs":false,"family":"Naymik","given":"Jesse","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":927936,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha−1) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha−1) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha−1, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R2 = 0.748, RMSE = 13.10 kg ha−1 from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha−1). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R2 = 0.765, RMSE = 12.37 kg ha−1), management variables (species, season, adj. R2 = 0.772, and RMSE = 12.13 kg ha−1), and biophysical variables (height, fractional ground cover, adj. R2 = 0.818, and RMSE = 10.29 kg ha−1). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.
","language":"English","publisher":"Wiley","doi":"10.1002/agj2.70011","usgsCitation":"Thieme, A., Jennewein, J., Hively, W.D., Lamb, B.T., Whitcraft, A., Mirsky, S., Reberg-Horton, S., and Justice, C., 2025, Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops: Agronomy Journal, v. 117, no. 1, e70011, 22 p., https://doi.org/10.1002/agj2.70011.","productDescription":"e70011, 22 p.","ipdsId":"IP-166011","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/agj2.70011","text":"Publisher Index Page"},{"id":491812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Eastern Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.72279086706524,\n 39.59745381238187\n ],\n [\n -76.72279086706524,\n 38.03247531099828\n ],\n [\n -75.71750552954822,\n 38.03247531099828\n ],\n [\n -75.71750552954822,\n 39.59745381238187\n ],\n [\n -76.72279086706524,\n 39.59745381238187\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"117","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":942060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":942061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. 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Macroseismic data are critical not only to investigate earthquakes that occurred before the start of the instrumental era in seismology, but are also, by virtue of the spatially rich nature of the data, of tremendous potential importance to investigate early instrumental and even recent earthquakes. Fueled by both the advent of on-line systems that now produce unprecedented volumes of macroseismic intensity data and by recognition of the importance of understanding key historical earthquakes, there has been a growing appreciation for the value of macroseismic data. It is, however, critical to understand both the unique challenges and the unique opportunities associated with these data. I review the evolution of thought and practices regarding analysis of macroseismic data, and the use of such data to improve the characterization of historic and early instrumental earthquakes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding past earthquakes","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-73580-6_3","usgsCitation":"Hough, S., 2025, Macroseismology, chap. of Understanding past earthquakes, p. 53-79, https://doi.org/10.1007/978-3-031-73580-6_3.","productDescription":"27 p.","startPage":"53","endPage":"79","ipdsId":"IP-134285","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487653,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-031-73580-6_3","text":"Publisher Index Page"},{"id":482170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-02-15","publicationStatus":"PW","contributors":{"editors":[{"text":"Elliott, Austin J.","contributorId":191820,"corporation":false,"usgs":false,"family":"Elliott","given":"Austin J.","affiliations":[],"preferred":false,"id":927799,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Gruetzner, Christoph","contributorId":351038,"corporation":false,"usgs":false,"family":"Gruetzner","given":"Christoph","affiliations":[],"preferred":false,"id":927800,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":350979,"corporation":false,"usgs":true,"family":"Hough","given":"Susan E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927630,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70271398,"text":"70271398 - 2025 - Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site","interactions":[],"lastModifiedDate":"2025-09-11T14:06:59.431869","indexId":"70271398","displayToPublicDate":"2025-02-15T08:56:42","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site","docAbstract":"Existing geothermal production in Utah is commonly collocated with surficial expressions of geothermal heat including active hot springs and hot spring deposits. However, geothermal potential across the Great Basin region is thought to be much higher for hidden or blind geothermal systems. Accordingly, exploration techniques that can locate geothermal resources that lack surface thermal features could support future development of these systems. The goal of the INGENIOUS project is to reduce exploration risk and discover new, economically viable hidden geothermal systems in the Great Basin region. This paper summarizes the efforts and preliminary results of blind geothermal resource prospecting in Utah as part of the larger INGENIOUS project. The Lund North site in the Basin and Range province of southwest Utah was designated as the fourth detailed study site for the project. The site was initially identified from the preliminary Play Fairway Analysis (PFA) geothermal favorability map, which is based on various data elements, and the identification of favorable structural settings across Utah. This site contains existing legacy data including a thermal gradient borehole with a heat-flow value exceeding 220 mW/m2. New data collected at this site include terrestrial gravity, magnetotellurics, transient electromagnetics, fluid geochemistry, geologic mapping, UAV-based lidar, paleomagnetism, aeromagnetic surveys, and a shallow temperature survey. Preliminary data and model interpretations corroborate the presence of a large, east-dipping, northeast-southwest-trending normal fault on the edge of a typical Basin and Range graben. Quaternary fault mapping refined by high-resolution lidar surveys suggests a complex step over fault geometry. Utilizing multi-disciplinary datasets for further site assessment at Lund North will facilitate the development of a local-scale PFA and geothermal conceptual model. This new data will be used to site successive thermal gradient drilling to verify heat flow with temperatures at depth at the Lund North site and further investigate the existence of a local geothermal system.
","largerWorkTitle":"Proceedings, 50th workshop on geothermal reservoir engineering","conferenceTitle":"50th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 10-12, 2025","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Hardwick, C.L., Szymanski, E., Hart-Wagoner, N.R., Ashton, S., Christensen, N., Earney, T.E., Faulds, J.E., Glen, J.M., Hiscock, A., Kirby, S., Knudsen, T., Kobe, S., Lindsey, C.R., Morbeck, B., Peacock, J., Rea-Downing, G.H., Schermerhorn, W.D., and Smith, K., 2025, Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site, in Proceedings, 50th workshop on geothermal reservoir engineering, v. 50, Stanford, CA, February 10-12, 2025, 14 p.","productDescription":"14 p.","ipdsId":"IP-174820","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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issues","interactions":[],"lastModifiedDate":"2025-01-10T15:47:05.625225","indexId":"70262010","displayToPublicDate":"2025-02-15T08:40:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Molluscan isotope sclerochronology in marine palaeoclimatology: Taxa, technique and timespan issues","docAbstract":"Study of the accretionary biomineralised hardparts of organisms (sclerochronology) can make a useful contribution to palaeoclimatology. Ontogenetic sequences of isotopic data (δ18O and Δ47 values) from the shells of marine molluscs are a source of information on seasonal sea-surface temperatures that can be used for detailed and rigorous evaluation of the outputs of numerical climate models. In situations where there is significant seasonality, and where shell preservation is adequate, accurate information about winter and summer surface temperature can be obtained from shallow-water benthic forms (bivalves and gastropods), in particular the early ontogeny of fast-growing species. Accurate information about winter surface temperature can also be obtained from individuals that lived at mid-shelf depths (20–40 m), but summer seafloor values from these need upward adjustment to derive a plausible surface temperature if thermal stratification of the water column occurs in this season. Ontogenetic δ18O profiles from planktonic pteropod gastropods are a potential source of insight into seasonal surface temperatures in the ocean basins; these organisms merit investigation for provision of information to complement shelf data.
Temperature profiles constructed from shell δ18O require an estimate of the δ18O value of ambient seawater, which can be derived by back-calculation from the Δ47-temperature supplied by the same shell material. Alternatively, through appropriate sampling and data processing, seasonal temperatures can be obtained directly from Δ47 profiles. Climate parameters are defined in terms of the mean state over a period of 30 consecutive years, a statistic (e.g., for seasonal temperatures) which can be derived from the long isotopic temperature records obtainable from bivalve species that live for many tens or hundreds of years. Efforts should be made to acquire such records, especially averaged data from crossdated shells, to specify climate parameters for precise times in the past. Information for precise times would be of particular value for icehouse intervals like the late Cenozoic, characterised by high frequency (high amplitude) climate fluctuation. Short records from non-crossdated shells can nevertheless provide useful insights into climate, particularly if a large dataset is obtained, supplying a reliable picture of the mean state and range of variation in climate parameters over the interval represented by the shells.
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estimated recharge to the Edwards aquifer and the upper zone of the Trinity aquifer, Bandera, Bexar, and Medina Counties, Texas, 1955–2022","interactions":[],"lastModifiedDate":"2025-03-13T20:01:46.08562","indexId":"sir20245112","displayToPublicDate":"2025-02-14T16:25:56","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5112","displayTitle":"Water-Budget Analysis of the Medina and Diversion Lake System, With Estimated Recharge to the Edwards Aquifer and the Upper Zone of the Trinity Aquifer, Bandera, Bexar, and Medina Counties, Texas, 1955–2022","title":"Water-budget analysis of the Medina and Diversion Lake system, with estimated recharge to the Edwards aquifer and the upper zone of the Trinity aquifer, Bandera, Bexar, and Medina Counties, Texas, 1955–2022","docAbstract":"The U.S. Geological Survey—in cooperation with the San Antonio Water System and the Edwards Aquifer Authority—used data collected during four different periods (March 1955–August 1964, October 1995–September 1996, March 2001–June 2002, and March 2017–October 2022) as part of a new study to refine previously derived relations between the altitude of the water surface of Medina Lake and recharge to the Edwards aquifer and the upper zone of the Trinity aquifer in the form of seepage losses from Medina Lake and the immediately downstream Diversion Lake. Any seepage losses that occur within the conservation pools of Medina and Diversion Lakes infiltrate the Edwards aquifer and the upper zone of the Trinity aquifer as recharge. To quantify recharge to the Edwards aquifer and the upper zone of the Trinity aquifer from Medina and Diversion Lakes, daily water budgets were used to calculate monthly and annual recharge (method 1). A new statistical analysis culminated in a new log-log weighted least-squares (WLS) regression equation that relates recharge from Medina and Diversion Lakes to the Medina Lake stage. Recharge estimates obtained by using the new log-log WLS regression equation (method 2), as well as the recharge estimated by using a method published in 1978 (referred to as the “Puente method”) (method 3), were compared with the calculated recharge during March 2017–September 2022. During March 2017–September 2022, the WLS estimated recharge was 224,310 acre-feet, 0.5 percent less than the calculated recharge of 225,400 acre-feet. The Puente method estimated recharge was 342,080 acre-feet, about 52 percent more than the calculated recharge. The analysis of the three methods indicates that WLS estimated recharge provides a more accurate accounting of actual recharge to the Edwards aquifer and the upper zone of the Trinity aquifer compared to the Puente method.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"Urban expansion is a prominent threat to biodiversity, particularly for terrestrial mammals, which are significantly impacted by disruptions in habitat connectivity and loss. It can also lead to increased human‑wildlife conflicts that contribute to species decline. Urban public works projects, such as flood control areas, may coincidentally preserve significant habitat, but their potential for conservation remains understudied. Hansen Dam, located in the city of Los Angeles, California, USA, is one of these flood control areas, hosting some of the last remaining riparian habitat in the greater Los Angeles area. We deployed motion‑triggered camera traps at 36 stratified random locations and completed small mammal trapping at 20 sites at Hansen Dam. We detected a total of 15 species, not including domesticated animals, but there is potential that additional species inhabit the area. All species detected were common and expected but demonstrate that the Hansen Dam flood control basin has a terrestrial mammal community similar to that reported for other urban habitats. These results suggest incidental habitat conservation could play a significant role in future conservation planning efforts.
","language":"English","publisher":"Pensoft","doi":"10.15560/21.1.198","usgsCitation":"Privett-Mendoza, A., Oganesyan, S., Fisher, R., Hitchcock, C.J., Clark, D., and Zellmer, A., 2025, Terrestrial mammal diversity at Hansen Dam (Los Angeles, California, USA): Flood control area acts as habitat in a human-dominated landscape: Check List: The Journal of Biodiversity Data, v. 21, no. 1, p. 198-215, https://doi.org/10.15560/21.1.198.","productDescription":"18 p.","startPage":"198","endPage":"215","ipdsId":"IP-163743","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":487696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15560/21.1.198","text":"Publisher Index Page"},{"id":482501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","otherGeospatial":"Hansen Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.395,\n 34.28\n ],\n [\n -118.395,\n 34.255\n ],\n [\n -118.355,\n 34.255\n ],\n [\n -118.355,\n 34.28\n ],\n [\n -118.395,\n 34.28\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Privett-Mendoza, Auxenia G.","contributorId":351493,"corporation":false,"usgs":false,"family":"Privett-Mendoza","given":"Auxenia G.","affiliations":[{"id":83998,"text":"Occidental College; Arroyos & Foothills Conservancy","active":true,"usgs":false}],"preferred":false,"id":928688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oganesyan, Stella","contributorId":351494,"corporation":false,"usgs":false,"family":"Oganesyan","given":"Stella","affiliations":[{"id":83999,"text":"Arroyos & Foothills Conservancy","active":true,"usgs":false}],"preferred":false,"id":928689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitchcock, Cynthia Joan 0000-0001-9293-043X","orcid":"https://orcid.org/0000-0001-9293-043X","contributorId":225261,"corporation":false,"usgs":true,"family":"Hitchcock","given":"Cynthia","email":"","middleInitial":"Joan","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Denise 0000-0002-9688-2946 drclark@usgs.gov","orcid":"https://orcid.org/0000-0002-9688-2946","contributorId":213957,"corporation":false,"usgs":true,"family":"Clark","given":"Denise","email":"drclark@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zellmer, Amanda J.","contributorId":351495,"corporation":false,"usgs":false,"family":"Zellmer","given":"Amanda J.","affiliations":[{"id":83998,"text":"Occidental College; Arroyos & Foothills Conservancy","active":true,"usgs":false}],"preferred":false,"id":928693,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263712,"text":"70263712 - 2025 - Statewide cumulative human health risk assessment of inorganics-contaminated groundwater wells, Montana, USA","interactions":[],"lastModifiedDate":"2025-02-20T15:44:39.514983","indexId":"70263712","displayToPublicDate":"2025-02-14T09:41:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Statewide cumulative human health risk assessment of inorganics-contaminated groundwater wells, Montana, USA","docAbstract":"Across the United States, rural residents rely on unregulated and generally unmonitored private wells for drinking water, which may pose serious health risks due to unrecognized contaminants. We assessed the nature, degree, and spatial distribution of cumulative health risks from inorganic contaminants in groundwater. Our analysis included nearly 84,000 data points from 6500+ wells, across 51 of Montana's 98 watersheds, using a public groundwater database. We compared a drinking water screening level cumulative risk assessment (CRA) for inorganics based on the U.S. Environmental Protection Agency (EPA) protective health thresholds (Maximum Contaminant Level Goals, Health Advisories [MCLG-HAs]) to a CRA based on EPA public supply enforceable Maximum Contaminant Levels (MCLs). Based on median concentrations of 19 inorganics (antimony, arsenic, barium, beryllium, boron, cadmium, chromium, copper, fluoride, manganese, molybdenum, nickel, nitrate, lead, selenium, strontium, thallium, uranium, zinc), 75% of watersheds had MCLG-HA-based cumulative risk values > 1.0; arsenic and uranium contributed the most risk, followed by strontium, fluoride, manganese and boron. Hence, this screening level (Tier I) CRA indicated widespread potential for unrecognized human health risk to private well users from inorganic contaminants considering both carcinogenic and non-carcinogenic risks. Sensitivity analysis showed that benchmarks applied (MCLG-HAs versus MCLs) exerted the largest control on results. Our findings identify priority regions for Tier 2 risk assessments to elucidate local sources and distributions of geogenic versus anthropomorphic contaminants. Our study is the first statewide assessment of cumulative health risk from groundwater that we are aware of, and results support increased statewide drinking water education and testing to reduce human health risks from contaminated private well water.
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Adam","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":927921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiekover, Nicklas 0009-0007-3910-9272","orcid":"https://orcid.org/0009-0007-3910-9272","contributorId":351129,"corporation":false,"usgs":false,"family":"Kiekover","given":"Nicklas","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":927922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":205668,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":927923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":927924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parker, Albert E.","contributorId":203235,"corporation":false,"usgs":false,"family":"Parker","given":"Albert E.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":927925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterson, Robert K.D.","contributorId":351130,"corporation":false,"usgs":false,"family":"Peterson","given":"Robert K.D.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":927926,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LaFave, John","contributorId":351131,"corporation":false,"usgs":false,"family":"LaFave","given":"John","affiliations":[{"id":36941,"text":"Montana Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":927927,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263597,"text":"70263597 - 2025 - Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa","interactions":[],"lastModifiedDate":"2025-02-19T15:57:20.28773","indexId":"70263597","displayToPublicDate":"2025-02-14T08:47:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa","docAbstract":"From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240040","usgsCitation":"Yoon, C., Skoumal, R., Michael, A.J., Jolly, A., Ellis, A.P., Downs, D.T., Dotray, P., Deligne, N.I., Chang, J., Bennington, N.L., Wech, A., Haney, M.M., Hotovec-Ellis, A.J., Lutu-McMoore, E., and Langkilde, M., 2025, Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa: The Seismic Record, v. 5, no. 1, p. 83-96, https://doi.org/10.1785/0320240040.","productDescription":"14 p.","startPage":"83","endPage":"96","ipdsId":"IP-170130","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927473,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927474,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927475,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lutu-McMoore, Elinor","contributorId":341954,"corporation":false,"usgs":false,"family":"Lutu-McMoore","given":"Elinor","email":"","affiliations":[{"id":81814,"text":"NOAA National Weather Service Pago Pago Office","active":true,"usgs":false}],"preferred":false,"id":927476,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Langkilde, Marcus","contributorId":350957,"corporation":false,"usgs":false,"family":"Langkilde","given":"Marcus","affiliations":[{"id":83889,"text":"NOAA NWS Pago Pago","active":true,"usgs":false}],"preferred":false,"id":927477,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70264632,"text":"70264632 - 2025 - Establishing historical baselines of arthropod assemblages using rodent paleomiddens","interactions":[],"lastModifiedDate":"2025-03-18T15:19:02.044943","indexId":"70264632","displayToPublicDate":"2025-02-14T08:06:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Establishing historical baselines of arthropod assemblages using rodent paleomiddens","docAbstract":"Aim
Arthropods are under-represented in paleoecological studies but are critical agents in ecological processes. Despite rigorous documentation of diverse arthropod assemblages from ancient rodent (or paleo) middens worldwide, their use for studying ancient arthropod diversity has stalled in recent decades. Here, we review published studies to identify how paleomidden arthropods can be leveraged to address significant questions in ecology and synthesise spatiotemporal trends in ancient arthropod diversity.
Location
Global with a focus on the Americas.
Time Period
From ~40,000 to 100 years before present.
Major Taxa Studied
Fossil arthropods preserved in rodent paleomiddens.
Methods
We identified four primary objectives in ecology that can be advanced using paleomidden arthropod fossils: the reconstruction of ancient biodiversity, the elucidation of mechanisms and processes driving arthropod range shifts, the incorporation of arthropods into ancient trophic webs, and the assessment of adaptive responses to changing environments.
Results
We identified 20 papers reporting arthropod remains, of which 14 provided data suitable for quantitative analyses. Although no significant differences in arthropod community diversity were found across broad geographic regions, substantial differences in community structure were observed. In North America, we also detected a decline in arthropod diversity from 10 to 5 ka BP, driven primarily by the absence of high-diversity assemblages during this period.
Main Conclusions
Paleomidden arthropods remain an underutilised ecological resource that can be leveraged to improve the inferential scope of arthropod research across spatiotemporal gradients. Collaboration between paleo- and neoecologists could help expand the sampling of ancient and modern arthropod communities, particularly those associated with paleomiddens, to create high-resolution datasets spanning important periods in biogeographical history. Paleomidden arthropods will remain a fundamental component for understanding arthropod biodiversity changes in the past, present, and future.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.70006","usgsCitation":"Braasch, J., Betancourt, J.L., Dezerald, O., Peiris, U., Tapia-Rozas, M., Villagra, C., Latorre, C.L., and Gonzalez, A., 2025, Establishing historical baselines of arthropod assemblages using rodent paleomiddens: Global Ecology and Biogeography, v. 34, no. 2, e70006, 12 p., https://doi.org/10.1111/geb.70006.","productDescription":"e70006, 12 p.","ipdsId":"IP-172669","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":488330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.70006","text":"Publisher Index Page"},{"id":483474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Braasch, Joseph","contributorId":352381,"corporation":false,"usgs":false,"family":"Braasch","given":"Joseph","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":931016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":931017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dezerald, Olivier","contributorId":336824,"corporation":false,"usgs":false,"family":"Dezerald","given":"Olivier","email":"","affiliations":[{"id":80869,"text":"DECOD (Ecosystem Dynamics and Sustainability), INRAE, Institut Agro, IFREMER, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":931018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peiris, Udari","contributorId":352382,"corporation":false,"usgs":false,"family":"Peiris","given":"Udari","affiliations":[{"id":84194,"text":"Pontificia Universidad Católica de Chile, Santiago","active":true,"usgs":false}],"preferred":false,"id":931019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tapia-Rozas, Maura","contributorId":352383,"corporation":false,"usgs":false,"family":"Tapia-Rozas","given":"Maura","affiliations":[{"id":84194,"text":"Pontificia Universidad Católica de Chile, Santiago","active":true,"usgs":false}],"preferred":false,"id":931020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Villagra, Cristian","contributorId":352384,"corporation":false,"usgs":false,"family":"Villagra","given":"Cristian","affiliations":[{"id":84195,"text":"Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":931021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Latorre, Claudio L.","contributorId":331520,"corporation":false,"usgs":false,"family":"Latorre","given":"Claudio","email":"","middleInitial":"L.","affiliations":[{"id":79225,"text":"Pontificia Universidad Católica de Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":931022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzalez, Angelica L.","contributorId":336826,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Angelica L.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":931023,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263500,"text":"sir20245127 - 2025 - Groundwater age estimates for the Mississippi River Valley alluvial aquifer based on tracer data collected during 2018–20","interactions":[],"lastModifiedDate":"2025-07-21T18:22:10.201666","indexId":"sir20245127","displayToPublicDate":"2025-02-13T14:55:22","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5127","displayTitle":"Groundwater Age Estimates for the Mississippi River Valley Alluvial Aquifer Based on Tracer Data Collected During 2018–20","title":"Groundwater age estimates for the Mississippi River Valley alluvial aquifer based on tracer data collected during 2018–20","docAbstract":"This study characterized groundwater age across the Mississippi River Valley alluvial aquifer (MRVA). Groundwater samples from 69 MRVA wells and 19 wells in Tertiary units of the Mississippi embayment aquifer system (MEAS) were analyzed for sulfur hexafluoride (SF6), tritium (3H), helium (He), and (or) carbon-14 of dissolved inorganic carbon (14C). The age distributions of 89 samples were estimated by fitting lumped parameter models to processed tracer concentrations with the U.S. Geological Survey software TracerLPM. Mean ages of MRVA groundwater samples ranged from 12 to 22,000 years, with a median of 140 years. Mean ages of MEAS groundwater samples ranged from 230 to 52,000 or more years, with a median of 13,500 years. The spatial distribution of MRVA groundwater ages was found to be influenced by depth, inflow of groundwater from deeper units, and soil saturated hydraulic conductivity. In parts of the MRVA, the spatial distribution of MRVA groundwater ages was found to be influenced by annual recharge and (or) annual groundwater pumpage.
Director, Lower Mississippi-Gulf Water Science Center, U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The rapid advancement of environmental DNA (eDNA) science in the past two decades has inspired a concomitant growth in the development of eDNA sampling and analytical methods. However, these methods are often developed by individual laboratories or institutions, which can isolate protocols within programmes, agencies or regions and prevent the beneficial exchange of data and ideas. Recent efforts to advance national and international coordination have resulted in a groundswell of standardisation efforts, but there is still considerable confusion around the role of formal standards for regulatory or research applications. With this commentary, we hope to provide clarity on the terminology used in standardisation discussions, including the differences between formal standards and best practice guidelines. Additionally, we discuss how eDNA method choice may be informed by environmental management scenarios and review examples of formal eDNA method standards being used to inform management action. The eDNA community now has an opportunity to develop a roadmap for method development to help close standardisation gaps, advance eDNA method adoption and accelerate our ability to monitor biological life at the scales our current environmental challenges demand.
","language":"English","publisher":"Pensoft","doi":"10.3897/mbmg.9.132076","usgsCitation":"Theroux, S., Sepulveda, A., Abbott, C., Gold, Z., Watts, A., Hunter, M., Klymus, K.E., Hirsch, S., Craine, J.M., Jones-Slobodian, D.N., Brown, R., Steele, J., Takahashi, M., Noble, R.T., and Darling, J.A., 2025, What is eDNA method standardization and why do we need it?: Metabarcoding and Metagenomics, v. 9, e132076, 16 p., https://doi.org/10.3897/mbmg.9.132076.","productDescription":"e132076, 16 p.","ipdsId":"IP-167695","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":489778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/mbmg.9.132076","text":"Publisher Index Page"},{"id":482267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2025-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Theroux, Susanna","contributorId":244544,"corporation":false,"usgs":false,"family":"Theroux","given":"Susanna","affiliations":[],"preferred":false,"id":927815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":927816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abbott, Cathryn L.","contributorId":350357,"corporation":false,"usgs":false,"family":"Abbott","given":"Cathryn 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