Urban stream restoration requires a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Urban stream restoration benefits from a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began in 2017 to assess streamflow conditions at U.S. Geological Survey streamgages along Indian and Kill Creeks in Johnson County, Kansas. These streams represent the most urban (Indian Creek) and least urban (Kill Creek) drainage basins in the county. The assessment used 40 streamflow indicators to characterize streamflow conditions for both streams and quantify the degree of hydromodification for Indian Creek. The 40 streamflow indicators consisted of 35 commonly used indicators for characterizing streamflow, 2 less common seasonality indicators, and 3 other indicators based on duration curves, runoff hydrographs, and streamflow percentile classes. The indicators represented five key components of the natural streamflow regime: magnitude, frequency, duration, timing, and rate of change. As part of the study, indicators were evaluated as to general utility for characterizing streamflow conditions, quantifying hydromodification, and assessing the effectiveness of implemented management practices intended to restore urban streams. Results identifying indicators that serve these purposes could be applied more generally to other streams in Johnson County to assess hydromodification and potential restoration opportunities. Although the same set of streamflow indicators may not apply to other regions, methods and results presented in this report provide guidance, techniques, and perspective for future related or similar studies elsewhere, particularly those designed to quantify hydromodification of urban streams and monitor the effectiveness of restoration efforts.
Compared to Kill Creek, which, for the purposes of this study, was considered representative of a least disturbed rural reference condition, Indian Creek hydrology was determined to be substantially modified because of urbanization. Of the 35 streamflow indicators evaluated, 19 indicated a generally consistent and substantial difference between the 2 streams. Hydromodification of Indian Creek was characterized by larger annual mean and monthly mean streamflows (and, thus, larger streamflow volumes), larger low streamflows of shorter duration, larger high streamflows with increased frequency and shorter duration, faster rise and fall rates, and decreased seasonality of high and low streamflows. For the two seasonality indicators, seasonality of high and low streamflows decreased. Duration curves, runoff event hydrographs, and streamflow percentile classes also indicated differences between the two streams for specific ranges of streamflow.
Indicators that were useful in identifying generally consistent and substantial differences between the two streams, and therefore demonstrating they collectively or individually may be indicators of hydromodification, included annual median and mean flows; monthly mean flows for February, July, August, September, October, November, and December; all the minimum mean flow indictors (1-day, 3-day, 7-day, 30-day, and 90-day); annual number and mean magnitude of peak flows; some of the flow pulse indicators; and rise and fall rates. Indicators determined to be marginally useful or not useful for identifying consistent and substantial streamflow differences between streams included the flashiness indicators Richards-Baker flashiness index and the fraction of the year the daily mean flow is greater than the annual mean flow, which was not expected.
Municipalities are challenged by the need to restore stream quality in urbanized areas where options are limited because of existing development. Understanding hydromodification effects and implications for stream quality can help managers plan urban development that minimizes degradation of stream quality and provides insights for implementing effective management practices. Streamflow indicators identified in this report can be used to guide urban stream restoration. In particular, the most useful indicators could form the basis of numeric criteria for restoration goals aimed at achieving or progressing toward more natural streamflow conditions—and, by extension, more healthy ecosystems—by characterizing flow conditions, quantifying hydromodification, establishing stream-restoration goals, and monitoring progress toward achieving those goals as management practices are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235063","collaboration":"Prepared in cooperation with the Johnson County Stormwater Management Program","usgsCitation":"Rasmussen, T.J., Juracek, K.E., Eslick, P.J., Eng, K., and Kellenberger, L.J., 2024, Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018: U.S. Geological Survey Scientific Investigations Report 2023–5063, 44 p., https://doi.org/10.3133/sir20235063.","productDescription":"Report: v, 44 p.; 1 Appendix; 2 Tables; Dataset","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114771","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":424135,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5063/sir20235063.XML"},{"id":424140,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table3.1.xlsx","text":"Table 3.1","size":"112 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":424139,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table2.1.csv","text":"Table 2.1","size":"10.5 kB","linkFileType":{"id":7,"text":"csv"}},{"id":424133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5063/coverthb.jpg"},{"id":424134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5063/sir20235063.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5063"},{"id":499323,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115947.htm","linkFileType":{"id":5,"text":"html"}},{"id":424143,"rank":11,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235063/full"},{"id":424142,"rank":10,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":424136,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5063/images/"},{"id":424137,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_appendix1.pdf","text":"Appendix 1","size":"606 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":424138,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table2.1.xlsx","text":"Table 2.1","size":"40.8 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":424141,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table3.1.csv","text":"Table 3.1","size":"51.4 kB","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"Kansas","county":"Johnson County","otherGeospatial":"Indian and Kill Creek Basins","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.6075,39.0437],[-94.6075,39.0399],[-94.6082,38.8463],[-94.6084,38.8341],[-94.6102,38.7376],[-95.0572,38.7395],[-95.0558,38.9816],[-95.0477,38.9778],[-95.0383,38.9771],[-95.0312,38.9773],[-95.0292,38.9813],[-95.0271,38.9881],[-95.0249,38.9962],[-95.0189,38.9987],[-95.0135,38.9991],[-95.0077,38.998],[-94.9946,38.9976],[-94.9899,38.997],[-94.9841,38.995],[-94.9789,38.9926],[-94.9755,38.9885],[-94.9704,38.9851],[-94.9645,38.9832],[-94.9575,38.982],[-94.9527,38.9828],[-94.9479,38.9845],[-94.9448,38.9871],[-94.9423,38.9898],[-94.9386,38.9933],[-94.9367,38.9964],[-94.9335,38.9995],[-94.9264,38.9998],[-94.9217,38.9996],[-94.9176,38.9977],[-94.9209,38.9919],[-94.923,38.9856],[-94.9207,38.9837],[-94.9164,38.9859],[-94.9115,38.9889],[-94.9078,38.9924],[-94.9014,39.0022],[-94.8989,39.0053],[-94.8945,39.0102],[-94.8919,39.0155],[-94.891,39.021],[-94.8875,39.0313],[-94.8824,39.0379],[-94.8768,39.0441],[-94.8681,39.052],[-94.8631,39.0564],[-94.8488,39.0578],[-94.8318,39.0546],[-94.8131,39.0486],[-94.8038,39.0456],[-94.7197,39.0435],[-94.6693,39.0433],[-94.6075,39.0437]]]},\"properties\":{\"name\":\"Johnson\",\"state\":\"KS\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The free-living thermophilic amoeba Naegleria fowleri (N. fowleri) causes the highly fatal disease primary amoebic meningoencephalitis. The environmental conditions that are favorable to the growth and proliferation of N. fowleri are not well-defined, especially in northern regions of the United States. In this study, we used culture-based methods and multiple molecular approaches to detect and analyzeN. fowleri and other Naegleria spp. in water, sediment, and biofilm samples from five hot spring sites in Grand Teton National Park, Wyoming, U.S.A. These results provide the first detections of N. fowleri in Grand Teton National Park and provide new insights into the distribution of pathogenic N. fowleri and other nonpathogenic Naegleria spp. in natural thermal water systems in northern latitudes.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.3c00650","usgsCitation":"Barnhart, E.P., Kinsey, S., Wright, P.R., Caldwell Eldridge, S.L., Hill, V., Kahler, A., Mattioli, M., Cornman, R.S., Iwanowicz, D.D., Eddy, Z., Halonen, S., Mueller, R.C., Peyton, B., and Puzon, G., 2024, Naegleria fowleri detected in Grand Teton National Park hot springs: ACS ES&T Water, v. 4, no. 2, p. 628-637, https://doi.org/10.1021/acsestwater.3c00650.","productDescription":"10 p.","startPage":"628","endPage":"637","ipdsId":"IP-151893","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":425470,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":440765,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.3c00650","text":"Publisher Index Page"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.58046877034668,\n 44.09749050172226\n ],\n [\n -110.64687896214029,\n 44.09749050172226\n ],\n [\n -110.64687896214029,\n 44.054934283597305\n ],\n [\n -110.58046877034668,\n 44.054934283597305\n ],\n [\n -110.58046877034668,\n 44.09749050172226\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.6676678993602,\n 43.64302902412618\n ],\n [\n -110.6676678993602,\n 43.62565907757332\n ],\n [\n -110.63897558140427,\n 43.62565907757332\n ],\n [\n -110.63897558140427,\n 43.64302902412618\n ],\n [\n -110.6676678993602,\n 43.64302902412618\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinsey, Stacy 0000-0001-7629-2634 skinsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7629-2634","contributorId":220238,"corporation":false,"usgs":true,"family":"Kinsey","given":"Stacy","email":"skinsey@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Peter R. 0000-0003-0305-4541 prwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0305-4541","contributorId":239858,"corporation":false,"usgs":true,"family":"Wright","given":"Peter","email":"prwright@usgs.gov","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":894231,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hill, Vince","contributorId":333925,"corporation":false,"usgs":false,"family":"Hill","given":"Vince","email":"","affiliations":[],"preferred":false,"id":894311,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kahler, Amy","contributorId":333893,"corporation":false,"usgs":false,"family":"Kahler","given":"Amy","email":"","affiliations":[{"id":17914,"text":"CDC","active":true,"usgs":false}],"preferred":false,"id":894234,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mattioli, Mia","contributorId":333891,"corporation":false,"usgs":false,"family":"Mattioli","given":"Mia","email":"","affiliations":[{"id":17914,"text":"CDC","active":true,"usgs":false}],"preferred":false,"id":894232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":894235,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":894236,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eddy, Zachary","contributorId":333894,"corporation":false,"usgs":false,"family":"Eddy","given":"Zachary","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":894237,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Halonen, Sandra","contributorId":333895,"corporation":false,"usgs":false,"family":"Halonen","given":"Sandra","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":894238,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mueller, Rebecca C.","contributorId":175360,"corporation":false,"usgs":false,"family":"Mueller","given":"Rebecca","email":"","middleInitial":"C.","affiliations":[{"id":27561,"text":"Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA","active":true,"usgs":false}],"preferred":false,"id":894240,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peyton, Brent","contributorId":333897,"corporation":false,"usgs":false,"family":"Peyton","given":"Brent","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":894241,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Puzon, Geoffrey","contributorId":333896,"corporation":false,"usgs":false,"family":"Puzon","given":"Geoffrey","affiliations":[{"id":80008,"text":"CSIRO Australia","active":true,"usgs":false}],"preferred":false,"id":894239,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70250926,"text":"70250926 - 2024 - Extreme drought impacts have been underestimated in grasslands and shrublands globally","interactions":[],"lastModifiedDate":"2024-01-12T13:59:33.314714","indexId":"70250926","displayToPublicDate":"2024-01-08T07:45:29","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Extreme drought impacts have been underestimated in grasslands and shrublands globally","docAbstract":"Hidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.
In this paper, we describe a general framework for modelling multi-state capture-recapture data via HMMs using both maximum likelihood and Bayesian methods. We then apply an HMM to invasive silver carp telemetry data from the Illinois River and compare the results estimated by both methods.
Our analysis demonstrates disadvantages of relying on a single approach and highlights insights obtained from implementing both methods together. While both methods often struggled to converge, our results show biologically informative priors for Bayesian methods and initial values for maximum likelihood methods can guide convergence toward realistic solutions. Incorporating prior knowledge of the system can successfully constrain estimation to biologically realistic movement and detection probabilities when dealing with sparse data.
Biologically unrealistic estimates may be a sign of poor model convergence. In contrast, consistent convergence behavior across approaches can increase the credibility of a model. Estimates of movement probabilities can strongly influence the predicted population dynamics of a system. Therefore, thoroughly assessing results from HMMs is important when evaluating potential management strategies, particularly for invasive species.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-023-00434-w","usgsCitation":"Labuzzetta, C.J., Coulter, A.A., and Erickson, R.A., 2024, Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River: Ecology Movement, v. 12, 2, 15 p., https://doi.org/10.1186/s40462-023-00434-w.","productDescription":"2, 15 p.","ipdsId":"IP-150787","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":440773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-023-00434-w","text":"Publisher Index Page"},{"id":426962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.0589176022094,\n 38.26712368948924\n ],\n [\n -87.45540197720929,\n 38.26712368948924\n ],\n [\n -87.45540197720929,\n 42.10926577184944\n ],\n [\n -91.0589176022094,\n 42.10926577184944\n ],\n [\n -91.0589176022094,\n 38.26712368948924\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Labuzzetta, Charles J. 0000-0002-6027-0120","orcid":"https://orcid.org/0000-0002-6027-0120","contributorId":332055,"corporation":false,"usgs":true,"family":"Labuzzetta","given":"Charles","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coulter, Alison A.","contributorId":90992,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[{"id":26877,"text":"Southern Illinois University, Carbondale, IL","active":true,"usgs":false},{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":897154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":897155,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251089,"text":"70251089 - 2024 - Can the planetary health concept save freshwater biodiversity and ecosystems?","interactions":[],"lastModifiedDate":"2024-01-23T12:54:20.120177","indexId":"70251089","displayToPublicDate":"2024-01-08T06:51:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17134,"text":"Lancet Planetary Health","active":true,"publicationSubtype":{"id":10}},"title":"Can the planetary health concept save freshwater biodiversity and ecosystems?","docAbstract":"People clearly need and benefit from healthy freshwater ecosystems; Given the precarious state of these important systems and services, current efforts to address the freshwater biodiversity crisis remain insufficient. Planetary health is an emerging framework that aims to secure the state of natural systems within environmental limits that ensure humanity can flourish. The planetary health concept is tied to the planetary boundaries framework in which various ecological thresholds are identified with the goal of constraining human activity to within those boundaries (so-called safe operating spaces). Freshwater systems are influenced by some planetary-scale processes like the climate systems and phosphorus and nitrogen cycles. Nonetheless, safe boundaries to guide the conservation and management of freshwater ecosystems need to consider their uneven distribution around the globe, and their ecological and hydrologic limits, which are often site and context dependent. Efforts to down-scale planetary boundaries concepts to the management of freshwater recreational fisheries at the lake scale, suggest that there are opportunities for rethinking planetary health as a nested cross-scale approach from the planet to the watershed.","language":"English","publisher":"Elsevier","doi":"10.1016/S2542-5196(23)00275-9","usgsCitation":"Cooke, S., Lynch, A., Tickner, D., Abell, R., Dalu, T., Fiorella, K.J., Raghavan, R., Harrison, I.J., Jahnig, S.C., Vollmer, D., and Carpenter, S., 2024, Can the planetary health concept save freshwater biodiversity and ecosystems?: Lancet Planetary Health, v. 8, no. 1, p. e2-e3, https://doi.org/10.1016/S2542-5196(23)00275-9.","productDescription":"2 p.","startPage":"e2","endPage":"e3","ipdsId":"IP-156822","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":440775,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/s2542-5196(23)00275-9","text":"Publisher Index Page"},{"id":424737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":893053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":893054,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tickner, David","contributorId":224152,"corporation":false,"usgs":false,"family":"Tickner","given":"David","email":"","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":893055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abell, Robin","contributorId":152400,"corporation":false,"usgs":false,"family":"Abell","given":"Robin","affiliations":[],"preferred":false,"id":893056,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dalu, Tatenda","contributorId":332550,"corporation":false,"usgs":false,"family":"Dalu","given":"Tatenda","email":"","affiliations":[{"id":79488,"text":"University of Mpumalanga","active":true,"usgs":false}],"preferred":false,"id":893057,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fiorella, Kathryn J.","contributorId":268093,"corporation":false,"usgs":false,"family":"Fiorella","given":"Kathryn","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":893058,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raghavan, Rajeev","contributorId":250656,"corporation":false,"usgs":false,"family":"Raghavan","given":"Rajeev","email":"","affiliations":[{"id":50216,"text":"Kerala University of Fisheries and Ocean Studies","active":true,"usgs":false}],"preferred":false,"id":893059,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harrison, Ian J.","contributorId":200864,"corporation":false,"usgs":false,"family":"Harrison","given":"Ian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":893060,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jahnig, Sonja C.","contributorId":211858,"corporation":false,"usgs":false,"family":"Jahnig","given":"Sonja","email":"","middleInitial":"C.","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":893061,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vollmer, Derek","contributorId":333549,"corporation":false,"usgs":false,"family":"Vollmer","given":"Derek","email":"","affiliations":[{"id":55551,"text":"WWF","active":true,"usgs":false}],"preferred":false,"id":893062,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carpenter, Steve","contributorId":333550,"corporation":false,"usgs":false,"family":"Carpenter","given":"Steve","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":893063,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70250932,"text":"70250932 - 2024 - Subsurface redox interactions regulate ebullitive methane flux in heterogeneous Mississippi River deltaic wetland","interactions":[],"lastModifiedDate":"2024-01-13T15:05:43.092673","indexId":"70250932","displayToPublicDate":"2024-01-07T09:03:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5407,"text":"Journal of Advances in Modeling Earth Systems","active":true,"publicationSubtype":{"id":10}},"title":"Subsurface redox interactions regulate ebullitive methane flux in heterogeneous Mississippi River deltaic wetland","docAbstract":"As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive-transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient-driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.
Studies of recovery from acidic deposition have focused on reversal of acidification and its associated effects, but as recovery proceeds slowly, chemical dilution of surface waters is emerging as a key factor in the recovery process that has significant chemical and biological implications. This investigation uses long-term chemical records from 130 streams in the Adirondack region of New York, USA, to evaluate the role of ongoing decreases in conductance, an index of dilution, in the recovery of these streams. Stream chemistry data spanning up to 40 years (1980s–2022) showed that acid-neutralizing capacity has increased in 92% of randomly selected streams, but that harmful levels of acidification still occur in 37% of these streams. Conductance and Ca2+ concentrations decreased in 79% of streams, and SO42− concentrations in streams continued to show strong decreases but remained several times higher than concentrations in precipitation. These changes were ongoing through 2022 even though acidic deposition levels were approaching those estimated for pre-industrialization. Further dilution is continuing through ongoing decreases in stream SO42−. Nevertheless, Ca2+ continued to be leached from soils by SO42−, organic acids and NO3−, limiting the replenishment of available soil Ca2+, a prerequisite to stem further dilution of stream water.
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epidem.2023.100738","usgsCitation":"Loo, S.L., Howerton, E., Contamin, L., Smith, C.P., Borchering, R.K., Mullany, L.C., Bents, S., Carcelen, E., Jung, S., Bogich, T.L., van Panhuis, W., Kerr, J., Espino, J., Yan, K., Hochheiser, H., Runge, M.C., Shea, K., Lessler, J., Viboud, C., and Truelove, S., 2024, The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy: Epidemics, v. 46, 100738, 10 p., https://doi.org/10.1016/j.epidem.2023.100738.","productDescription":"100738, 10 p.","ipdsId":"IP-158025","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":489978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epidem.2023.100738","text":"Publisher Index Page"},{"id":482971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Loo, Sara L","contributorId":331821,"corporation":false,"usgs":false,"family":"Loo","given":"Sara","email":"","middleInitial":"L","affiliations":[{"id":79288,"text":"Johns Hopkins University Infectious Disease Dynamics","active":true,"usgs":false}],"preferred":false,"id":929838,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howerton, Emily 0000-0002-0639-3728","orcid":"https://orcid.org/0000-0002-0639-3728","contributorId":258035,"corporation":false,"usgs":false,"family":"Howerton","given":"Emily","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":929839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Contamin, Lucie","contributorId":258068,"corporation":false,"usgs":false,"family":"Contamin","given":"Lucie","email":"","affiliations":[],"preferred":false,"id":929840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Claire P.","contributorId":258036,"corporation":false,"usgs":false,"family":"Smith","given":"Claire","email":"","middleInitial":"P.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":929841,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borchering, Rebecca K. 0000-0003-4309-2913","orcid":"https://orcid.org/0000-0003-4309-2913","contributorId":258031,"corporation":false,"usgs":false,"family":"Borchering","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":929842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullany, Luke C","contributorId":301869,"corporation":false,"usgs":false,"family":"Mullany","given":"Luke","email":"","middleInitial":"C","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":929843,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bents, Samantha","contributorId":331818,"corporation":false,"usgs":false,"family":"Bents","given":"Samantha","email":"","affiliations":[{"id":52216,"text":"National Institutes of Health Fogarty International Center","active":true,"usgs":false}],"preferred":false,"id":929844,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carcelen, Erica","contributorId":351991,"corporation":false,"usgs":false,"family":"Carcelen","given":"Erica","affiliations":[{"id":84077,"text":"Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":929845,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jung, Sung-mok","contributorId":331819,"corporation":false,"usgs":false,"family":"Jung","given":"Sung-mok","email":"","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":929846,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bogich, Tiffany L. 0000-0002-8143-5289","orcid":"https://orcid.org/0000-0002-8143-5289","contributorId":260459,"corporation":false,"usgs":false,"family":"Bogich","given":"Tiffany","email":"","middleInitial":"L.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":929847,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"van Panhuis, Willem G.","contributorId":304740,"corporation":false,"usgs":false,"family":"van Panhuis","given":"Willem G.","affiliations":[],"preferred":false,"id":929848,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kerr, Jessica","contributorId":331824,"corporation":false,"usgs":false,"family":"Kerr","given":"Jessica","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":929849,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Espino, Jessi","contributorId":351955,"corporation":false,"usgs":false,"family":"Espino","given":"Jessi","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":929850,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Yan, Katie","contributorId":351822,"corporation":false,"usgs":false,"family":"Yan","given":"Katie","affiliations":[{"id":84058,"text":"The Pennsylvania State University, University Park, Pennsylvania, USA","active":true,"usgs":false}],"preferred":false,"id":929851,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hochheiser, Harry","contributorId":290452,"corporation":false,"usgs":false,"family":"Hochheiser","given":"Harry","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":929852,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929853,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":929854,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Lessler, Justin","contributorId":258042,"corporation":false,"usgs":false,"family":"Lessler","given":"Justin","email":"","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":929855,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Viboud, Cécile","contributorId":351985,"corporation":false,"usgs":false,"family":"Viboud","given":"Cécile","affiliations":[{"id":52216,"text":"National Institutes of Health Fogarty International Center","active":true,"usgs":false}],"preferred":false,"id":929856,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Truelove, Shaun","contributorId":258037,"corporation":false,"usgs":false,"family":"Truelove","given":"Shaun","email":"","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":929857,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70250867,"text":"70250867 - 2024 - Planning hydrological restoration of coastal wetlands: Key model considerations and solutions","interactions":[],"lastModifiedDate":"2024-01-25T14:55:29.470908","indexId":"70250867","displayToPublicDate":"2024-01-06T09:21:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Planning hydrological restoration of coastal wetlands: Key model considerations and solutions","docAbstract":"The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.169881","usgsCitation":"Twomey, A., Nunez, K., Carr, J., Crooks, S., Friess, D., Glamore, W., Orr, M., Reef, R., Rogers, K., Waltham, N., and Lovelock, C.E., 2024, Planning hydrological restoration of coastal wetlands: Key model considerations and solutions: Science of the Total Environment, v. 915, 169881, 16 p., https://doi.org/10.1016/j.scitotenv.2024.169881.","productDescription":"169881, 16 p.","ipdsId":"IP-156710","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":440785,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.169881","text":"Publisher Index Page"},{"id":424276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"915","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twomey, Alice","contributorId":333063,"corporation":false,"usgs":false,"family":"Twomey","given":"Alice","email":"","affiliations":[{"id":13335,"text":"The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nunez, Karinna","contributorId":333064,"corporation":false,"usgs":false,"family":"Nunez","given":"Karinna","email":"","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":891827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crooks, Steve","contributorId":333065,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":891829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friess, Daniel A.","contributorId":35454,"corporation":false,"usgs":false,"family":"Friess","given":"Daniel A.","affiliations":[{"id":25407,"text":"Department of Geography, National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":891830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glamore, William","contributorId":333067,"corporation":false,"usgs":false,"family":"Glamore","given":"William","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":891831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Orr, Michelle","contributorId":197537,"corporation":false,"usgs":false,"family":"Orr","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":891832,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reef, Ruth","contributorId":298614,"corporation":false,"usgs":false,"family":"Reef","given":"Ruth","affiliations":[{"id":64623,"text":"Monash University, Australia","active":true,"usgs":false}],"preferred":false,"id":891833,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Kerrylee","contributorId":64151,"corporation":false,"usgs":false,"family":"Rogers","given":"Kerrylee","email":"","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":891834,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Waltham, Nathan","contributorId":333070,"corporation":false,"usgs":false,"family":"Waltham","given":"Nathan","email":"","affiliations":[{"id":40403,"text":"James Cook University","active":true,"usgs":false}],"preferred":false,"id":891835,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891836,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70252011,"text":"70252011 - 2024 - Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","interactions":[],"lastModifiedDate":"2024-03-11T12:18:44.85142","indexId":"70252011","displayToPublicDate":"2024-01-06T07:15:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1517,"text":"Engineering Geology","active":true,"publicationSubtype":{"id":10}},"title":"Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","docAbstract":"Lithology and geologic structure are important controls on landslide susceptibility and are incorporated into many regional landslide hazard models. Typically, metrics for mapped geologic units are used as model input variables and a single set of values for material strength are assumed, regardless of spatial heterogeneities that may exist within a map unit. Here we describe how differences in bedding thickness, grain size, inferred uniaxial compressive strength, and bedding dip control the inherent susceptibility of slopes to deep-seated failure within a single mapped geologic unit - the Tyee Formation of Oregon, USA. The Tyee, which covers over 15,000 km2 and underlies much of the Oregon Coast Range, comprises gently folded alternating beds of sandstone and siltstone deposited as turbidites, forming a 2-km thick Eocene submarine fan which has been uplifted and exhumed through the Cenozoic. Deep-seated landslides are widespread in the Tyee, but form a complex spatial pattern such that landslide density ranges from 0 to 24% of the total landscape area. These slides are often extensive and sufficiently deep to reduce local hillslope gradients, resulting in a strong negative correlation between landslide density and mean local slope. Mean annual precipitation and predicted strong ground motions from Cascadia earthquake scenarios also fail to explain the spatial distribution of deep-seated landslides. Consequently, landslide stability models, which are strongly influenced by landscape slope, pore-water pressure, and seismic acceleration, yield landslide susceptibility maps which are broadly anti-correlated with mapped deep-seated landslide density. Through a multivariable linear regression model, we show that much of the variance in deep-seated landslide density can be explained by variability of intra-unit stratigraphic and structural characteristics, which we measure at 128 sites across two study areas totaling ∼3000 km2. Our results suggest bedding dip is only weakly correlated to landslide density, but strongly influences landslide failure style. Subtle increases in bedding dip, even in the gently folded Tyee Formation, result in a substantially higher likelihood of a landslide being cataclinal, or parallel to bedding. Overall, we find a slight majority of landslides fail within these cataclinal slopes, and that these landslides tend to be larger than non-cataclinal landslides. We also show that the lithological and structural properties that influence landslide susceptibility are distinct for these two populations of landslides. Our results demonstrate how localized, intra-unit, geologic variability can exert strong control on landslide susceptibility and failure style. This suggests that in some locations, landslide hazard models could be significantly improved by incorporating detailed, spatially variable, geologic properties rather than relying solely on generalized geologic map units.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2023.107387","usgsCitation":"LaHusen, S.R., and Grant, A.R., 2024, Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA: Engineering Geology, v. 329, 107387, 14 p., https://doi.org/10.1016/j.enggeo.2023.107387.","productDescription":"107387, 14 p.","ipdsId":"IP-146959","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enggeo.2023.107387","text":"Publisher Index Page"},{"id":426489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tyee Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.10474453625554,\n 42.47204971687748\n ],\n [\n -121.72095547375605,\n 42.47204971687748\n ],\n [\n -121.72095547375605,\n 45.35210028381104\n ],\n [\n -125.10474453625554,\n 45.35210028381104\n ],\n [\n -125.10474453625554,\n 42.47204971687748\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"329","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":896263,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274033,"text":"70274033 - 2024 - Integrating the human dimensions into fish and wildlife management depends on increasing managers’ social science fluency","interactions":[],"lastModifiedDate":"2026-02-23T17:50:15.823548","indexId":"70274033","displayToPublicDate":"2024-01-06T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1909,"text":"Human Dimensions of Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Integrating the human dimensions into fish and wildlife management depends on increasing managers’ social science fluency","docAbstract":"It is a common experience in human dimensions to hear people say, “wildlife management is people management.” Good people management requires the full integration of the human dimensions into natural resources work. This means going beyond conducting human dimensions research to understanding and applying lessons learned from social science. A key step here is building managers’ fluency in social science concepts so they can more easily relate existing literature to their own practical questions. I use three example issues from human-wildlife conflict – calibrating trust, managing anger, and fostering autonomy – to illustrate how increased fluency in psychology could inform conflict management in the context of larger sociopolitical discourses. I conclude with ideas for how organizations and scientists could help managers build this integrative capacity in order to better achieve a shared objective of a wildlife management profession that works well with people for the good of humans and wildlife.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10871209.2024.2301965","usgsCitation":"Jones, M.S., 2024, Integrating the human dimensions into fish and wildlife management depends on increasing managers’ social science fluency: Human Dimensions of Wildlife, v. 30, no. 4, p. 442-449, https://doi.org/10.1080/10871209.2024.2301965.","productDescription":"8 p.","startPage":"442","endPage":"449","ipdsId":"IP-149593","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500595,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10871209.2024.2301965","text":"Publisher Index Page"},{"id":500435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Megan Siobhan 0000-0002-4284-3650","orcid":"https://orcid.org/0000-0002-4284-3650","contributorId":294651,"corporation":false,"usgs":true,"family":"Jones","given":"Megan","email":"","middleInitial":"Siobhan","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956227,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250949,"text":"70250949 - 2024 - Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","interactions":[],"lastModifiedDate":"2024-01-13T15:15:09.210779","indexId":"70250949","displayToPublicDate":"2024-01-05T09:11:23","publicationYear":"2024","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":"Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","docAbstract":"Hawaiian honeycreepers, a group of endemic Hawaiian forest birds, are being threatened by avian malaria, a non-native disease that is driving honeycreepers populations to extinction. Avian malaria is caused by the parasite Plasmodium relictum, which is transmitted by the invasive mosquito Culex quinquefasciatus. Environmental and geographical factors play an important role in shaping mosquito-borne disease transmission dynamics through their influence on the distribution and abundance of mosquitoes. We assessed the effects of environmental (temperature, precipitation), geographic (site, elevation, distance to anthropogenic features), and trap type (CDC light trap, CDC gravid trap) factors on mosquito occurrence and abundance. Occurrence was analyzed using classification and regression tree models (CART) and generalized linear models (GLM); abundance (count data) was analyzed using generalized linear mixed models (GLMMs). Models predicted highest mosquito occurrence at mid-elevation sites and between July and November. Occurrence increased with temperature and precipitation up to 580 mm. For abundance, the best model was a zero-inflated negative-binomial model that indicated higher abundance of mosquitoes at mid-elevation sites and peak abundance between August and October. Estimation of occurrence and abundance as well as understanding the factors that influence them are key for mosquito control, which may reduce the risk of forest bird extinction.
Bird populations are declining globally. Wind and solar energy can reduce emissions of fossil fuels that drive anthropogenic climate change, yet renewable-energy production represents a potential threat to bird species. Surveys to assess potential effects at renewable-energy facilities are exclusively local, and the geographic extent encompassed by birds killed at these facilities is largely unknown, which creates challenges for minimizing and mitigating the population-level and cumulative effects of these fatalities. We performed geospatial analyses of stable hydrogen isotope data obtained from feathers of 871 individuals of 24 bird species found dead at solar- and wind-energy facilities in California (USA). Most species had individuals with a mix of origins, ranging from 23% to 98% nonlocal. Mean minimum distances to areas of likely origin for nonlocal individuals were as close as 97 to >1250 km, and these minimum distances were larger for species found at solar-energy facilities in deserts than at wind-energy facilities in grasslands (Cohen's d = 6.5). Fatalities were drawn from an estimated 30–100% of species’ desingated ranges, and this percentage was significantly smaller for species with large ranges found at wind facilities (Pearson's r = −0.67). Temporal patterns in the geographic origin of fatalities suggested that migratory movements and nonmigratory movements, such as dispersal and nomadism, influence exposure to fatality risk for these birds. Our results illustrate the power of using stable isotope data to assess the geographic extent of renewable-energy fatalities on birds. As the buildout of renewable-energy facilities continues, accurate assessment of the geographic footprint of wildlife fatalities can be used to inform compensatory mitigation for their population-level and cumulative effects.
In October 2020, a new population of invasive brown treesnakes (Boiga irregularis) was discovered on the 33-ha Cocos Island, 2.5 km off the south coast of Guam. Cocos Island is a unique conservation resource, providing refuge for many lizards and birds, including endangered species, which were extirpated from mainland Guam by invasive predators including brown treesnakes. We sought to evaluate the usefulness of toxic baiting with acetaminophen-treated carrion baits and cage trapping, common tools for the control of brown treesnakes on mainland Guam, as potential eradication tools on Cocos Island. We evaluated multiple bait types and bait presentations: on the ground, suspended in the canopy emulating aerial bait applications and in four plastic-tube bait station configurations intended to exclude non-target species. We monitored all baits with time-lapse cameras. Despite improved exclusion of non-targets by bait station design, most baits were quickly removed by non-target species, particularly coconut crabs (Birgus latro) and Mariana monitors (Varanus tsukamotoi). Monitoring of 1,250 baits available for 2,427 bait nights resulted in no observations of brown treesnakes taking any bait. Subsequently, we tested two trap types commonly used on Guam and compared trapping success with live versus dead mouse lures. In 10,553 trap nights using live and dead mouse lures, we only captured one brown treesnake, in a trap with a live mouse lure. These baiting and trapping rates are so low as to be ineffectual for all practical purposes. Concurrent visual searching and hand capture of brown treesnakes during initial rapid response efforts demonstrates that these low baiting and trapping success rates are not a result of low snake density. We make a case for our assumption that the ineffectiveness of these tools on Cocos Island is due to the context of extremely high abundance of preferred live prey, primarily large geckos and birds. Our results have profound conservation ramifications, because any future island invasions by brown treesnakes are likely to occur within similarly prey-rich environments where these baiting and trapping methods might be similarly ineffective.
","language":"English","publisher":"Pensoft","doi":"10.3897/neobiota.90.103041","usgsCitation":"Siers, S.R., Nafus, M., Calaor, J.E., Volsteadt, R.M., Grassi, M.S., Volsteadt, M., Collins, A.F., Barnhart, P., Huse, L.T., Yackel Adams, A.A., and Vice, D.L., 2024, Limitations of invasive snake control tools in the context of a new invasion on an island with abundant prey: NeoBiota, v. 90, p. 1-33, https://doi.org/10.3897/neobiota.90.103041.","productDescription":"33 p.","startPage":"1","endPage":"33","ipdsId":"IP-143261","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":440793,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.90.103041","text":"Publisher Index Page"},{"id":435065,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MT1JNO","text":"USGS data release","linkHelpText":"Cocos Island, Guam Brown Treesnake Rapid Response Visual Survey and Capture Data, 10/2020 - 05/2023"},{"id":426051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Cocos Island, Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.65710932315943,\n 13.243675349902261\n ],\n [\n 144.64497735020223,\n 13.236713036998665\n ],\n [\n 144.64352875641617,\n 13.232747079534391\n ],\n [\n 144.66100241895901,\n 13.23988576858983\n ],\n [\n 144.65710932315943,\n 13.243675349902261\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"90","noUsgsAuthors":false,"publicationDate":"2024-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":895542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nafus, Melia Gail 0000-0002-7325-3055","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":245717,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia Gail","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":895543,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Calaor, Jaried E.","contributorId":334395,"corporation":false,"usgs":false,"family":"Calaor","given":"Jaried","email":"","middleInitial":"E.","affiliations":[{"id":54632,"text":"Research Corporation of the University of Guam","active":true,"usgs":false}],"preferred":false,"id":895544,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Volsteadt, Rachel M.","contributorId":257490,"corporation":false,"usgs":false,"family":"Volsteadt","given":"Rachel","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":895545,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grassi, Matthew S.","contributorId":334396,"corporation":false,"usgs":false,"family":"Grassi","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":54632,"text":"Research Corporation of the University of Guam","active":true,"usgs":false}],"preferred":false,"id":895546,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Volsteadt, Megan","contributorId":334397,"corporation":false,"usgs":false,"family":"Volsteadt","given":"Megan","email":"","affiliations":[{"id":80127,"text":"Guam Department of Agriculture, Division of Aquatic and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":895547,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collins, Aaron F.","contributorId":334398,"corporation":false,"usgs":false,"family":"Collins","given":"Aaron","email":"","middleInitial":"F.","affiliations":[{"id":65960,"text":"USDA Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":895548,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, Patrick D","contributorId":334399,"corporation":false,"usgs":false,"family":"Barnhart","given":"Patrick D","affiliations":[{"id":65960,"text":"USDA Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":895549,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huse, Logan Tanner 0000-0002-8237-1248","orcid":"https://orcid.org/0000-0002-8237-1248","contributorId":334400,"corporation":false,"usgs":true,"family":"Huse","given":"Logan","email":"","middleInitial":"Tanner","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":895550,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":895551,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vice, Diane L.","contributorId":334401,"corporation":false,"usgs":false,"family":"Vice","given":"Diane","email":"","middleInitial":"L.","affiliations":[{"id":80127,"text":"Guam Department of Agriculture, Division of Aquatic and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":895552,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70251132,"text":"70251132 - 2024 - Heterogeneous multi-stage accretionary orogenesis — Evidence from the Gunnison block in the Yavapai Province, southwest USA","interactions":[],"lastModifiedDate":"2024-01-24T13:12:49.893969","indexId":"70251132","displayToPublicDate":"2024-01-05T07:09:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Heterogeneous multi-stage accretionary orogenesis — Evidence from the Gunnison block in the Yavapai Province, southwest USA","docAbstract":"Proterozoic rocks exposed in the southwestern U.S.A. represent one of the best examples of crustal growth by arc-related magmatism and accretionary orogenesis. Within the Southwest the 1.8–1.7 Ga Yavapai Province is widely regarded as a classic example of juvenile arc crust, however 1.8–2.5 Ga inherited zircon and Nd and Hf model ages have been recognized near Gunnison in central Colorado. These data have led to questions regarding the extent and nature of pre-1.8 Ga crustal material and the genesis of the Yavapai Province. We present evidence for a geochemically distinct, spatially restricted crustal block underlain by pre-1.8 Ga crust material (referred to here as the Gunnison block) in central to western Colorado within the Yavapai Province. The Gunnison block is characterized by 1.8–1.9 and 2.4–2.6 Ga inherited zircon, Pb isotopic systematics (μ = 9.8 ± 0.1, κ = 3.7 ± 0.1) elevated relative to 1.8 Ga depleted mantle values, 1.8–2.5 Ga Nd and Hf model ages, and a distinct pressure-temperature-time history. The geochemical data are consistent with mixing between juvenile 1.8 Ga and pre-1.8 Ga sources. The older crustal component is most similar to the isotopically enriched Mojave Province of eastern California and western Arizona, suggesting greater similarities between these provinces than previously recognized. Monazite and xenotime petrochronology indicate ca. 1.75–1.74, 1.72–1.69, 1.67, and 1.47–1.38 Ga tectono-metamorphic events. These data suggest that the Gunnison block accreted to other components of the Yavapai Province outboard of Laurentia at 1.75–1.74 Ga. The composite Yavapai Province was accreted to the margin of Laurentia during the 1.72–1.69 Ga Yavapai orogeny. Later overprinting is associated with the ∼1.68–1.60 Ga Mazatzal and ∼1.47–1.37 Ga Picuris orogenies. Identification of distinct crustal terranes within the Yavapai Province supports models involving multiple arcs and back-arcs that were progressively assembled prior to their accretion to Laurentia, perhaps akin to the present-day Banda Sea in Indonesia.
Accurate estimation and monitoring of wildlife population trends is foundational to evidence-based conservation. Here, we use hierarchical modelling to estimate population trends for six species of management interest (coyotes; red foxes, white-tailed deer, gray foxes; eastern wild turkey, and bobcats) while accounting for observation error from a long-term camera trap survey conducted across the State of New York. We were able to detect population level trends in occurrence and abundance and produce spatially explicit predictions for all six species using a combination of single-species occupancy models and Royle-Nichols models. Coyote (mean λ = 1.22, 95 % CI = 0.85–1.82) and red fox (mean λ = 1.17, 95 % CI = 0.95–1.46) populations were widely distributed with stable populations across the sampling period from 2014 to 2021. White-tailed deer populations were highly abundant and displayed an increasing population trend (mean λ = 1.85, 95 % CI = 1.54–2.10). Eastern wild turkey occupancy remained low across the state despite displaying a slight increase in occupancy over the sampling period (mean ψ = 0.16, 95 % CI = 0.07–0.25). Gray fox occupancy was also low (mean ψ = 0.22, 95 % CI = 0.12–0.29), consistent with growing concerns over the species across North America. Despite recent recoveries elsewhere, bobcat populations in New York State displayed very low occupancy (mean ψ = 0.07, 95 % CI = 0.02–0.12), highlighting the necessity of monitoring to inform conservation action. We provide empirically supported management implications for each species and demonstrate the efficacy of long-term camera trapping to provide robust evidence on population trends while accounting for imperfect detections, over scales meaningful to species management and conservation.
In June 2017, we documented the first observed spawning event by a reintroduced population of Lake Sturgeon (Acipenser fulvescens) in Fall Creek, a tributary to Cayuga Lake, New York, USA. This is the first observed spawning encounter of adult Lake Sturgeon since the beginning of the multi-agency Lake Sturgeon restoration effort in Cayuga Lake initiated in 1995 by the New York State Department of Environmental Conservation. Lake Sturgeon egg deposition was found specifically on substrate mainly composed of gravel sized rocks with depths and flows that made up a unique microhabitat combination within the creek which is not typical of habitat identified in other Lake Sturgeon spawning habitat studies across the Great Lakes. An estimated 810,052 ± 24,386 eggs were deposited in the sampled area of Fall Creek. The identified, potentially productive spawning microhabitat type in Fall Creek is likely to be widespread in similar tributaries around Cayuga Lake as well as small tributaries to other Finger Lakes and Lake Ontario. Ongoing research is focused on the evaluation of the extent of Finger Lakes habitat similar to that identified in Fall Creek. This microhabitat evaluation of sturgeon spawning and the broad scale landscape knowledge of tributary habitats, should support subsequent management to restore Lake Sturgeon.
Paciorek and Wehner raise important questions around our use of the Mann-Kendall nonparametric trend test on smoothed data for analyzing long-term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p-values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends).
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL105124","usgsCitation":"Biederman, J.A., Zhang, F., Dannenberg, M.P., Yan, D., Reed, S., and Smith, W.K., 2024, Reply to comment on \"Five decades of observed daily precipitation reveal longer and more variable drought events across much of the western United States\": Journal of Geophysical Research, v. 51, no. 1, e2023GL105124, 6 p., https://doi.org/10.1029/2023GL105124.","productDescription":"e2023GL105124, 6 p.","ipdsId":"IP-154606","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":440799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl105124","text":"Publisher Index Page"},{"id":432032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.06751495056879,\n 32.0119835429842\n ],\n [\n -102.98743753076731,\n 36.993099498285346\n ],\n [\n -102.06791186487006,\n 37.09353770710277\n ],\n [\n -102.09308594191134,\n 41.069216907905286\n ],\n [\n -102.34800791515715,\n 48.99550250125003\n ],\n [\n -123.27911288566722,\n 48.98865726463285\n ],\n [\n -123.1795305777867,\n 48.31008011632255\n ],\n [\n -124.719113482527,\n 48.39103080262578\n ],\n [\n -124.3871471391106,\n 47.15752397273701\n ],\n [\n -123.98769447051444,\n 44.57373873393493\n ],\n [\n -124.596418783085,\n 42.99648431592081\n ],\n [\n -124.23397557215532,\n 41.39175380482783\n ],\n [\n -124.54483887239707,\n 40.37437833634513\n ],\n [\n -123.98124096809096,\n 39.545871342131676\n ],\n [\n -123.66754462725268,\n 38.78499566774218\n ],\n [\n -123.07771629761646,\n 38.03495488174764\n ],\n [\n -122.3694596530575,\n 37.06315898923884\n ],\n [\n -121.96831232538602,\n 36.302571682480774\n ],\n [\n -120.738875039444,\n 34.87824783254452\n ],\n [\n -120.21025192953425,\n 33.59503953576042\n ],\n [\n -118.4748537969432,\n 32.93429118822007\n ],\n [\n -116.92815682354961,\n 32.49483101958306\n ],\n [\n -114.61078821180485,\n 32.65904638000069\n ],\n [\n -114.76889651868294,\n 32.433686759935995\n ],\n [\n -111.07388284638813,\n 31.242672789119908\n ],\n [\n -108.06396793132916,\n 31.274651251462373\n ],\n [\n -108.1337794861821,\n 31.719498997119473\n ],\n [\n -106.46019825998971,\n 31.71668673143826\n ],\n [\n -103.06751495056879,\n 32.0119835429842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Biederman, Joel A.","contributorId":201939,"corporation":false,"usgs":false,"family":"Biederman","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":908528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Fangyue","contributorId":266007,"corporation":false,"usgs":false,"family":"Zhang","given":"Fangyue","email":"","affiliations":[{"id":54855,"text":"USDA Agricultural Research Service Southwest Watershed Research Center, Tucson, Arizona 85719 ; School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona 85721","active":true,"usgs":false}],"preferred":false,"id":908529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dannenberg, Matthew P.","contributorId":239668,"corporation":false,"usgs":false,"family":"Dannenberg","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":47960,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ; Geographical and Sustainability Services, University of Iowa, Iowa City, IA","active":true,"usgs":false}],"preferred":false,"id":908530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yan, Dong","contributorId":207300,"corporation":false,"usgs":false,"family":"Yan","given":"Dong","email":"","affiliations":[{"id":37515,"text":"University of Arizona School of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":908532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":908533,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251052,"text":"70251052 - 2024 - Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","interactions":[],"lastModifiedDate":"2024-01-19T15:15:53.833192","indexId":"70251052","displayToPublicDate":"2024-01-04T09:15:25","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-002","title":"Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","docAbstract":"The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale.\n\nThe workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. \n\nConfidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM.\nLocal monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.","language":"English","publisher":"Chesapeake Bay Program STAC (Scientific and Technical Advisory Committee)","usgsCitation":"Berger, K., Filippino, K.C., Shenk, G.W., Goulet, N., Lookenbill, M., Moyer, D.L., Noe, G.E., Porter, A.J., Shallenberger, J., Thomas, B., and Yactayo, G., 2024, Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model: STAC Workshop Report 24-002, 35 p.","productDescription":"35 p.","ipdsId":"IP-160274","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":424622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424607,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/document-library/22313/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1904296875,\n 38.41916639395372\n ],\n [\n -75.223388671875,\n 38.64261790634527\n ],\n [\n -75.35522460937499,\n 38.79690830348427\n ],\n [\n -75.498046875,\n 38.87392853923629\n ],\n [\n -75.5419921875,\n 39.0533181067413\n ],\n [\n -75.662841796875,\n 39.30029918615029\n ],\n [\n -75.750732421875,\n 39.70718665682654\n ],\n [\n -75.6298828125,\n 40.052847601823984\n ],\n [\n -75.69580078125,\n 40.07807142745009\n ],\n [\n -75.95947265625,\n 40.052847601823984\n ],\n [\n -76.0693359375,\n 40.069664523297774\n ],\n [\n -76.058349609375,\n 40.18726672309203\n ],\n [\n -75.9375,\n 40.29628651711716\n ],\n [\n -75.91552734375,\n 40.3549167507906\n ],\n [\n -75.89355468749999,\n 40.47202439692057\n ],\n [\n -76.09130859375,\n 40.56389453066509\n ],\n [\n -76.190185546875,\n 40.64730356252251\n ],\n [\n -76.0693359375,\n 40.75557964275589\n ],\n [\n -75.83862304687499,\n 40.871987756697415\n ],\n [\n -75.76171875,\n 40.91351257612758\n ],\n [\n -75.706787109375,\n 40.95501133048621\n ],\n [\n -75.7177734375,\n 41.071069130806414\n ],\n [\n -75.662841796875,\n 41.1455697310095\n ],\n [\n -75.5419921875,\n 41.13729606112276\n ],\n [\n -75.322265625,\n 41.104190944576466\n ],\n [\n -75.377197265625,\n 41.22824901518529\n ],\n [\n -75.377197265625,\n 41.28606238749825\n ],\n [\n -75.377197265625,\n 41.43449030894922\n ],\n [\n -75.399169921875,\n 41.6154423246811\n ],\n [\n -75.34423828125,\n 41.68111756290652\n ],\n [\n -75.2783203125,\n 41.91045347666418\n ],\n [\n -75.38818359375,\n 42.00848901572399\n ],\n [\n -75.377197265625,\n 42.09007006868398\n ],\n [\n -75.223388671875,\n 42.17968819665961\n ],\n [\n -74.970703125,\n 42.26917949243506\n ],\n [\n -74.8388671875,\n 42.32606244456202\n ],\n [\n -74.520263671875,\n 42.415346114253616\n ],\n [\n -74.278564453125,\n 42.54498667313236\n ],\n [\n -74.322509765625,\n 42.64204079304426\n ],\n [\n -74.410400390625,\n 42.80346172417078\n ],\n [\n -74.68505859374999,\n 42.924251753870685\n ],\n [\n -75.069580078125,\n 42.98053954751642\n ],\n [\n -75.38818359375,\n 42.96446257387128\n ],\n [\n -75.684814453125,\n 42.93229601903058\n ],\n [\n -75.9375,\n 42.87596410238256\n ],\n [\n -76.201171875,\n 42.827638636242284\n ],\n [\n -76.26708984375,\n 42.72280375732727\n ],\n [\n -76.2890625,\n 42.601619944327965\n ],\n [\n -76.2890625,\n 42.52069952914966\n ],\n [\n -76.343994140625,\n 42.415346114253616\n ],\n [\n -76.46484375,\n 42.382894009614034\n ],\n [\n -76.640625,\n 42.431565872579185\n ],\n [\n -76.7724609375,\n 42.39912215986002\n ],\n [\n -76.80541992187499,\n 42.24478535602799\n ],\n [\n -76.88232421875,\n 42.285437007491545\n ],\n [\n -76.9482421875,\n 42.415346114253616\n ],\n [\n -77.04711914062499,\n 42.44778143462245\n ],\n [\n -77.14599609375,\n 42.415346114253616\n ],\n [\n -77.2998046875,\n 42.382894009614034\n ],\n [\n -77.222900390625,\n 42.54498667313236\n ],\n [\n -77.442626953125,\n 42.69858589169842\n ],\n [\n -77.574462890625,\n 42.60970621339408\n ],\n [\n -77.640380859375,\n 42.48830197960227\n ],\n [\n -77.728271484375,\n 42.439674178149424\n ],\n [\n -77.6513671875,\n 42.31793945446847\n ],\n [\n -77.596435546875,\n 42.22851735620852\n ],\n [\n -77.5634765625,\n 42.09007006868398\n ],\n [\n -77.6953125,\n 41.92680320648791\n ],\n [\n -77.9150390625,\n 41.83682786072714\n ],\n [\n -78.0908203125,\n 41.795888098191426\n ],\n [\n -78.453369140625,\n 41.599013054830216\n ],\n [\n -78.453369140625,\n 41.50857729743935\n ],\n [\n -78.42041015625,\n 41.376808565702355\n ],\n [\n -78.3984375,\n 41.21172151054787\n ],\n [\n -78.519287109375,\n 41.054501963290505\n ],\n [\n -78.541259765625,\n 40.9218144123785\n ],\n [\n -78.409423828125,\n 40.713955826286046\n ],\n [\n -78.299560546875,\n 40.55554790286311\n ],\n [\n -78.343505859375,\n 40.48873742102282\n ],\n [\n -78.475341796875,\n 40.30466538259176\n ],\n [\n -78.64013671875,\n 40.06125658140474\n ],\n [\n -78.826904296875,\n 39.9434364619742\n ],\n [\n -78.848876953125,\n 39.80853604144591\n ],\n [\n -78.85986328125,\n 39.715638134796336\n ],\n [\n -78.99169921875,\n 39.69873414348139\n ],\n [\n -79.046630859375,\n 39.64799732373418\n ],\n [\n -79.266357421875,\n 39.436192999314095\n ],\n [\n -79.420166015625,\n 39.2832938689385\n ],\n [\n -79.354248046875,\n 39.26628442213066\n ],\n [\n -79.266357421875,\n 39.232253141714885\n ],\n [\n -79.2333984375,\n 39.155622393423215\n ],\n [\n -79.244384765625,\n 39.01918369029134\n ],\n [\n -79.27734374999999,\n 38.89103282648846\n ],\n [\n -79.398193359375,\n 38.74551518488265\n ],\n [\n -79.661865234375,\n 38.54816542304656\n ],\n [\n -79.683837890625,\n 38.47079371120379\n ],\n [\n -79.727783203125,\n 38.34165619279595\n ],\n [\n -79.815673828125,\n 38.20365531807149\n ],\n [\n -80.04638671875,\n 38.013476231041935\n ],\n [\n -80.17822265625,\n 37.779398571318765\n ],\n [\n -80.2880859375,\n 37.59682400108367\n ],\n [\n -80.4638671875,\n 37.47485808497102\n ],\n [\n -80.694580078125,\n 37.38761749978395\n ],\n [\n -80.771484375,\n 37.23032838760387\n ],\n [\n -80.57373046875,\n 37.26530995561875\n ],\n [\n -80.44189453125,\n 37.309014074275915\n ],\n [\n -80.255126953125,\n 37.31775185163688\n ],\n [\n -80.013427734375,\n 37.3002752813443\n ],\n [\n -79.8486328125,\n 37.23907530202184\n ],\n [\n -79.771728515625,\n 37.18657859524883\n ],\n [\n -79.6728515625,\n 37.07271048132943\n ],\n [\n -79.541015625,\n 37.09900294387622\n ],\n [\n -79.354248046875,\n 37.142803443716836\n ],\n [\n -79.1455078125,\n 37.10776507118514\n ],\n [\n -79.112548828125,\n 37.055177106660814\n ],\n [\n -78.936767578125,\n 36.932330061503144\n ],\n [\n -78.837890625,\n 36.94111143010769\n ],\n [\n -78.662109375,\n 37.055177106660814\n ],\n [\n -78.486328125,\n 37.03763967977139\n ],\n [\n -78.42041015625,\n 36.94111143010769\n ],\n [\n -78.20068359374999,\n 36.96744946416934\n ],\n [\n -77.904052734375,\n 37.03763967977139\n ],\n [\n -77.750244140625,\n 37.081475648860525\n ],\n [\n -77.53051757812499,\n 37.081475648860525\n ],\n [\n -77.354736328125,\n 37.07271048132943\n ],\n [\n -77.069091796875,\n 37.081475648860525\n ],\n [\n -76.959228515625,\n 37.01132594307015\n ],\n [\n -76.893310546875,\n 36.932330061503144\n ],\n [\n -76.871337890625,\n 36.83566824724438\n ],\n [\n -76.849365234375,\n 36.677230602346214\n ],\n [\n -76.7724609375,\n 36.527294814546245\n ],\n [\n -76.629638671875,\n 36.55377524336089\n ],\n [\n -76.46484375,\n 36.589068371399115\n ],\n [\n -76.35498046875,\n 36.48314061639213\n ],\n [\n -76.256103515625,\n 36.57142382346277\n ],\n [\n -76.190185546875,\n 36.66841891894786\n ],\n [\n -76.0693359375,\n 36.65079252503471\n ],\n [\n -75.9375,\n 36.66841891894786\n ],\n [\n -75.948486328125,\n 36.76529191711624\n ],\n [\n -75.904541015625,\n 37.01132594307015\n ],\n [\n -75.926513671875,\n 37.17782559332976\n ],\n [\n -75.882568359375,\n 37.42252593456307\n ],\n [\n -75.618896484375,\n 37.640334898059486\n ],\n [\n -75.509033203125,\n 37.82280243352756\n ],\n [\n -75.38818359375,\n 38.013476231041935\n ],\n [\n -75.16845703124999,\n 38.272688535980976\n ],\n [\n -75.1904296875,\n 38.41916639395372\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berger, Karl","contributorId":333488,"corporation":false,"usgs":false,"family":"Berger","given":"Karl","email":"","affiliations":[{"id":79897,"text":"Metropolitan Washington Council of Governments","active":true,"usgs":false}],"preferred":false,"id":892892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Filippino, Katherine C.","contributorId":333489,"corporation":false,"usgs":false,"family":"Filippino","given":"Katherine","email":"","middleInitial":"C.","affiliations":[{"id":79898,"text":"Hampton Roads Planning District Commission","active":true,"usgs":false}],"preferred":false,"id":892893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goulet, Normand","contributorId":333490,"corporation":false,"usgs":false,"family":"Goulet","given":"Normand","email":"","affiliations":[{"id":79899,"text":"Norther Virginia Regional Planning District Commission","active":true,"usgs":false}],"preferred":false,"id":892895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lookenbill, Michael 0000-0001-5857-8276","orcid":"https://orcid.org/0000-0001-5857-8276","contributorId":236910,"corporation":false,"usgs":false,"family":"Lookenbill","given":"Michael","email":"","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":892896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moyer, Douglas L. 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":892898,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Porter, Aaron J. 0000-0002-0781-3309","orcid":"https://orcid.org/0000-0002-0781-3309","contributorId":239980,"corporation":false,"usgs":true,"family":"Porter","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892899,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shallenberger, James","contributorId":333491,"corporation":false,"usgs":false,"family":"Shallenberger","given":"James","email":"","affiliations":[{"id":79900,"text":"Susquehanna River Basin Commission","active":true,"usgs":false}],"preferred":false,"id":892900,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thomas, Bryant","contributorId":333492,"corporation":false,"usgs":false,"family":"Thomas","given":"Bryant","email":"","affiliations":[{"id":39875,"text":"Virginia Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":892901,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yactayo, Guido","contributorId":333493,"corporation":false,"usgs":false,"family":"Yactayo","given":"Guido","email":"","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":892902,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70255769,"text":"70255769 - 2024 - Contrasting demographic responses under future climate for two populations of a montane amphibian","interactions":[],"lastModifiedDate":"2024-07-03T12:04:54.242749","indexId":"70255769","displayToPublicDate":"2024-01-04T07:03:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting demographic responses under future climate for two populations of a montane amphibian","docAbstract":"For species with complex life histories, climate change can have contrasting effects for different life stages within locally adapted populations and may result in responses counter to general climate change predictions. Using data from two, 14-year demographic studies for a North American montane amphibian, Cascades frog (Rana cascadae), we quantified how aspects of current climate influenced annual survival of larvae and adult stages and modeled the stochastic population growth rate (λs) of each population for current (1980–2006) and future periods (2080s). Climate drivers of survival for the populations were similar for larvae (i.e., decreases in precipitation lead to pond drying and mortality), but diverged for terrestrial stages where decreases in winter length and summer precipitation had opposite effects. By the 2080s, we predict one population will be in sharp decline (λs = 0.90), while the other population will remain nearly stable (λs = 0.99) in the absence of other stressors, such as mortality due to disease. Our case study demonstrates a result counter to many climate envelope predictions in that stage-specific responses to local climate and hydrology result in a higher extinction risk for the more northern population.
The failure of a 144-m-high lava-dam waterfall on the Río Coca, Ecuador, in February 2020 initiated a catastrophic watershed reset—regressive erosion upstream and a massive sediment pulse downstream—as the river evolves towards a new equilibrium grade. The evolution of this river corridor after a sudden base-level fall embodies the “complex response” concepts long understood through laboratory experiments, numerical modelling and smaller-scale field studies, but that have not been observed in the field before on this scale. This paper presents geomorphic and geotechnical data to characterize the evolution of the Río Coca since 2020. In the three years after the lava-dam failure, the erosion front migrated almost 13 km upstream along the mainstem river and triggered secondary headcuts that began migrating up tributaries. Erosion of the mainstem and tributary valleys generated a sediment pulse estimated to be 277 million m3 and ~500 million tonnes (Mt) over three years, depositing sediment tens of meters thick over tens of kilometres downstream from the former waterfall. This sediment pulse is one of the largest in modern times, comparable to the annual sediment load of a major continent-draining river but with orders-of-magnitude greater sediment yield. Geomorphic adjustment of the Río Coca represents a highly unusual natural disaster threatening life, property, water quality, the regional economy, major infrastructure and energy security. However, this event also provides a rare opportunity to learn how a large autogenic watershed disturbance and recovery evolve, with important lessons for interpreting the sedimentary record of volcanic landscapes.
National parks and other protected areas are important for preserving landscapes and biodiversity worldwide. An essential component of the mission of the United States (U.S.) National Park Service (NPS) requires understanding and maintaining accurate inventories of species on protected lands. We describe a new, national-scale synthesis of amphibian species occurrence in the NPS system. Many park units have a list of amphibian species observed within their borders compiled from various sources and available publicly through the NPSpecies platform. However, many of the observations in NPSpecies remain unverified and the lists are often outdated. We updated the amphibian dataset for each park unit by collating old and new park-level records and had them verified by regional experts. The new dataset contains occurrence records for 292 of the 424 NPS units and includes updated taxonomy, international and state conservation rankings, hyperlinks to a supporting reference for each record, specific notes, and related fields which can be used to better understand and manage amphibian biodiversity within a single park or group of parks.
The eastern migratory population of monarch butterflies (Danaus plexippus) started declining as early as the mid-1970s and seemed to stop declining by the early 2000s; the population now (about 2022) persists at a much-reduced abundance. Stochastic variation in abundance, at levels typical of monarch butterflies and other insects, was assessed to determine whether this population is at heightened risk of quasi-extinction, a level of abundance below which recovery of the migratory behavior is uncertain. Using previously published Bayesian state-space modeling methods it was determined roughly equivalent risk of quasi-extinction as was reported in 2016 for the species (28.7 percent [1.9–81.0 credible interval] and 52.0 percent [3.2–97.7 credible interval] at the 10- and 20-year marks, respectively). Though highly uncertain, the risk is non-negligibly positive. Warning signal analysis indicates the current dynamic is dominated by stochastic variation, which seems to be heightening risk with the passage of time. Increasing breeding opportunities through restoration of milkweed in its northern breeding locations seems to be the most promising means of mitigating extinction risk for this species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231097","usgsCitation":"Thogmartin, W.E., 2024, Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22): U.S. Geological Survey Open-File Report 2023–1097, 10 p., https://doi.org/10.3133/ofr20231097.","productDescription":"Report: iii, 10 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152775","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":423797,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WRARO7","text":"USGS data release","linkHelpText":"Eastern migratory monarch butterfly population estimates and associated early warning signals (2006–22)"},{"id":423796,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1097/images/"},{"id":423798,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231097/full"},{"id":423795,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.XML"},{"id":423794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.pdf","text":"Report","size":"949 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1097"},{"id":423793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1097/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (> 100 km2) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.