Effective wildlife management requires robust information regarding population status, habitat requirements, and likely responses to changing resource conditions. Single-species management may inadequately conserve communities and result in undesired effects to non-target species. Thus, management can benefit from understanding habitat relationships for multiple species. Pinyon pine and juniper (Pinus spp. and Juniperus spp.) are expanding into sagebrush-dominated (Artemisia spp.) ecosystems within North America and mechanical removal of these trees is frequently conducted to restore sagebrush ecosystems and recover Greater Sage-grouse (Centrocercus urophasianus). However, pinyon-juniper removal effects on non-target species are poorly understood, and changing pinyon-juniper woodland dynamics, climate, and anthropogenic development may obscure conservation priorities. To better predict responses to changing resource conditions, evaluate non-target effects of pinyon-juniper removal, prioritize species for conservation, and inform species recovery within pinyon-juniper and sagebrush ecosystems, we modeled population trends and density-habitat relationships for four sagebrush-associated, four pinyon-juniper-associated, and three generalist songbird species with respect to these ecosystems. We fit hierarchical population models to point count data collected throughout the western United States from 2008 to 2020. We found regional population changes for 10 of 11 species investigated; 6 of which increased in the highest elevation region of our study. Our models indicate pinyon-juniper removal will benefit Brewer's Sparrow (Spizella breweri), Green-tailed Towhee (Pipilo chlorurus), and Sage Thrasher (Oreoscoptes montanus) densities. Conversely, we predict largest negative effects of pinyon-juniper removal for species occupying early successional pinyon-juniper woodlands: Bewick's Wren (Thryomanes bewickii), Black-throated Gray Warblers (Setophaga nigrescens), Gray Flycatcher (Empidonax wrightii), and Juniper Titmouse (Baeolophus ridgwayi). Our results highlight the importance of considering effects to non-target species before implementing large-scale habitat manipulations. Our modeling framework can help prioritize species and regions for conservation action, infer effects of management interventions and a changing environment on wildlife, and help land managers balance habitat requirements across ecosystems.
This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235088","collaboration":"Prepared in cooperation with Department of Fisheries and Wildlife, Michigan State University","programNote":"Science Analytics and Synthesis Program","usgsCitation":"Yu, H., Cooper, A.R., Ross, J., McKerrow, A., Wieferich, D.J., and Infante, D.M., 2023, Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation: U.S. Geological Survey Scientific Investigations Report 2023–5088, 41 p., https://doi.org/10.3133/sir20235088.","productDescription":"Report: vii, 41 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132262","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":499547,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1UV25FW","text":"USGS data release","linkHelpText":"Fluvial Fish Native Distributions for the Conterminous United States using the NHDPlusV2.1 and Boosted Regression Tree (BRT) Models (ver. 2.0, December 2024)"},{"id":422490,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94XM9XV","text":"USGS data release","linkHelpText":"Aquatic Gap Analysis Project (Aquatic GAP) Aquatic Species Distribution Modeling on the National Hydrography Dataset Plus Version 2.1"},{"id":422787,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235088/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5088"},{"id":422540,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5088/sir20235088.xml"},{"id":422539,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5088/images"},{"id":422489,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5088/sir20235088.pdf","text":"Report","size":"26.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5088"},{"id":422488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5088/coverthb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.
","language":"English","publisher":"MDPI","doi":"10.3390/su152215887","usgsCitation":"Mekonen, S.S., Boyce, S.E., Mohammed, A.K., Flint, L.E., Flint, A., and Disse, M., 2023, Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley: Sustainability, v. 15, no. 22, 15887, 25 p., https://doi.org/10.3390/su152215887.","productDescription":"15887, 25 p.","ipdsId":"IP-146940","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su152215887","text":"Publisher Index Page"},{"id":426968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Kobo Valley, Riya Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.36,\n 12.88\n ],\n [\n 39.36,\n 11.92\n ],\n [\n 39.84,\n 11.92\n ],\n [\n 39.84,\n 12.88\n ],\n [\n 39.36,\n 12.88\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonen, Sisay Simachew","contributorId":333048,"corporation":false,"usgs":false,"family":"Mekonen","given":"Sisay","email":"","middleInitial":"Simachew","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, Abdella K.","contributorId":333049,"corporation":false,"usgs":false,"family":"Mohammed","given":"Abdella","email":"","middleInitial":"K.","affiliations":[{"id":79718,"text":"Hydraulic and Water Resources Engineering, Arba Minch University","active":true,"usgs":false}],"preferred":false,"id":897194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":897195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L 0000-0002-5118-751X","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":239656,"corporation":false,"usgs":false,"family":"Flint","given":"Alan L","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":897196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Disse, Markus","contributorId":333050,"corporation":false,"usgs":false,"family":"Disse","given":"Markus","email":"","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250769,"text":"70250769 - 2023 - Carbon dioxide (CO2) gas and eDNA monitoring as tools for eradicating invasive fish from anchialine pools in Hawai‘i","interactions":[],"lastModifiedDate":"2024-01-03T12:47:16.405249","indexId":"70250769","displayToPublicDate":"2023-11-13T06:45:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Carbon dioxide (CO2) gas and eDNA monitoring as tools for eradicating invasive fish from anchialine pools in Hawai‘i","docAbstract":"Invasive fish can profoundly affect communities they invade. In Hawai‘i, invasive fishes have become established in many anchialine pools, threatening the persistence of resident invertebrates, including several endangered species. Tools to eradicate invasive fishes from these pools are lacking. This study tested the efficacy of carbon dioxide (CO2) gas diffused into anchialine pool water as a method to eradicate invasive Mozambique tilapia (Oreochromis mossambicus), guppies (Poecilia reticulata), and western mosquitofish (Gambusia affinis). We first conducted aquarium trials to identify how these fishes were affected by elevated CO2 and the concomitant reduction in pH. We then carried out field trials in pools containing these fish in one pool each at two national historical parks on the Island of Hawai‘i during July 2021–January 2022. We also developed environmental DNA (eDNA) protocols to detect fish that may have survived CO2 treatments. The effect of CO2 on fish behavior varied among species; at pH 5.3 (CO2 = 255 mg/L) for tilapia and 5.0 (CO2 = 488 mg/L) for tilapia, guppies, and mosquitofish, all generally lost their ability to swim, showed slow or no gill movement, and altered their position in the water column. No tilapia survived the trials (n = 4 and 6 individuals at pH 5.3 and 5.0, respectively). In contrast, 41.7% (n = 12) of adult guppies and 66.7% (n = 12) of adult mosquitofish survived treatment at pH 5.0. In the field we were unable to reduce anchialine pool water pH below 5.7. Regardless, we were able to eradicate tilapia from one pool over four sequential treatments. Post-treatment eDNA assessments supported visual surveys, confirming our results. We were not able to eradicate guppies and mosquitofish. Results from this study show that CO2 can be an effective tool for eradicating invasive tilapia from anchialine pools, and post-treatment eDNA assessments can provide managers with a method for evaluating the success of eradication efforts.","language":"English","publisher":"Reabic","doi":"10.3391/mbi.2023.14.4.11","usgsCitation":"Peck, R., Munnstermann, M., Hayes, M., Atkinson, C., Beavers, S., Cupp, A.R., and Banko, P.C., 2023, Carbon dioxide (CO2) gas and eDNA monitoring as tools for eradicating invasive fish from anchialine pools in Hawai‘i: Management of Biological Invasions, v. 14, no. 4, p. 749-774, https://doi.org/10.3391/mbi.2023.14.4.11.","productDescription":"26 p.","startPage":"749","endPage":"774","ipdsId":"IP-155507","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":441610,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.4.11","text":"Publisher Index Page"},{"id":435124,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AEQWX0","text":"USGS data release","linkHelpText":"Puuhonua o Honaunau and Kaloko-Honokohau National Historical Parks, carbon dioxide treatment and qPCR eDNA assays for eradicating and monitoring invasive fish in anchialine pools, 2019-2022 (ver. 2.0, July 2023)"},{"id":424062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-155.778234,20.245743],[-155.772734,20.245409],[-155.746893,20.232325],[-155.737004,20.222773],[-155.735822,20.212417],[-155.732704,20.205392],[-155.653966,20.16736],[-155.630382,20.146916],[-155.624565,20.145911],[-155.607797,20.137987],[-155.600909,20.126573],[-155.598033,20.124539],[-155.590923,20.122497],[-155.58168,20.123617],[-155.568368,20.130545],[-155.558933,20.13157],[-155.523661,20.120028],[-155.516795,20.11523],[-155.502561,20.114155],[-155.468211,20.104296],[-155.443957,20.095318],[-155.405459,20.078772],[-155.4024,20.075541],[-155.387578,20.067119],[-155.33021,20.038517],[-155.29548,20.024438],[-155.282629,20.021969],[-155.270316,20.014525],[-155.240933,19.990173],[-155.204486,19.969438],[-155.194593,19.958368],[-155.179939,19.949372],[-155.149215,19.922872],[-155.144394,19.920523],[-155.131235,19.906801],[-155.124618,19.897288],[-155.12175,19.886099],[-155.107541,19.872467],[-155.098716,19.867811],[-155.095032,19.867882],[-155.086341,19.855399],[-155.084357,19.849736],[-155.085674,19.838584],[-155.088979,19.826656],[-155.094414,19.81491],[-155.09207,19.799409],[-155.091216,19.776368],[-155.093517,19.771832],[-155.093387,19.737751],[-155.087118,19.728013],[-155.079426,19.726193],[-155.063972,19.728917],[-155.045382,19.739824],[-155.006423,19.739286],[-154.997278,19.72858],[-154.987168,19.708524],[-154.981102,19.690687],[-154.984718,19.672161],[-154.983778,19.641647],[-154.974342,19.633201],[-154.963933,19.627605],[-154.950359,19.626461],[-154.947874,19.62425],[-154.947718,19.621947],[-154.951014,19.613614],[-154.947106,19.604856],[-154.93394,19.597505],[-154.928205,19.592702],[-154.924422,19.586553],[-154.903542,19.570622],[-154.875,19.556797],[-154.852618,19.549172],[-154.837384,19.538354],[-154.826732,19.537626],[-154.814417,19.53009],[-154.809561,19.522377],[-154.809379,19.519086],[-154.822968,19.48129],[-154.838545,19.463642],[-154.86854,19.438126],[-154.887817,19.426425],[-154.928772,19.397646],[-154.944185,19.381852],[-154.964619,19.365646],[-154.980861,19.349291],[-155.020537,19.331317],[-155.061729,19.316636],[-155.113272,19.290613],[-155.1337,19.276099],[-155.159635,19.268375],[-155.172413,19.26906],[-155.187427,19.266156],[-155.19626,19.261295],[-155.205892,19.260907],[-155.243961,19.271313],[-155.264619,19.274213],[-155.296761,19.266289],[-155.303808,19.261835],[-155.31337,19.250698],[-155.341268,19.234039],[-155.349148,19.217756],[-155.360631,19.20893],[-155.378638,19.202435],[-155.390701,19.201171],[-155.417369,19.187858],[-155.427093,19.179546],[-155.432519,19.170623],[-155.453516,19.151952],[-155.465663,19.146964],[-155.505281,19.137908],[-155.51474,19.132501],[-155.51214,19.128174],[-155.512137,19.124296],[-155.519652,19.117025],[-155.526136,19.115889],[-155.528902,19.11371],[-155.544806,19.091059],[-155.551129,19.08878],[-155.557817,19.08213],[-155.555326,19.069377],[-155.555177,19.053932],[-155.557371,19.046565],[-155.566446,19.032531],[-155.576599,19.027412],[-155.581903,19.02224],[-155.596032,18.998833],[-155.596521,18.980654],[-155.601866,18.971572],[-155.613966,18.970399],[-155.625256,18.961951],[-155.625,18.959934],[-155.638054,18.941723],[-155.658486,18.924835],[-155.672005,18.917466],[-155.681825,18.918694],[-155.687716,18.923358],[-155.690171,18.932195],[-155.693117,18.940542],[-155.726043,18.969437],[-155.763598,18.981837],[-155.806109,19.013967],[-155.853943,19.023762],[-155.88155,19.036644],[-155.884077,19.039266],[-155.886278,19.05576],[-155.903693,19.080777],[-155.908355,19.081138],[-155.921389,19.121183],[-155.917292,19.155963],[-155.903339,19.217792],[-155.90491,19.230147],[-155.902565,19.258427],[-155.895435,19.274639],[-155.890842,19.298905],[-155.887356,19.337101],[-155.888701,19.348031],[-155.898792,19.377984],[-155.913849,19.401107],[-155.909087,19.415455],[-155.921707,19.43055],[-155.924269,19.438794],[-155.925166,19.468081],[-155.922609,19.478611],[-155.924124,19.481406],[-155.930523,19.484921],[-155.935641,19.485628],[-155.936403,19.481905],[-155.939145,19.481577],[-155.95149,19.486649],[-155.952897,19.488805],[-155.953663,19.510003],[-155.960457,19.546612],[-155.962264,19.551779],[-155.965211,19.554745],[-155.96935,19.555963],[-155.970969,19.586328],[-155.978206,19.608159],[-155.997728,19.642816],[-156.028982,19.650098],[-156.032928,19.653905],[-156.034994,19.65936],[-156.033326,19.66923],[-156.027427,19.672154],[-156.029281,19.678908],[-156.036079,19.690252],[-156.04796,19.698938],[-156.051652,19.703649],[-156.052485,19.718667],[-156.064364,19.730766],[-156.05722,19.742536],[-156.052315,19.756836],[-156.049651,19.780452],[-156.021732,19.8022],[-156.006267,19.81758],[-155.982821,19.845651],[-155.976651,19.85053],[-155.964817,19.855183],[-155.949251,19.857034],[-155.945297,19.853443],[-155.940311,19.852305],[-155.925843,19.858928],[-155.926938,19.870221],[-155.92549,19.875],[-155.915662,19.887126],[-155.901987,19.912081],[-155.894099,19.923135],[-155.894474,19.926927],[-155.892533,19.932162],[-155.866919,19.954172],[-155.856588,19.968885],[-155.840708,19.976952],[-155.838692,19.975527],[-155.835312,19.976078],[-155.831948,19.982775],[-155.828965,19.995542],[-155.825473,20.025944],[-155.828182,20.035424],[-155.850385,20.062506],[-155.866931,20.078652],[-155.88419,20.10675],[-155.899149,20.145728],[-155.906035,20.205157],[-155.901452,20.235787],[-155.890663,20.25524],[-155.882631,20.263026],[-155.873921,20.267744],[-155.853293,20.271548],[-155.811459,20.26032],[-155.783242,20.246395],[-155.778234,20.245743]]],[[[-157.789581,21.438396],[-157.789734,21.437679],[-157.789276,21.435833],[-157.790543,21.434313],[-157.791718,21.434881],[-157.793045,21.43391],[-157.793167,21.43574],[-157.791565,21.43651],[-157.791779,21.437752],[-157.793289,21.437658],[-157.791779,21.438435],[-157.791092,21.438442],[-157.790741,21.43874],[-157.789581,21.438396]]],[[[-160.125,21.95909],[-160.122262,21.962881],[-160.112746,21.995245],[-160.09645,22.001489],[-160.072123,22.003334],[-160.058543,21.99638],[-160.051992,21.983681],[-160.052729,21.980321],[-160.056336,21.977939],[-160.060549,21.976729],[-160.063349,21.978354],[-160.065811,21.976562],[-160.078393,21.955153],[-160.085787,21.927295],[-160.080012,21.910808],[-160.079065,21.89608],[-160.098897,21.884711],[-160.124283,21.876789],[-160.147609,21.872814],[-160.16162,21.864746],[-160.174796,21.846923],[-160.189782,21.82245],[-160.205211,21.789053],[-160.200427,21.786479],[-160.205851,21.779518],[-160.218044,21.783755],[-160.23478,21.795418],[-160.24961,21.815145],[-160.244943,21.848943],[-160.231028,21.886263],[-160.228965,21.889117],[-160.21383,21.899193],[-160.205528,21.907507],[-160.202716,21.912422],[-160.190158,21.923592],[-160.167471,21.932863],[-160.13705,21.948632],[-160.127302,21.955508],[-160.125,21.95909]]],[[[-159.431707,22.220015],[-159.40732,22.230555],[-159.388119,22.223252],[-159.385977,22.220009],[-159.367563,22.214906],[-159.359842,22.214831],[-159.357227,22.217744],[-159.353795,22.217669],[-159.339964,22.208519],[-159.315613,22.186817],[-159.308855,22.155555],[-159.297808,22.149748],[-159.295875,22.144547],[-159.295271,22.13039],[-159.297143,22.113815],[-159.317451,22.080944],[-159.321667,22.063411],[-159.324775,22.05867],[-159.333267,22.054639],[-159.337996,22.046575],[-159.341401,22.028978],[-159.333224,21.973005],[-159.333109,21.964176],[-159.334714,21.961099],[-159.350828,21.950817],[-159.356613,21.939546],[-159.382349,21.924479],[-159.408284,21.897781],[-159.425862,21.884527],[-159.446599,21.871647],[-159.471962,21.88292],[-159.490914,21.888898],[-159.517973,21.890996],[-159.555415,21.891355],[-159.574991,21.896585],[-159.577784,21.900486],[-159.584272,21.899038],[-159.610241,21.898356],[-159.637849,21.917166],[-159.648132,21.93297],[-159.671872,21.957038],[-159.681493,21.960054],[-159.705255,21.963427],[-159.72014,21.970789],[-159.758218,21.980694],[-159.765735,21.986593],[-159.788139,22.018411],[-159.790932,22.031177],[-159.786543,22.06369],[-159.780096,22.072567],[-159.748159,22.100388],[-159.741223,22.115666],[-159.733457,22.142756],[-159.726043,22.152171],[-159.699978,22.165252],[-159.66984,22.170782],[-159.608794,22.207878],[-159.591596,22.219456],[-159.583965,22.22668],[-159.559643,22.229185],[-159.554166,22.228212],[-159.548594,22.226263],[-159.54115,22.216764],[-159.534594,22.219403],[-159.523769,22.217602],[-159.51941,22.215646],[-159.518348,22.211182],[-159.515574,22.208008],[-159.507811,22.205987],[-159.501055,22.211064],[-159.500821,22.225538],[-159.488558,22.23317],[-159.480158,22.232715],[-159.467007,22.226529],[-159.45619,22.228811],[-159.441809,22.226321],[-159.431707,22.220015]]],[[[-157.014553,21.185503],[-156.999108,21.182221],[-156.991318,21.18551],[-156.987768,21.18935],[-156.982343,21.207798],[-156.984464,21.210063],[-156.984032,21.212198],[-156.974002,21.218503],[-156.969064,21.217018],[-156.962847,21.212131],[-156.951654,21.191662],[-156.950808,21.182636],[-156.946159,21.175963],[-156.918248,21.168279],[-156.903466,21.16421],[-156.898174,21.16594],[-156.89613,21.169561],[-156.896537,21.172208],[-156.867944,21.16452],[-156.841592,21.167926],[-156.821944,21.174693],[-156.771495,21.180053],[-156.742231,21.176214],[-156.738341,21.17202],[-156.736648,21.16188],[-156.719386,21.163911],[-156.712696,21.161547],[-156.714158,21.152238],[-156.726033,21.13236],[-156.748932,21.1086],[-156.775995,21.089751],[-156.790815,21.081686],[-156.794136,21.075796],[-156.835351,21.06336],[-156.865795,21.057801],[-156.877137,21.0493],[-156.891946,21.051831],[-156.89517,21.055771],[-156.953719,21.067761],[-157.00295,21.083282],[-157.02617,21.089015],[-157.032045,21.091094],[-157.037667,21.097864],[-157.079696,21.105835],[-157.095373,21.10636],[-157.125,21.1026],[-157.143483,21.096632],[-157.254061,21.090601],[-157.298054,21.096917],[-157.313343,21.105755],[-157.299187,21.132488],[-157.299471,21.135972],[-157.293774,21.146127],[-157.284346,21.157755],[-157.276474,21.163175],[-157.274504,21.162762],[-157.259911,21.174875],[-157.254709,21.181376],[-157.251007,21.190952],[-157.25026,21.207739],[-157.256935,21.215665],[-157.261457,21.217661],[-157.263163,21.220873],[-157.26069,21.225684],[-157.257085,21.227268],[-157.241534,21.220969],[-157.226445,21.220185],[-157.212082,21.221848],[-157.202125,21.219298],[-157.192439,21.207644],[-157.185553,21.205602],[-157.157103,21.200706],[-157.148125,21.200745],[-157.144627,21.202555],[-157.128207,21.201488],[-157.113438,21.197375],[-157.097971,21.198012],[-157.064264,21.189076],[-157.053053,21.188754],[-157.047757,21.190739],[-157.039987,21.190909],[-157.014553,21.185503]]],[[[-156.544169,20.522802],[-156.550016,20.520273],[-156.559994,20.521892],[-156.586238,20.511711],[-156.603844,20.524372],[-156.631143,20.514943],[-156.642347,20.508285],[-156.647464,20.512017],[-156.668809,20.504738],[-156.682939,20.506775],[-156.703673,20.527237],[-156.702265,20.532451],[-156.696662,20.541646],[-156.6801,20.557021],[-156.651567,20.565574],[-156.614598,20.587109],[-156.610734,20.59377],[-156.576871,20.60657],[-156.56714,20.604895],[-156.553604,20.594729],[-156.543034,20.580115],[-156.542808,20.573674],[-156.548909,20.56859],[-156.556021,20.542657],[-156.553018,20.539382],[-156.540189,20.534741],[-156.539643,20.527644],[-156.544169,20.522802]]],[[[-156.612012,21.02477],[-156.612065,21.027273],[-156.606238,21.034371],[-156.592256,21.03288],[-156.580448,21.020172],[-156.562773,21.016167],[-156.549813,21.004939],[-156.546291,21.005082],[-156.528246,20.967757],[-156.518707,20.954662],[-156.512226,20.95128],[-156.510391,20.940358],[-156.507913,20.937886],[-156.49948,20.934577],[-156.495883,20.928005],[-156.493263,20.916011],[-156.481055,20.898199],[-156.474796,20.894546],[-156.422668,20.911631],[-156.386045,20.919563],[-156.374297,20.927616],[-156.370729,20.932669],[-156.352649,20.941414],[-156.345655,20.941596],[-156.342365,20.938737],[-156.332817,20.94645],[-156.324578,20.950184],[-156.307198,20.942739],[-156.286332,20.947701],[-156.275116,20.937361],[-156.263107,20.940888],[-156.242555,20.937838],[-156.230159,20.931936],[-156.230089,20.917864],[-156.226757,20.916677],[-156.222062,20.918309],[-156.217953,20.916573],[-156.216341,20.907035],[-156.173103,20.876926],[-156.170458,20.874605],[-156.166746,20.865646],[-156.132669,20.861369],[-156.129381,20.847513],[-156.115735,20.827301],[-156.100123,20.828502],[-156.090291,20.831872],[-156.059788,20.81054],[-156.033287,20.808246],[-156.003532,20.795545],[-156.002947,20.789418],[-155.987944,20.776552],[-155.984587,20.767496],[-155.986851,20.758577],[-155.985413,20.744245],[-155.987216,20.722717],[-155.991534,20.713654],[-156.00187,20.698064],[-156.01415,20.685681],[-156.020044,20.686857],[-156.030702,20.682452],[-156.040341,20.672719],[-156.043786,20.664902],[-156.053385,20.65432],[-156.059753,20.652044],[-156.081472,20.654387],[-156.089365,20.648519],[-156.120985,20.633685],[-156.129898,20.627523],[-156.142665,20.623605],[-156.144588,20.624032],[-156.148085,20.629067],[-156.156772,20.629639],[-156.169732,20.627358],[-156.173393,20.6241],[-156.184556,20.629719],[-156.192938,20.631769],[-156.210258,20.628518],[-156.225338,20.62294],[-156.236145,20.61595],[-156.265921,20.601629],[-156.284391,20.596488],[-156.288037,20.59203],[-156.293454,20.588783],[-156.302692,20.586199],[-156.322944,20.588273],[-156.351716,20.58697],[-156.359634,20.581977],[-156.370725,20.57876],[-156.377633,20.578427],[-156.415313,20.586099],[-156.417523,20.589728],[-156.415746,20.594044],[-156.417799,20.598682],[-156.423141,20.602079],[-156.427708,20.598873],[-156.431872,20.598143],[-156.438385,20.601337],[-156.444242,20.607941],[-156.442884,20.613842],[-156.450651,20.642212],[-156.445894,20.64927],[-156.443673,20.656018],[-156.448656,20.704739],[-156.451038,20.725469],[-156.452895,20.731287],[-156.458438,20.736676],[-156.462242,20.753952],[-156.462058,20.772571],[-156.464043,20.781667],[-156.473562,20.790756],[-156.489496,20.798339],[-156.501688,20.799933],[-156.506026,20.799463],[-156.515994,20.794234],[-156.525215,20.780821],[-156.537752,20.778408],[-156.631794,20.82124],[-156.678634,20.870541],[-156.688969,20.888673],[-156.687804,20.89072],[-156.688132,20.906325],[-156.691334,20.91244],[-156.697418,20.916368],[-156.69989,20.920629],[-156.69411,20.952708],[-156.680905,20.980262],[-156.665514,21.007054],[-156.652419,21.008994],[-156.645966,21.014416],[-156.642592,21.019936],[-156.644167,21.022312],[-156.642809,21.027583],[-156.619581,21.027793],[-156.612012,21.02477]]],[[[-157.010001,20.929757],[-156.989813,20.932127],[-156.971604,20.926254],[-156.937529,20.925274],[-156.91845,20.922546],[-156.897169,20.915395],[-156.837047,20.863575],[-156.825237,20.850731],[-156.809576,20.826036],[-156.808469,20.820396],[-156.809463,20.809169],[-156.817427,20.794606],[-156.838321,20.764575],[-156.846413,20.760201],[-156.851481,20.760069],[-156.869753,20.754701],[-156.890295,20.744855],[-156.909081,20.739533],[-156.949009,20.738997],[-156.96789,20.73508],[-156.984747,20.756677],[-156.994001,20.786671],[-156.988933,20.815496],[-156.991834,20.826603],[-157.006243,20.849603],[-157.010911,20.854476],[-157.054552,20.877219],[-157.059663,20.884634],[-157.061128,20.890635],[-157.062511,20.904385],[-157.05913,20.913407],[-157.035789,20.927078],[-157.025626,20.929528],[-157.010001,20.929757]]],[[[-158.044485,21.306011],[-158.0883,21.2988],[-158.1033,21.2979],[-158.1127,21.3019],[-158.1211,21.3169],[-158.1225,21.3224],[-158.111949,21.326622],[-158.114196,21.331123],[-158.119427,21.334594],[-158.125459,21.330264],[-158.13324,21.359207],[-158.1403,21.3738],[-158.149719,21.385208],[-158.161743,21.396282],[-158.1792,21.4043],[-158.181274,21.409626],[-158.181,21.420868],[-158.182648,21.430073],[-158.192352,21.44804],[-158.205383,21.459793],[-158.219446,21.46978],[-158.233,21.4876],[-158.231171,21.523857],[-158.23175,21.533035],[-158.234314,21.540058],[-158.250671,21.557373],[-158.27951,21.575794],[-158.277679,21.578789],[-158.254425,21.582684],[-158.190704,21.585892],[-158.17,21.5823],[-158.12561,21.586739],[-158.10672,21.596577],[-158.106689,21.603024],[-158.1095,21.6057],[-158.108185,21.607487],[-158.079895,21.628101],[-158.0668,21.6437],[-158.066711,21.65234],[-158.0639,21.6584],[-158.0372,21.6843],[-158.018127,21.699955],[-157.9923,21.708],[-157.98703,21.712494],[-157.968628,21.712704],[-157.947174,21.689568],[-157.939,21.669],[-157.9301,21.6552],[-157.924591,21.651183],[-157.9228,21.6361],[-157.9238,21.6293],[-157.910797,21.611183],[-157.900574,21.605885],[-157.87735,21.575277],[-157.878601,21.560181],[-157.872528,21.557568],[-157.8669,21.5637],[-157.85614,21.560661],[-157.85257,21.557514],[-157.836945,21.529945],[-157.837372,21.512085],[-157.849579,21.509598],[-157.852625,21.499971],[-157.84549,21.466747],[-157.84099,21.459483],[-157.82489,21.455379],[-157.8163,21.4502],[-157.8139,21.4403],[-157.8059,21.4301],[-157.786513,21.415633],[-157.779846,21.417309],[-157.774455,21.421352],[-157.772209,21.431236],[-157.774905,21.453698],[-157.772209,21.457741],[-157.764572,21.461335],[-157.754239,21.461335],[-157.737617,21.459089],[-157.731777,21.455944],[-157.731328,21.444713],[-157.73582,21.438424],[-157.740762,21.424048],[-157.741211,21.414614],[-157.7386,21.4043],[-157.730191,21.401871],[-157.728221,21.402104],[-157.726421,21.402845],[-157.724324,21.403311],[-157.723794,21.40329],[-157.723286,21.403227],[-157.722735,21.403121],[-157.722544,21.403036],[-157.721845,21.401596],[-157.721083,21.399541],[-157.7189,21.3961],[-157.7089,21.3833],[-157.7087,21.3793],[-157.7126,21.3689],[-157.7106,21.3585],[-157.7088,21.3534],[-157.6971,21.3364],[-157.6938,21.3329],[-157.6619,21.3131],[-157.6518,21.3139],[-157.652629,21.308709],[-157.6537,21.302],[-157.6946,21.2739],[-157.6944,21.2665],[-157.7001,21.264],[-157.7097,21.2621],[-157.7139,21.2638],[-157.7142,21.2665],[-157.7114,21.272],[-157.7122,21.2814],[-157.7143,21.2845],[-157.7213,21.2869],[-157.7572,21.278],[-157.765,21.2789],[-157.7782,21.2735],[-157.7931,21.2604],[-157.8096,21.2577],[-157.8211,21.2606],[-157.8241,21.2646],[-157.8253,21.2714],[-157.8319,21.2795],[-157.8457,21.29],[-157.89,21.3065],[-157.894518,21.319632],[-157.898969,21.327391],[-157.90482,21.329172],[-157.918939,21.318615],[-157.917921,21.313781],[-157.913469,21.310983],[-157.910925,21.305768],[-157.952263,21.306531],[-157.950736,21.312509],[-157.951881,21.318742],[-157.967971,21.327986],[-157.973334,21.327426],[-157.989424,21.317984],[-158.0245,21.3093],[-158.044485,21.306011]]]]},\"properties\":{\"name\":\"Hawaii\",\"nation\":\"USA \"}}]}","volume":"14","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":891340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munnstermann, Maya","contributorId":332917,"corporation":false,"usgs":false,"family":"Munnstermann","given":"Maya","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":891341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Malia","contributorId":332918,"corporation":false,"usgs":false,"family":"Hayes","given":"Malia","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":891342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atkinson, Carter T. 0000-0002-4232-5335","orcid":"https://orcid.org/0000-0002-4232-5335","contributorId":302619,"corporation":false,"usgs":true,"family":"Atkinson","given":"Carter T.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":891343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beavers, Sallie","contributorId":332919,"corporation":false,"usgs":false,"family":"Beavers","given":"Sallie","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":891344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":891345,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":891346,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250652,"text":"70250652 - 2023 - Time-dependent weakening of granite at hydrothermal conditions","interactions":[],"lastModifiedDate":"2023-12-22T12:50:40.17831","indexId":"70250652","displayToPublicDate":"2023-11-13T06:43:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Time-dependent weakening of granite at hydrothermal conditions","docAbstract":"The evolution of a fault's frictional strength during the interseismic period is a critical component of the earthquake cycle, yet there have been relatively few studies that examine the time-dependent evolution of strength at conditions representative of seismogenic depths. Using a simulated fault in Westerly granite, we examined how frictional strength evolves under hydrothermal conditions up to 250°C during slide-hold-slide experiments. At temperatures ≤100°C, frictional strength generally increases with hold duration but, at 200 and 250°C, an initial increase in strength transitions to rapid time-dependent weakening for holds longer than 14 hr. Forward modeling of long hold periods at 250°C using the rate and state friction constitutive equations requires a second, strongly negative, state variable with a long evolution distance. This implies that significant hydrothermal alteration is occurring at 250°C, consistent with microstructural observations of dissolution and secondary mineral precipitation.
Differences in sediment biogeochemistry among tidal marsh features with different hydrological and geomorphological characteristics, including marsh interiors, marsh edges, first-order channels, and third-order channels, can result in spatial variation in MeHg production and availability. To better understand the link between MeHg production in sediments and bioaccumulation in primary and secondary consumer invertebrates and fish, we characterized mesoscale spatial variation in sediment biogeochemistry and MeHg concentrations of sediments, water, and consumer tissues among marsh features. Our results indicated that marsh interiors had biogeochemical conditions, including greater concentrations of organic matter and sulfate reduction rates, that resulted in greater MeHg concentrations in sediments and surface water particulates from marsh interiors compared to other features. Tissue MeHg concentrations of consumers also differed among features, with greater concentrations from marsh edges and interiors compared to channels. This spatial mismatch of MeHg concentrations in sediments and water compared to those in consumers may have resulted from differences in behavior and physiology among consumers that influenced the spatial scale over which MeHg was integrated into tissues. Our results highlight the importance of sampling across a suite of marsh features and considering the behavioral and physiological traits of sentinel taxa for contaminant monitoring studies.
Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.
Volcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade-long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state-of-the-art models of meteorology, emissions, and dispersion. The framework is open-source and introduces a new dynamic plume-rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12-day eruption period model performance varied between days with trade and non-trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JD039281","usgsCitation":"Moisseeva, N., Businger, S., and Elias, T., 2023, VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i: Journal of Geophysical Research Atmospheres, v. 128, no. 22, e2023JD039281, 14 p., https://doi.org/10.1029/2023JD039281.","productDescription":"e2023JD039281, 14 p.","ipdsId":"IP-153377","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jd039281","text":"Publisher Index Page"},{"id":463904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.7156069329796,\n 19.56532392435213\n ],\n [\n -155.7156069329796,\n 19.345990597059426\n ],\n [\n -155.48166856061957,\n 19.345990597059426\n ],\n [\n -155.48166856061957,\n 19.56532392435213\n ],\n [\n -155.7156069329796,\n 19.56532392435213\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moisseeva, Nadya 0000-0001-7317-1597","orcid":"https://orcid.org/0000-0001-7317-1597","contributorId":335180,"corporation":false,"usgs":false,"family":"Moisseeva","given":"Nadya","email":"","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":918411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Businger, Steven","contributorId":345757,"corporation":false,"usgs":false,"family":"Businger","given":"Steven","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":918412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918413,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263989,"text":"70263989 - 2023 - Four different surgical approaches to vasectomies in male Texas bobwhite quail (Colinus virginianus texanus) and northern bobwhite quail (Colinus virginianus)","interactions":[],"lastModifiedDate":"2025-03-04T15:23:24.262302","indexId":"70263989","displayToPublicDate":"2023-11-10T09:18:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2191,"text":"Journal of Avian Medicine and Surgery","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Four different surgical approaches to vasectomies in male Texas bobwhite quail (Colinus virginianus texanus) and northern bobwhite quail (Colinus virginianus)","title":"Four different surgical approaches to vasectomies in male Texas bobwhite quail (Colinus virginianus texanus) and northern bobwhite quail (Colinus virginianus)","docAbstract":"Vasectomies render a male sterile and have been used for various management purposes, including conservation efforts. This report evaluated 4 different surgical approaches (external approach, internal approach with dissection, internal approach with cautery, and internal approach caudally) to perform 177 vasectomies in Texas bobwhite (Colinus virginianus texanus; n = 171) and northern bobwhite quail (Colinus virginianus; n = 6) in a field setting. Birds were not randomized into groups for the different approaches. Survival was recorded in 83% (147/177) of the birds. The most common cause of death was hemorrhage from the common iliac vein due to damage during the surgical procedure. Other causes for death included transection of the ureter, parasitism, euthanasia, and undetermined causes. The approach that had the highest survival rate (89.8%, 132/147) was the internal approach with cautery, and based on these results the authors recommend this approach for vasectomies in Texas and northern bobwhite quail.
","language":"English","publisher":"Association of Avian Veterinarians","doi":"10.1647/2021-0132","usgsCitation":"Lamb, S., Olsen, G.H., and Pilny, A., 2023, Four different surgical approaches to vasectomies in male Texas bobwhite quail (Colinus virginianus texanus) and northern bobwhite quail (Colinus virginianus): Journal of Avian Medicine and Surgery, v. 37, no. 3, p. 226-234, https://doi.org/10.1647/2021-0132.","productDescription":"9 p.","startPage":"226","endPage":"234","ipdsId":"IP-146172","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":482796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, Stephanie K.","contributorId":351767,"corporation":false,"usgs":false,"family":"Lamb","given":"Stephanie K.","affiliations":[{"id":84037,"text":"Arizona Exotic Animal Hospital","active":true,"usgs":false}],"preferred":false,"id":929426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olsen, Glenn H. 0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":238130,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilny, Anthony","contributorId":351768,"corporation":false,"usgs":false,"family":"Pilny","given":"Anthony","affiliations":[{"id":84040,"text":"Arizona Exotics Animal Hospital","active":true,"usgs":false}],"preferred":false,"id":929428,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250390,"text":"70250390 - 2023 - A review of grass carp and related species literature on diet, behavior, toxicology, and physiology focused on informing development of controls for invasive grass carp populations in North America","interactions":[],"lastModifiedDate":"2023-12-06T13:22:40.49949","indexId":"70250390","displayToPublicDate":"2023-11-10T07:21:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"title":"A review of grass carp and related species literature on diet, behavior, toxicology, and physiology focused on informing development of controls for invasive grass carp populations in North America","docAbstract":"Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument’s pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over
Prions cause fatal neurodegenerative diseases and exhibit remarkable durability, which engenders a wide array of potential exposure scenarios. In chronic wasting disease of deer, elk, moose, and reindeer and in scrapie of sheep and goats, prions are transmitted via environmental routes and the ability of plants to accumulate and subsequently transmit prions has been hypothesized, but not previously demonstrated. Here, we establish the ability of several crop and other plant species to take up prions via their roots and translocate them to above-ground tissues from various growth media including soils. We demonstrate that plants can accumulate prions in above-ground tissues to levels sufficient to transmit disease after oral ingestion by mice. Our results suggest plants may serve as vectors for prion transmission in the environment—a finding with implications for wildlife conservation, agriculture, and public health.
Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability, especially at short time scales, in future wetland CH4 model developments.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG007259","usgsCitation":"Zhang, Z., Bansal, S., Chang, K., Fluet-Chouinard, E., Delwiche, K.B., Goeckede, M., Gustafson, A., Knox, S., Leppanen, A., Liu, L., Liu, J., Malhotra, A., Markkanen, T., McNicol, G., Melton, J.R., Miller, P.A., Peng, C., Raivonen, M., Riley, W., Sonnentag, O., Aalto, T., Vargas, R., Zhang, W., Zhu, Q., Zhu, Q., Zhuang, Q., Windham-Myers, L., Jackson, R.B., and Poulter, B., 2023, Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses: JGR Biogeosciences, v. 128, no. 11, e2022JG007259, 21 p., https://doi.org/10.1029/2022JG007259.","productDescription":"e2022JG007259, 21 p.","ipdsId":"IP-154074","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441629,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg007259","text":"Publisher Index Page"},{"id":423095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":883670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":883671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chang, Kuang-Yu 0000-0002-7859-5871","orcid":"https://orcid.org/0000-0002-7859-5871","contributorId":260439,"corporation":false,"usgs":false,"family":"Chang","given":"Kuang-Yu","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fluet-Chouinard, Etienne","contributorId":217392,"corporation":false,"usgs":false,"family":"Fluet-Chouinard","given":"Etienne","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":883673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Delwiche, Kyle B.","contributorId":139866,"corporation":false,"usgs":false,"family":"Delwiche","given":"Kyle","email":"","middleInitial":"B.","affiliations":[{"id":13299,"text":"Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA","active":true,"usgs":false}],"preferred":false,"id":883674,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goeckede, Mathias 0000-0003-2833-8401","orcid":"https://orcid.org/0000-0003-2833-8401","contributorId":217409,"corporation":false,"usgs":false,"family":"Goeckede","given":"Mathias","email":"","affiliations":[{"id":39621,"text":"Max Planck Institute for Biogeochemistry","active":true,"usgs":false}],"preferred":false,"id":883675,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gustafson, Adrian","contributorId":329953,"corporation":false,"usgs":false,"family":"Gustafson","given":"Adrian","email":"","affiliations":[{"id":78747,"text":"8Department of Physical Geography and Ecosystem Science, Lund University, Sweden","active":true,"usgs":false}],"preferred":false,"id":883676,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knox, Sara","contributorId":272638,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":883677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leppanen, Antii","contributorId":329954,"corporation":false,"usgs":false,"family":"Leppanen","given":"Antii","email":"","affiliations":[{"id":78748,"text":"10Finnish Meteorological Institute, Climate System Research Unit, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":883678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liu, Licheng","contributorId":297866,"corporation":false,"usgs":false,"family":"Liu","given":"Licheng","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883679,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":883680,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Malhotra, Avni 0000-0002-7850-6402","orcid":"https://orcid.org/0000-0002-7850-6402","contributorId":197909,"corporation":false,"usgs":false,"family":"Malhotra","given":"Avni","email":"","affiliations":[{"id":35065,"text":"Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883681,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Markkanen, Tiina","contributorId":329955,"corporation":false,"usgs":false,"family":"Markkanen","given":"Tiina","email":"","affiliations":[{"id":78748,"text":"10Finnish Meteorological Institute, Climate System Research Unit, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":883682,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McNicol, Gavin 0000-0002-6655-8045","orcid":"https://orcid.org/0000-0002-6655-8045","contributorId":260536,"corporation":false,"usgs":false,"family":"McNicol","given":"Gavin","email":"","affiliations":[],"preferred":false,"id":883683,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Melton, Joe R.","contributorId":329956,"corporation":false,"usgs":false,"family":"Melton","given":"Joe","email":"","middleInitial":"R.","affiliations":[{"id":78751,"text":"15Environment and Climate Change Canada, Climate Research Division, Victoria, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":883684,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Miller, Paul A","contributorId":329957,"corporation":false,"usgs":false,"family":"Miller","given":"Paul","email":"","middleInitial":"A","affiliations":[{"id":78747,"text":"8Department of Physical Geography and Ecosystem Science, Lund University, Sweden","active":true,"usgs":false}],"preferred":false,"id":883685,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Peng, Changhui","contributorId":197932,"corporation":false,"usgs":false,"family":"Peng","given":"Changhui","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":883686,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Raivonen, Maarit","contributorId":329958,"corporation":false,"usgs":false,"family":"Raivonen","given":"Maarit","email":"","affiliations":[{"id":78748,"text":"10Finnish Meteorological Institute, Climate System Research Unit, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":883687,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Riley, William","contributorId":222533,"corporation":false,"usgs":false,"family":"Riley","given":"William","affiliations":[],"preferred":false,"id":883688,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sonnentag, Oliver 0000-0001-9333-9721","orcid":"https://orcid.org/0000-0001-9333-9721","contributorId":225735,"corporation":false,"usgs":false,"family":"Sonnentag","given":"Oliver","email":"","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":883689,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Aalto, Tuula","contributorId":329959,"corporation":false,"usgs":false,"family":"Aalto","given":"Tuula","affiliations":[{"id":78748,"text":"10Finnish Meteorological Institute, Climate System Research Unit, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":883690,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Vargas, Rodrigo 0000-0001-6829-5333","orcid":"https://orcid.org/0000-0001-6829-5333","contributorId":224770,"corporation":false,"usgs":false,"family":"Vargas","given":"Rodrigo","email":"","affiliations":[{"id":39556,"text":"U. Delaware","active":true,"usgs":false}],"preferred":false,"id":883691,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Zhang, Wenxin","contributorId":167815,"corporation":false,"usgs":false,"family":"Zhang","given":"Wenxin","email":"","affiliations":[],"preferred":false,"id":883692,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Zhu, Qing","contributorId":260547,"corporation":false,"usgs":false,"family":"Zhu","given":"Qing","affiliations":[],"preferred":false,"id":883693,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":883695,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Zhuang, Qianlai","contributorId":207137,"corporation":false,"usgs":false,"family":"Zhuang","given":"Qianlai","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":883694,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":883696,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Jackson, Robert B. 0000-0001-8846-7147","orcid":"https://orcid.org/0000-0001-8846-7147","contributorId":34252,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert","email":"","middleInitial":"B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":883697,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Poulter, Benjamin","contributorId":298276,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":883698,"contributorType":{"id":1,"text":"Authors"},"rank":29}]}} ,{"id":70250005,"text":"70250005 - 2023 - Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska","interactions":[],"lastModifiedDate":"2023-11-12T13:40:42.228675","indexId":"70250005","displayToPublicDate":"2023-11-09T07:36:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska","docAbstract":"Our warming climate is adversely affecting cryospheric landscapes via glacial retreat, permafrost degradation, and associated slope destabilization. In Prince William Sound, Alaska, the rapid retreat of Barry Glacier has destabilized the slopes flanking the glacier, resulting in numerous landslides. The largest of these landslides (∼500 Mm3 in volume) is more than 2 km wide and has the potential to generate a tsunami that could affect nearby recreationists, marine traffic, infrastructure, natural and cultural resources, and the community of Whittier, located 60 km from the landslide. Here, we combine landslide structural and kinematic element mapping with data acquired from bi-yearly airborne lidar, multi-week satellite-based synthetic aperture radar (SAR), sub-hourly ground-based SAR, and seismic monitoring from 2020 to 2022 to characterize this landslide and examine its evolution. While some methods serve as a snapshot in time that is a culmination of events, others emphasize the ever-evolving nature of the landslide and associated hazards. Four major kinematic elements define the overall structure of the landslide, which vary in deformation type and rate, from creep (5 mm per day over several months) to episodic movement (2 m in 30 days) and landslide-wide to localized events. In some areas of the landslide, short-term deformation deviates from structures formed by cumulative movement, implying structural and kinematic evolution associated with glacier retreat. These insights are important for assessing landslide hazards and hazard evolution for large, slow-moving bedrock landslides in actively deglaciating environments.
Adaptive plasticity in thermal tolerance traits may buffer organisms against changing temperatures, making such responses of particular interest in the face of global climate change. Although population variation is integral to the evolvability of this trait, many studies inferring proxies of physiological vulnerability from thermal tolerance traits extrapolate data from one or a few populations to represent the species. Estimates of physiological vulnerability can be further complicated by methodological effects associated with experimental design. We evaluated how populations varied in their acclimation capacity (i.e., the magnitude of plasticity) for critical thermal maximum (CTmax) in two species of tailed frogs (Ascaphidae), cold-stream specialists. We used the estimates of acclimation capacity to infer physiological vulnerability to future warming. We performed CTmax experiments on tadpoles from 14 populations using a fully factorial experimental design of two holding temperatures (8 and 15°C) and two experimental starting temperatures (8 and 15°C). This design allowed us to investigate the acute effects of transferring organisms from one holding temperature to a different experimental starting temperature, as well as fully acclimated responses by using the same holding and starting temperature. We found that most populations exhibited beneficial acclimation, where CTmax was higher in tadpoles held at a warmer temperature, but populations varied markedly in the magnitude of the response and the inferred physiological vulnerability to future warming. We also found that the response of transferring organisms to different starting temperatures varied substantially among populations, although accounting for acute effects did not greatly alter estimates of physiological vulnerability at the species level or for most populations. These results underscore the importance of sampling widely among populations when inferring physiological vulnerability, as population variation in acclimation capacity and thermal sensitivity may be critical when assessing vulnerability to future warming.
Coral reefs are iconic ecosystems that support diverse, productive communities in both shallow and deep waters. However, our incomplete knowledge of cold-water coral (CWC) niche space limits our understanding of their distribution and precludes a complete accounting of the ecosystem services they provide. Here, we present the results of recent surveys of the CWC mound province on the Blake Plateau off the U.S. east coast, an area of intense human activity including fisheries and naval operations, and potentially energy and mineral extraction. At one site, CWC mounds are arranged in lines that total over 150 km in length, making this one of the largest reef complexes discovered in the deep ocean. This site experiences rapid and extreme shifts in temperature between 4.3 and 10.7 °C, and currents approaching 1 m s−1. Carbon is transported to depth by mesopelagic micronekton and nutrient cycling on the reef results in some of the highest nitrate concentrations recorded in the region. Predictive models reveal expanded areas of highly suitable habitat that currently remain unexplored. Multidisciplinary exploration of this new site has expanded understanding of the cold-water coral niche, improved our accounting of the ecosystem services of the reef habitat, and emphasizes the importance of properly managing these systems.
Multi-species assemblages can help identify key resources in their habitat by evaluating how they are partitioning their resources. Here we used the isotopic niche of loggerhead, Kemp’s ridley, and green sea turtles to assess their ecological niche within a Gulf of Mexico bay. Additionally, we assessed temporal and size-class variation in their diets by comparing the δ13C and δ15N values over various capture years and size classes. Based on the isotopic niche overlap results, interspecific competition occurs among loggerhead, Kemp’s ridley, and smaller green turtles. Green turtle and Kemp’s ridley diet varied by size class, while Kemp’s ridley diet also varied temporally. The three species partition resources by selecting different preferred diet items, although some overlap in diet, including between the herbivorous (green) and carnivorous (Kemp’s ridley, loggerhead) species, appears to occur. The high quality of this habitat may allow species co-existence and population recovery despite the possible use of shared resources.
Debris flows regularly traverse bedrock channels that dissect steep landscapes, but our understanding of bedrock erosion by debris flows and their impact on steepland morphology is still rudimentary. Quantitative models of steep bedrock channel networks are based on geomorphic transport laws designed to represent erosion by water-dominated flows. To quantify the impact of debris flow erosion on steep channel network form, it is first necessary to develop methods to estimate spatial variations in bulk debris flow properties (e.g., flow depth, velocity) throughout the channel network that can be integrated into landscape evolution models. Here, we propose and evaluate two methods to estimate spatial variations in bulk debris flow properties along the length of a channel profile. We incorporate both methods into a model designed to simulate the evolution of longitudinal channel profiles that evolve in response to debris flow and fluvial processes. To explore this model framework, we propose a general family of debris flow erosion laws where erosion rate is a function of debris flow depth and channel slope. Model results indicate that erosion by debris flows can explain the occurrence of a scaling break in the slope–area curve at low-drainage areas and that upper-network channel morphology may be useful for inferring catchment-averaged erosion rates in quasi-steady landscapes. Validating specific forms of a debris flow incision law, however, would require more detailed model–data comparisons in specific landscapes where input parameters and channel morphometry can be better constrained. Results improve our ability to interpret topographic signals within steep channel networks and identify observational targets critical for constraining a debris flow incision law.
Coral reefs are iconic ecosystems that support diverse, productive communities in both shallow and deep waters. However, our incomplete knowledge of cold-water coral (CWC) niche space limits our understanding of their distribution and precludes a complete accounting of the ecosystem services they provide. Here, we present the results of recent surveys of the CWC mound province on the Blake Plateau off the U.S. east coast, an area of intense human activity including fisheries and naval operations, and potentially energy and mineral extraction. At one site, CWC mounds are arranged in lines that total over 150 km in length, making this one of the largest reef complexes discovered in the deep ocean. This site experiences rapid and extreme shifts in temperature between 4.3 and 10.7 °C, and currents approaching 1 m s−1. Carbon is transported to depth by mesopelagic micronekton and nutrient cycling on the reef results in some of the highest nitrate concentrations recorded in the region. Predictive models reveal expanded areas of highly suitable habitat that currently remain unexplored. Multidisciplinary exploration of this new site has expanded understanding of the cold-water coral niche, improved our accounting of the ecosystem services of the reef habitat, and emphasizes the importance of properly managing these systems.
A disadvantage of colorimetric detection in nucleic acid amplification assays is the possibility that a colorblind individual may interpret colors differently than observers with full-color vision. Using an isothermal amplification assay, the ability of colorblind individuals to distinguish between positive and negative results for four dyes was tested. Five individuals with self-reported colorblindness and four with full-color vision reported their observations of the color of the solution. Although colorblind individuals may accurately interpret assay results, they were often not accurate in reporting the color. Hydroxynaphthol blue was the most problematic dye, and both phenol red and SYBR™ green were less troublesome. Consideration for colorblind individuals is warranted when developing an assay and training staff in its performance.
Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a. Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.