Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
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Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237391,"text":"70237391 - 2022 - An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","interactions":[],"lastModifiedDate":"2022-10-12T13:40:56.735739","indexId":"70237391","displayToPublicDate":"2022-10-12T08:20:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","title":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","docAbstract":"We used two datasets to investigate the reliability of plumage for sexing adult Ruddy Turnstones Arenaria interpres of the morinella subspecies during May and early June in Delaware Bay, on the Mid-Atlantic Coast of the United States (39.1202°N, 75.2479°W). We first examined 23 years of data on the capture and recapture of 1,818 individual Ruddy Turnstones to assess the consistency of observers with varying levels of expertise in assigning sex using plumage criteria. Among birds recaptured once, the sex recorded for about 10% differed between captures. This increased to about 16% among birds recaptured more than once. Significantly more birds sexed as females early in the season (during 1–12 May) were later sexed as males than vice versa. This suggests that early-season captures may include birds still in non- (or partial) breeding plumage, which can be confused with female breeding plumage. Second, we compared plumage-based and genetic assessments of sex for 66 Ruddy Turnstones captured in Delaware Bay on 29 May 2016 and 19 May 2017; these individuals were sexed in the hand by an expert on shorebird plumages. Plumage-based and molecular assessments differed in only one case. This suggests that fewer birds will be wrongly sexed on plumage if more care is taken and better instruction is given to observers (including how to distinguish non- breeding plumage from female breeding plumage). We suggest simple procedures to reduce field-sexing errors for Ruddy Turnstones based on plumage.
","language":"English","publisher":"International Wader Study Group","doi":"10.18194/ws.00274","usgsCitation":"Fullagar, P.J., Chesser, R., Sitters, H.P., Davey, C.C., Niles, L., Drovetski, S.V., and Cortes-Rodriguez, M., 2022, An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America: Wader Study, v. 129, no. 2, p. 138-147, https://doi.org/10.18194/ws.00274.","productDescription":"10 p.","startPage":"138","endPage":"147","ipdsId":"IP-133440","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":408208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.09979248046875,\n 38.76479194327964\n ],\n [\n -74.94598388671875,\n 38.94232097947902\n ],\n [\n -74.87457275390625,\n 39.095962936305476\n ],\n [\n -74.85260009765625,\n 39.17478791493289\n ],\n [\n -74.8828125,\n 39.22587043822116\n ],\n [\n -75.13824462890625,\n 39.24288969082635\n ],\n [\n -75.23162841796875,\n 39.31517545076218\n ],\n [\n -75.27557373046875,\n 39.32579941789298\n ],\n [\n -75.311279296875,\n 39.36190883564925\n ],\n [\n -75.3662109375,\n 39.37677199661635\n ],\n [\n -75.44586181640625,\n 39.45316112807394\n ],\n [\n -75.51177978515625,\n 39.48284540453334\n ],\n [\n -75.498046875,\n 39.54217596171196\n ],\n [\n -75.47332763671875,\n 39.58240712203527\n ],\n [\n -75.531005859375,\n 39.63319206567459\n ],\n [\n -75.487060546875,\n 39.688167045890395\n ],\n [\n -75.42938232421875,\n 39.75999140525313\n ],\n [\n -75.311279296875,\n 39.82119422647453\n ],\n [\n -75.22064208984375,\n 39.83595916247957\n ],\n [\n -75.08880615234375,\n 39.86969567045658\n ],\n [\n -75.1519775390625,\n 39.91394967016644\n ],\n [\n -75.3662109375,\n 39.86969567045658\n ],\n [\n -75.53924560546875,\n 39.74521015328692\n ],\n [\n -75.5474853515625,\n 39.71141252523694\n ],\n [\n -75.63812255859375,\n 39.614152077002664\n ],\n [\n -75.6024169921875,\n 39.53370327008705\n ],\n [\n -75.62164306640625,\n 39.45528185347343\n ],\n [\n -75.56121826171875,\n 39.40861097325807\n ],\n [\n -75.5145263671875,\n 39.33854604847979\n ],\n [\n -75.42388916015625,\n 39.20246222588238\n ],\n [\n -75.44036865234375,\n 39.16839998800286\n ],\n [\n -75.42938232421875,\n 39.07677595221322\n ],\n [\n -75.38543701171875,\n 39.036252959636606\n ],\n [\n -75.34149169921875,\n 38.974357249228206\n ],\n [\n -75.34423828125,\n 38.9380483825641\n ],\n [\n -75.2947998046875,\n 38.884619201291905\n ],\n [\n -75.29754638671875,\n 38.86323626888358\n ],\n [\n -75.17669677734375,\n 38.77978137804918\n ],\n [\n -75.09979248046875,\n 38.76479194327964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Fullagar, Peter J.","contributorId":297536,"corporation":false,"usgs":false,"family":"Fullagar","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. 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These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220162","usgsCitation":"Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3100-3120, https://doi.org/10.1785/0220220162.","productDescription":"21 p.","startPage":"3100","endPage":"3120","ipdsId":"IP-138939","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.33762311995545,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 51.85857884546667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237374,"text":"70237374 - 2022 - Advances in coral immunity ‘omics in response to disease outbreaks","interactions":[],"lastModifiedDate":"2022-10-12T13:56:05.210143","indexId":"70237374","displayToPublicDate":"2022-10-11T14:09:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Advances in coral immunity ‘omics in response to disease outbreaks","docAbstract":"Coral disease has progressively become one of the most pressing issues affecting coral reef survival. In the last 50 years, several reefs throughout the Caribbean have been severely impacted by increased frequency and intensity of disease outbreaks leading to coral death. A recent example of this is stony coral tissue loss disease which has quickly spread throughout the Caribbean, devastating coral reef ecosystems. Emerging from these disease outbreaks has been a coordinated research response that often integrates ‘omics techniques to better understand the coral immune system. ‘Omics techniques encompass a wide range of technologies used to identify large scale gene, DNA, metabolite, and protein expression. In this review, we discuss what is known about coral immunity and coral disease from an ‘omics perspective. We reflect on the development of biomarkers and discuss ways in which coral disease experiments to test immunity can be improved. Lastly, we consider how existing data can be better leveraged to combat future coral disease outbreaks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.952199","usgsCitation":"Traylor-Knowles, N., Baker, A.C., Beavers, K.M., Garg, N., Guyon, J.R., Hawthorn, A.C., MacKnight, N.J., Medina, M., Mydlarz, L.D., Peters, E.C., Stewart, J.M., Studivan, M.S., and Voss, J.D., 2022, Advances in coral immunity ‘omics in response to disease outbreaks: Frontiers in Marine Science, v. 9, 952199, 26 p., https://doi.org/10.3389/fmars.2022.952199.","productDescription":"952199, 26 p.","ipdsId":"IP-144516","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":446147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.952199","text":"Publisher Index 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University","active":true,"usgs":false}],"preferred":false,"id":854330,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Studivan, Michael S.","contributorId":297510,"corporation":false,"usgs":false,"family":"Studivan","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":64418,"text":"University of Miami, NOAA","active":true,"usgs":false}],"preferred":false,"id":854331,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Voss, Joshua D.","contributorId":150551,"corporation":false,"usgs":false,"family":"Voss","given":"Joshua","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854332,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70237376,"text":"70237376 - 2022 - Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","interactions":[],"lastModifiedDate":"2022-10-11T19:08:25.114099","indexId":"70237376","displayToPublicDate":"2022-10-11T14:04:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","title":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","docAbstract":"Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2022.102576","usgsCitation":"Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E., Siler, D.L., Karra, S., and Middleton, R.S., 2022, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering: Geothermics, v. 106, 102576, 15 p., https://doi.org/10.1016/j.geothermics.2022.102576.","productDescription":"102576, 15 p.","ipdsId":"IP-132590","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1890937","text":"Publisher Index Page"},{"id":408181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Colorado Plateau, Gila Hot Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 32.008075959291055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":854339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karra, Satish","contributorId":297512,"corporation":false,"usgs":false,"family":"Karra","given":"Satish","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middleton, Richard S.","contributorId":297513,"corporation":false,"usgs":false,"family":"Middleton","given":"Richard","email":"","middleInitial":"S.","affiliations":[{"id":64420,"text":"Carbon Solutions LLC","active":true,"usgs":false}],"preferred":false,"id":854344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237354,"text":"70237354 - 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It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. 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It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. 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Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-16","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2022, Physics-guided recurrent neural networks for predicting lake water temperature, chap. 16 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 373-398, https://doi.org/10.1201/9781003143376-16.","productDescription":"26 p.","startPage":"373","endPage":"398","ipdsId":"IP-132700","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854186,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237346,"text":"70237346 - 2022 - Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","interactions":[],"lastModifiedDate":"2022-10-11T16:08:12.135327","indexId":"70237346","displayToPublicDate":"2022-10-11T11:00:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12625,"text":"Limnology & Oceanography: Letters","active":true,"publicationSubtype":{"id":10}},"title":"Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","docAbstract":"The dataset described here includes estimates of historical (1980–2020) daily surface water temperature, lake metadata, and daily weather conditions for lakes bigger than 4 ha in the conterminous United States (n = 185,549), and also in situ temperature observations for a subset of lakes (n = 12,227). 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0000-0003-4434-051X","orcid":"https://orcid.org/0000-0003-4434-051X","contributorId":297456,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":64397,"text":"University of Minnesota Department of Computer Science","active":true,"usgs":false}],"preferred":false,"id":854206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Topp, Simon Nemer 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A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":854209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854210,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237612,"text":"70237612 - 2022 - Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020","interactions":[],"lastModifiedDate":"2022-12-15T14:54:03.689933","indexId":"70237612","displayToPublicDate":"2022-10-11T09:53:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020","docAbstract":"Elevated concentrations and loads of nutrients in the South Platte River and Cherry Creek in Denver, Colorado, may have adverse effects on those streams and downstream water bodies, including increased production of algae, eutrophication, and decreased recreational opportunities. This article describes streamflow and concentrations and loads of nutrients for the South Platte River and Cherry Creek in Denver based on data collected during two longitudinal Lagrangian sampling campaigns in low-flow conditions in fall of 2019 and 2020. The results are used to assess sources and sinks of nutrients in the study area and help to establish baseline conditions against which future changes in nutrient concentrations and loads can be assessed. Discharges from Chatfield and Cherry Creek Reservoirs, storm drains, and most tributaries to the South Platte River, and Cherry Creek were generally small sources of streamflow and nutrient loads in both years. The Marcy Gulch, South Platte Water Renewal, and Robert W. Hite wastewater treatment plants were larger sources of streamflow and nutrient loads. The Burlington Ditch was a sink for streamflow and nutrient loads, diverting more than 95% of the South Platte River during the two sampling campaigns. Most other sinks were associated with decreases in streamflow between sampling sites. Golf courses were a potential source of nutrients for Cherry Creek but not for the South Platte River.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4060","usgsCitation":"Battaglin, W., and Chapin, T.W., 2022, Identifying nutrient sources and sinks to the South Platte River and Cherry Creek, Denver, CO, during low-flow conditions in 2019–2020: River Research and Applications, v. 38, no. 10, p. 1860-1883, https://doi.org/10.1002/rra.4060.","productDescription":"24 p.","startPage":"1860","endPage":"1883","ipdsId":"IP-131828","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":408322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Denver","otherGeospatial":"Cherry Creek, South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.23803710937499,\n 39.35978526869001\n ],\n [\n -103.919677734375,\n 39.35978526869001\n ],\n [\n -103.919677734375,\n 40.66397287638688\n ],\n [\n -105.23803710937499,\n 40.66397287638688\n ],\n [\n -105.23803710937499,\n 39.35978526869001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglin, William A. 0000-0001-7287-7096","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":204638,"corporation":false,"usgs":true,"family":"Battaglin","given":"William A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapin, Tanner William 0000-0003-3905-3241","orcid":"https://orcid.org/0000-0003-3905-3241","contributorId":297923,"corporation":false,"usgs":true,"family":"Chapin","given":"Tanner","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854653,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256617,"text":"70256617 - 2022 - The Bathy-drone: An autonomous unmanned drone-tethered sonar system","interactions":[],"lastModifiedDate":"2024-08-27T14:37:31.951355","indexId":"70256617","displayToPublicDate":"2022-10-10T09:32:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18351,"text":"Drones","active":true,"publicationSubtype":{"id":10}},"title":"The Bathy-drone: An autonomous unmanned drone-tethered sonar system","docAbstract":"A unique drone-based system for underwater mapping (bathymetry) was developed at the University of Florida. The system, called the “Bathy-drone”, comprises a drone that drags, via a tether, a small vessel on the water surface in a raster pattern. The vessel is equipped with a recreational commercial off-the-shelf (COTS) sonar unit that has down-scan, side-scan, and chirp capabilities and logs GPS-referenced sonar data onboard or transmitted in real time with a telemetry link. Data can then be retrieved post mission and plotted in various ways. The system provides both isobaths and contours of bottom hardness. Extensive testing of the system was conducted on a 5 acre pond located at the University of Florida Plant Science and Education Unit in Citra, FL. Prior to performing scans of the pond, ground-truth data were acquired with an RTK GNSS unit on a pole to precisely measure the location of the bottom at over 300 locations. An assessment of the accuracy and resolution of the system was performed by comparison to the ground-truth data. The pond ground truth had an average depth of 2.30 m while the Bathy-drone measured an average 21.6 cm deeper than the ground truth, repeatable to within 2.6 cm. The results justify integration of RTK and IMU corrections. During testing, it was found that there are numerous advantages of the Bathy-drone system compared to conventional methods including ease of implementation and the ability to initiate surveys from the land by flying the system to the water or placing the platform in the water. The system is also inexpensive, lightweight, and low-volume, thus making transport convenient. The Bathy-drone can collect data at speeds of 0–24 km/h (0–15 mph) and, thus, can be used in waters with swift currents. Additionally, there are no propellers or control surfaces underwater; hence, the vessel does not tend to snag on floating vegetation and can be dragged over sandbars. An area of more than 10 acres was surveyed using the Bathy-drone in one battery charge and in less than 25 min.
","language":"English","publisher":"MDPI","doi":"10.3390/drones6100294","usgsCitation":"Diaz, A.L., Ortega, A.E., Tingle, H., Pulido, A., Cordero, O., Nelson, M., Cocoves, N.E., Shin, J., Carthy, R., Wilkinson, B.E., and Ifju, P.G., 2022, The Bathy-drone: An autonomous unmanned drone-tethered sonar system: Drones, v. 6, no. 10, 294, 19 p., https://doi.org/10.3390/drones6100294.","productDescription":"294, 19 p.","ipdsId":"IP-144387","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/drones6100294","text":"Publisher Index Page"},{"id":433196,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Antonio L.","contributorId":341377,"corporation":false,"usgs":false,"family":"Diaz","given":"Antonio","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ortega, Andrew E.","contributorId":341378,"corporation":false,"usgs":false,"family":"Ortega","given":"Andrew","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tingle, Henry","contributorId":341379,"corporation":false,"usgs":false,"family":"Tingle","given":"Henry","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pulido, Andres","contributorId":341380,"corporation":false,"usgs":false,"family":"Pulido","given":"Andres","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cordero, Orlando","contributorId":341381,"corporation":false,"usgs":false,"family":"Cordero","given":"Orlando","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Marisa","contributorId":341382,"corporation":false,"usgs":false,"family":"Nelson","given":"Marisa","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cocoves, Nicholas E.","contributorId":341383,"corporation":false,"usgs":false,"family":"Cocoves","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shin, Jaejeong","contributorId":341384,"corporation":false,"usgs":false,"family":"Shin","given":"Jaejeong","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wilkinson, Benjamin E.","contributorId":341385,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ifju, Peter G.","contributorId":341386,"corporation":false,"usgs":false,"family":"Ifju","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908334,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70237751,"text":"70237751 - 2022 - Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies","interactions":[],"lastModifiedDate":"2022-10-21T14:14:05.787062","indexId":"70237751","displayToPublicDate":"2022-10-09T09:12:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10757,"text":"Energies","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies","title":"Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies","docAbstract":"Offshore geological sequestration of CO2 offers a viable approach for reducing greenhouse gas emissions into the atmosphere. Strategies include injection of CO2 into the deep-ocean or ocean-floor sediments, whereby depending on pressure–temperature conditions, CO2 can be trapped physically, gravitationally, or converted to CO2 hydrate. Energy-driven research continues to also advance CO2-for-CH4 replacement strategies in the gas hydrate stability zone (GHSZ), producing methane for natural gas needs while sequestering CO2. In all cases, safe storage of CO2 requires reliable monitoring of the targeted CO2 injection sites and the integrity of the repository over time, including possible leakage. Electromagnetic technologies used for oil and gas exploration, sensitive to electrical conductivity, have long been considered an optimal monitoring method, as CO2, similar to hydrocarbons, typically exhibits lower conductivity than the surrounding medium. We apply 3D controlled-source electromagnetic (CSEM) forward modeling code to simulate an evolving CO2 reservoir in deep-ocean sediments, demonstrating sufficient sensitivity and resolution of CSEM data to detect reservoir changes even before sophisticated inversion of data. Laboratory measurements place further constraints on evaluating certain systems within the GHSZ; notably, CO2 hydrate is measurably weaker than methane hydrate, and >1 order of magnitude more conductive, properties that may affect site selection, stability, and modeling considerations.
","language":"English","publisher":"MDPI","doi":"10.3390/en15197411","usgsCitation":"Constable, S., and Stern, L.A., 2022, Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies: Energies, v. 15, no. 19, 7411, 16 p., https://doi.org/10.3390/en15197411.","productDescription":"7411, 16 p.","ipdsId":"IP-142378","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en15197411","text":"Publisher Index Page"},{"id":408604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Constable, Steven","contributorId":9178,"corporation":false,"usgs":false,"family":"Constable","given":"Steven","email":"","affiliations":[{"id":16196,"text":"Scripps Institution of Oceanography, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":855447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":855448,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237298,"text":"ofr20221057 - 2022 - Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","interactions":[],"lastModifiedDate":"2026-03-27T20:28:39.65672","indexId":"ofr20221057","displayToPublicDate":"2022-10-07T11:57:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1057","displayTitle":"Channel Mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","title":"Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","docAbstract":"Bathymetric and topographic data were collected from May 2013 to February 2016 along the 15.84-mile reach of the Colorado River spanning from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona. Channel bathymetry was mapped using multibeam and singlebeam echo sounders; subaerial topography was mapped using a combination of ground-based total stations and aerial photogrammetry. These data were combined to produce a digital elevation model (DEM), spatially variable estimates of DEM uncertainty, and bed-substrate distribution maps. This project is part of a larger effort to monitor the status and trends of sand storage along the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. This report documents the study methodologies (survey methods and post-processing procedures, DEM production and uncertainty assessment, and bed-substrate classification) and presents the resulting datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221057","collaboration":"Prepared in cooperation with Northern Arizona University and Marda Science LLC","usgsCitation":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Gushue, T., Buscombe, D.D., and Kohl, K., 2022, Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona: U.S. Geological Survey Open-File Report 2022-1057, 20 p., https://doi.org/10.3133/ofr20221057.","productDescription":"Report: v, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-120853","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":408061,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1057/ofr20221057.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 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This subspecies, an unusual morph lacking the typical lateral bony plates of the G. aculeatus complex, occurs at arid southern latitudes in the eastern Pacific Ocean and survives in only three inland locations. Managers have lacked molecular data to answer basic questions about the ancestry and genetic distinctiveness of unarmoured populations. These data could be used to prioritize conservation efforts. We sampled G. aculeatus from 36 localities and used microsatellites and whole genome data to place unarmoured populations within the broader evolutionary context of G. aculeatus across southern California/northern Baja California. We identified three genetic groups with none consisting solely of unarmoured populations. Unlike G. aculeatus at northern latitudes, where Pleistocene glaciation has produced similar historical demographic profiles across populations, we found markedly different demographics depending on sampling location, with inland unarmoured populations showing steeper population declines and lower heterozygosity compared to low armoured populations in coastal lagoons. One exception involved the only high elevation population in the region, where the demography and alleles of unarmoured fish were similar to low armoured populations near the coast, exposing one of several cases of artificial translocation. Our results suggest that the current “management-by-phenotype” approach, based on lateral plates, is incidentally protecting the most imperilled populations; however, redirecting efforts toward evolutionary units, regardless of phenotype, may more effectively preserve adaptive potential.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.16722","usgsCitation":"Turba, R., Richmond, J.Q., Fitz-Gibbon, S., Morselli, M., Fisher, R., Swift, C.C., Ruiz-Campos, G., Backlin, A.R., Dellith, C., and Jacobs, D.K., 2022, Genetic structure and historic demography of endangered unarmoured threespine stickleback at southern latitudes signals a potential new management approach: Molecular Ecology, v. 31, no. 24, p. 6515-6530, https://doi.org/10.1111/mec.16722.","productDescription":"16 p.","startPage":"6515","endPage":"6530","ipdsId":"IP-144634","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446193,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.16722","text":"Publisher Index Page"},{"id":409689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Baja California, 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wave","interactions":[],"lastModifiedDate":"2024-06-13T16:04:59.632829","indexId":"70255211","displayToPublicDate":"2022-10-06T10:58:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Industrial energy development decouples ungulate migration from the green wave","docAbstract":"The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed ‘green-wave surfing’) is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (Odocoileus hemionus) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behaviour before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behaviour of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behaviour present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-022-01887-9","collaboration":"Western EcoSystems, INC","usgsCitation":"Aikens, E.O., Wyckoff, T., Sawyer, H., and Kauffman, M., 2022, Industrial energy development decouples ungulate migration from the green wave: Nature Ecology and Evolution, v. 6, p. 1733-1741, https://doi.org/10.1038/s41559-022-01887-9.","productDescription":"9 p.","startPage":"1733","endPage":"1741","ipdsId":"IP-136329","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.50057122965278,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 41.908308585415085\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":272241,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen","email":"","middleInitial":"O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":903738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wyckoff, Teal B.","contributorId":339010,"corporation":false,"usgs":false,"family":"Wyckoff","given":"Teal B.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawyer, Hall","contributorId":287880,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","affiliations":[{"id":61660,"text":"Western Ecosystems Technology, Inc., Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":903740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903741,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237277,"text":"70237277 - 2022 - Absolute accuracy assessment of lidar point cloud using amorphous objects","interactions":[],"lastModifiedDate":"2022-10-06T14:30:04.492861","indexId":"70237277","displayToPublicDate":"2022-10-06T09:26:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Absolute accuracy assessment of lidar point cloud using amorphous objects","docAbstract":"The accuracy assessment of airborne lidar point cloud typically estimates vertical accuracy by computing RMSEz (root mean square error of the z coordinate) from ground check points (GCPs). Due to the low point density of the airborne lidar point cloud, there is often not enough accurate semantic context to find an accurate conjugate point. To advance the accuracy assessment in full three-dimensional (3D) context, geometric features, such as the three-plane intersection point or two-line intersection point, are often used. Although the point density is still low, geometric features are mathematically modeled from many points. Thus, geometric features provide a robust determination of the intersection point, and the point is considered as a GCP. When no regular built objects are available, we describe the process of utilizing features of irregular shape called amorphous natural objects, such as a tree or a rock. When scanned to a high-density point cloud, an amorphous natural object can be used as ground truth reference data to estimate 3D georeferencing errors of the airborne lidar point cloud. The algorithm to estimate 3D accuracy is the optimization that minimizes the sum of the distance between the airborne lidar points to the ground scanned data. The search volume partitioning was the most important procedure to improve the computational efficiency. We also performed an extensive study to address the external uncertainty associated with the amorphous object method. We describe an accuracy assessment using amorphous objects (108 trees) spread over the project area. The accuracy results for ∆x, ∆y, and ∆z obtained using the amorphous object method were 3.1 cm, 3.6 cm, and 1.7 cm RMSE, along with a mean error of 0.1 cm, 0.1 cm, and 4.5 cm, respectively, satisfying the accuracy requirement of U.S. Geological Survey lidar base specification. This approach shows strong promise as an alternative to geometric feature methods when artificial targets are scarce. The relative convenience and advantages of using amorphous targets, along with its good performance shown here, make this amorphous object method a practical way to perform 3D accuracy assessment.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14194767","usgsCitation":"Kim, M., Stoker, J.M., Irwin, J., Danielson, J.J., and Park, S., 2022, Absolute accuracy assessment of lidar point cloud using amorphous objects: Remote Sensing, v. 14, no. 19, 4767, 18 p., https://doi.org/10.3390/rs14194767.","productDescription":"4767, 18 p.","ipdsId":"IP-145321","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":446201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14194767","text":"Publisher Index Page"},{"id":408035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":853945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":853946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Park, Seonkyung 0000-0003-3203-1998 seonkyungpark@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":222488,"corporation":false,"usgs":false,"family":"Park","given":"Seonkyung","email":"seonkyungpark@contractor.usgs.gov","affiliations":[{"id":40547,"text":"United Support Services, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":853949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237290,"text":"70237290 - 2022 - Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","interactions":[],"lastModifiedDate":"2022-10-06T14:25:24.776873","indexId":"70237290","displayToPublicDate":"2022-10-06T09:19:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","docAbstract":"Reliable quantitative information on sediment sources to rivers is critical to mitigate contamination and target conservation and restoration actions. However, the determination of the relative importance of sediment sources is complicated at the scale of large river basins by immense variability in erosional processes and sediment sources over space and time, heterogeneity in sediment transport and deposition, and a paucity of sediment monitoring data. Sediment source fingerprinting is an increasingly adopted field-based technique that identifies the nature and relative source contribution of sediment transported in waterways. Notably, sediment source fingerprinting provides information that is independent of other field, modeling, or remotely sensed techniques. However, the diversity in sediment fingerprinting sampling, analytical, and interpretive methods has been recognized as a problem in terms of developing standardized procedures for its application at the scale of large river basins. Accordingly, this review focuses on established sediment source fingerprinting studies conducted within the Mississippi River Basin (MRB), summarizes unique information provided by sediment source fingerprinting that is distinct from traditional monitoring techniques, evaluates consistency and reliability of methodological approaches among MRB studies, and provides prospects for the use of the sediment source fingerprinting technique as an aid to large-scale landscape conservation and restoration under current management frameworks. Most established MRB studies got creditable fingerprinting results and considered near-channel sources as the dominant sediment sources in most cases, while the comparability of their results suffers from a lack of standardization in procedural steps. Findings from MRB studies demonstrate that sediment source fingerprinting is a highly valuable and reliable sediment source assessment approach to assist land and water resource management under current management frameworks, but efforts are still needed to make this technique ready to be used in a more predominant way in large-scale landscape conservation and restoration efforts. We summarized research needs and suggested the best fingerprinting practices for management purposes with the aim of ensuring that this technique is as robust and reliable as it moves forward.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.116260","usgsCitation":"Xu, Z., Belmont, P., Brahney, J., and Gellis, A.C., 2022, Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin: Journal of Environmental Management, v. 324, 116260, 20 p., https://doi.org/10.1016/j.jenvman.2022.116260.","productDescription":"116260, 20 p.","ipdsId":"IP-141762","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":446204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2022.116260","text":"Publisher Index Page"},{"id":408033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.0869140625,\n 29.57345707301757\n ],\n [\n -89.7802734375,\n 28.729130483430154\n ],\n [\n -89.20898437499999,\n 29.34387539941801\n ],\n [\n -89.5166015625,\n 30.107117887092357\n ],\n [\n -89.384765625,\n 33.65120829920497\n ],\n [\n -82.8369140625,\n 34.77771580360469\n ],\n [\n -79.40917968749999,\n 36.59788913307022\n ],\n [\n -77.6953125,\n 42.391008609205045\n ],\n [\n -79.365234375,\n 42.4234565179383\n ],\n [\n -81.9580078125,\n 41.31082388091818\n ],\n [\n -87.099609375,\n 41.0130657870063\n ],\n [\n -87.62695312499999,\n 41.672911819602085\n ],\n [\n -88.2861328125,\n 43.229195113965005\n ],\n [\n -90.1318359375,\n 46.49839225859763\n ],\n [\n -92.021484375,\n 46.558860303117164\n ],\n [\n -93.8232421875,\n 47.54687159892238\n ],\n [\n -95.00976562499999,\n 47.54687159892238\n ],\n [\n -98.61328125,\n 47.100044694025215\n ],\n [\n -104.0185546875,\n 49.009050809382046\n ],\n [\n -115.00488281250001,\n 49.03786794532644\n ],\n [\n -114.60937499999999,\n 46.92025531537451\n ],\n [\n -114.521484375,\n 45.85941212790755\n ],\n [\n -112.8076171875,\n 44.62175409623324\n ],\n [\n -111.1376953125,\n 44.99588261816546\n ],\n [\n -109.2919921875,\n 44.715513732021336\n ],\n [\n -108.896484375,\n 42.65012181368022\n ],\n [\n -106.12792968749999,\n 40.88029480552824\n ],\n [\n -105.3369140625,\n 39.26628442213066\n ],\n [\n -105.29296874999999,\n 37.16031654673677\n ],\n [\n -105.29296874999999,\n 34.92197103616377\n ],\n [\n -103.0517578125,\n 34.23451236236987\n ],\n [\n -97.646484375,\n 33.46810795527896\n ],\n [\n -93.955078125,\n 31.353636941500987\n ],\n [\n -94.0869140625,\n 29.57345707301757\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Zhen","contributorId":297389,"corporation":false,"usgs":false,"family":"Xu","given":"Zhen","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":853997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belmont, Patrick","contributorId":275033,"corporation":false,"usgs":false,"family":"Belmont","given":"Patrick","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":853998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":853999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237272,"text":"70237272 - 2022 - Simple statistical models can be sufficient for testing hypotheses with population time series data","interactions":[],"lastModifiedDate":"2022-10-06T14:10:39.16442","indexId":"70237272","displayToPublicDate":"2022-10-06T08:52:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Simple statistical models can be sufficient for testing hypotheses with population time series data","docAbstract":"Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9339","usgsCitation":"Wenger, S., Stowe, E.S., Gido, K.B., Freeman, M., Kanno, Y., Franssen, N.R., Olden, J., Poff, N.L., Walters, A.W., Bumpers, P.M., Mims, M.C., Hooten, M.B., and Lu, X., 2022, Simple statistical models can be sufficient for testing hypotheses with population time series data: Ecology and Evolution, v. 12, no. 9, e9339, 13 p., https://doi.org/10.1002/ece3.9339.","productDescription":"e9339, 13 p.","ipdsId":"IP-133439","costCenters":[{"id":683,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446206,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.9339","text":"External Repository"},{"id":408029,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":853925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stowe, Edward S.","contributorId":273256,"corporation":false,"usgs":false,"family":"Stowe","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gido, Keith B.","contributorId":198487,"corporation":false,"usgs":false,"family":"Gido","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":853927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Franssen, Nathan R.","contributorId":273252,"corporation":false,"usgs":false,"family":"Franssen","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olden, Julian 0000-0003-2143-1187","orcid":"https://orcid.org/0000-0003-2143-1187","contributorId":296007,"corporation":false,"usgs":false,"family":"Olden","given":"Julian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":853931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poff, N. LeRoy","contributorId":261271,"corporation":false,"usgs":false,"family":"Poff","given":"N.","email":"","middleInitial":"LeRoy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":853933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bumpers, Phillip M.","contributorId":203871,"corporation":false,"usgs":false,"family":"Bumpers","given":"Phillip","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853934,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mims, Meryl C. 0000-0003-0570-988X","orcid":"https://orcid.org/0000-0003-0570-988X","contributorId":209951,"corporation":false,"usgs":false,"family":"Mims","given":"Meryl","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":853935,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hooten, Mevin B. 0000-0002-1614-723X","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":292295,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853936,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lu, Xinyi","contributorId":279368,"corporation":false,"usgs":false,"family":"Lu","given":"Xinyi","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":853937,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70238817,"text":"70238817 - 2022 - Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","interactions":[],"lastModifiedDate":"2022-12-13T13:40:55.160716","indexId":"70238817","displayToPublicDate":"2022-10-06T07:10:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5538,"text":"AoB PLANTS","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","docAbstract":"Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/aobpla/plac045","usgsCitation":"Applestein, C., Caughlin, T., and Germino, M., 2022, Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation: AoB PLANTS, v. 14, no. 6, plac045, 13 p., https://doi.org/10.1093/aobpla/plac045.","productDescription":"plac045, 13 p.","ipdsId":"IP-127630","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aobpla/plac045","text":"Publisher Index Page"},{"id":410359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.2526670227266,\n 45.4\n ],\n [\n -117.2526670227266,\n 43.21761290801206\n ],\n [\n -113.74569616023115,\n 43.21761290801206\n ],\n [\n -113.74569616023115,\n 45.4\n ],\n [\n -117.2526670227266,\n 45.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":858781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858782,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240117,"text":"70240117 - 2022 - Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","interactions":[],"lastModifiedDate":"2023-01-27T13:09:43.480884","indexId":"70240117","displayToPublicDate":"2022-10-06T07:02:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","docAbstract":"Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.
Widespread surface creep is observed across a number of active faults included in the United States (US) National Seismic Hazard Model (NSHM). In northern California, creep occurs on the central section of the San Andreas fault, along the Hayward and Calaveras faults through the San Francisco Bay Area, and to the north coast region along the Maacama and Bartlett Springs faults. In southern California, creep is observed across the Coachella segment of the San Andreas fault, through the Brawley Seismic Zone, and along the Imperial and Superstition Hills faults. Seismic hazard assessments for California have accounted for creep using various data and methods, including the most recent Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3) in 2013. The purpose of this study is to expand and update the UCERF3 creep rate data set for the 2023 release of the US NSHM and to invert geodetic data and the surface creep rate data for the spatial distribution of interseismic fault creep on California faults using an elastic model with physical creep constraints. The updated surface creep rate compilation consists of a variety of data types including alignment arrays, offset cultural markers, creepmeters, Interferometric Synthetic Aperture Radar, and Global Positioning System data. We compile a total of 497 surface creep rate measurements, 400 of which are new and 97 of which appear in the UCERF3 compilation. We compute creep rate distributions for each of the five 2023 NSHM geodetic‐based and geologic‐based deformation models. Computed creep rates are used to reduce the total fault moment rate available for earthquake sequences in the NSHM model. We find that, despite relatively large variability in model long‐term slip rates across all five deformation models, the variability in depth‐averaged creep rate across all models is relatively small, typically 5–10 mm/yr along the creeping San Andreas fault section and only 2–4 mm/yr along the Maacama and Rodgers Creek‐Hayward faults.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/ 0220220186","usgsCitation":"Johnson, K., Murray, J.R., and Wespestad, C., 2022, Creep rate models for the 2023 US National Seismic Hazard Model: Physically constrained inversions for the distribution of creep on California faults: Seismological Research Letters, v. 93, no. 6, p. 3151-3169, https://doi.org/10.1785/ 0220220186.","productDescription":"19 p.","startPage":"3151","endPage":"3169","ipdsId":"IP-142160","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, K. 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M.","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":927345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wespestad, Crystal","contributorId":296055,"corporation":false,"usgs":false,"family":"Wespestad","given":"Crystal","email":"","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":927347,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256651,"text":"70256651 - 2022 - Next-generation technologies unlock new possibilities to track rangeland productivity and quantify multi-scale conservation outcomes","interactions":[],"lastModifiedDate":"2024-08-29T14:42:33.295584","indexId":"70256651","displayToPublicDate":"2022-10-05T09:34:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Next-generation technologies unlock new possibilities to track rangeland productivity and quantify multi-scale conservation outcomes","docAbstract":"Historically, relying on plot-level inventories impeded our ability to quantify large-scale change in plant biomass, a key indicator of conservation practice outcomes in rangeland systems. Recent technological advances enable assessment at scales appropriate to inform management by providing spatially comprehensive estimates of productivity that are partitioned by plant functional group across all contiguous US rangelands. We partnered with the Sage Grouse and Lesser Prairie-Chicken Initiatives and the Nebraska Natural Legacy Project to demonstrate the ability of these new datasets to quantify multi-scale changes and heterogeneity in plant biomass following mechanical tree removal, prescribed fire, and prescribed grazing. In Oregon's sagebrush steppe, for example, juniper tree removal resulted in a 21% increase in one pasture's productivity and an 18% decline in another. In Nebraska's Loess Canyons, perennial grass productivity initially declined 80% at sites invaded by trees that were prescriptively burned, but then fully recovered post-fire, representing a 492% increase from nadir. In Kansas' Shortgrass Prairie, plant biomass increased 4-fold (966,809 kg/ha) in pastures that were prescriptively grazed, with gains highly dependent upon precipitation as evidenced by sensitivity of remotely sensed estimates (SD ± 951,308 kg/ha). Our results emphasize that next-generation remote sensing datasets empower land managers to move beyond simplistic control versus treatment study designs to explore nuances in plant biomass in unprecedented ways. The products of new remote sensing technologies also accelerate adaptive management and help communicate wildlife and livestock forage benefits from management to diverse stakeholders.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.116359","usgsCitation":"Roberts, C.P., Naugle, D., Allred, B.W., Donovan, V.M., Fogarty, D.T., Jones, M., Maestas, J., Olsen, A.C., and Twidwell, D., 2022, Next-generation technologies unlock new possibilities to track rangeland productivity and quantify multi-scale conservation outcomes: Journal of Environmental Management, v. 324, 116359, 8 p., https://doi.org/10.1016/j.jenvman.2022.116359.","productDescription":"116359, 8 p.","ipdsId":"IP-137365","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2022.116359","text":"Publisher Index Page"},{"id":433304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Nebraska, Oregon","geographicExtents":"{\n \"type\": 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Montana","active":true,"usgs":false}],"preferred":false,"id":908492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allred, Brady W.","contributorId":341485,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":908493,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donovan, Victoria M.","contributorId":341486,"corporation":false,"usgs":false,"family":"Donovan","given":"Victoria","email":"","middleInitial":"M.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908494,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Dillon T.","contributorId":341487,"corporation":false,"usgs":false,"family":"Fogarty","given":"Dillon","email":"","middleInitial":"T.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908495,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Matthew O.","contributorId":341488,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew O.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":908496,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maestas, Jeremy D.","contributorId":341489,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy D.","affiliations":[{"id":65354,"text":"USDA Natural Resources Conservation Service","active":true,"usgs":false}],"preferred":false,"id":908497,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Olsen, Andrew C.","contributorId":341490,"corporation":false,"usgs":false,"family":"Olsen","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":7041,"text":"The Nature 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This model uses the method of Zeng and Shen (2017) to invert for slip rate and strain‐rate parameters based on inputs from Global Positioning System (GPS) velocities and geologic slip‐rate constraints. The model connects adjacent major fault segments in California and the Cascadia subduction zone to form blocks that extend to the boundaries of the study area. Faults within the blocks are obtained from the NSHM geologic fault section database. The model slip rates are determined using a least‐squares inversion with a normalized chi‐square of 6.6. I also apply a time‐dependent correction called “ghost transient” effect to account for the viscoelastic responses from large historic earthquakes along the San Andreas fault and Cascadia subduction zone. Major discrepancies between model slip rates and geologic slip rates along the San Andreas fault, for example, from the Cholame to the Mojave and San Bernardino segments of the San Andreas, are well reduced after the ghost transient correction is applied to GPS velocities. The off‐fault moment rate distribution is consistent with regional tectonics and seismicity patterns with a total rate of
Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.
Location: Continental United States.
Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.
Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.
Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.
","language":"English","publisher":"NeoBiota","doi":"10.3897/neobiota.77.86364","usgsCitation":"Jarnevich, C.S., Sofaer, H., Engelstad, P., and Belamaric, P., 2022, Regional models do not outperform continental models for invasive species: NeoBiota, v. 77, https://doi.org/10.3897/neobiota.77.86364.","productDescription":"22 p.","startPage":"1-22","ipdsId":"IP-137001","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446233,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.77.86364","text":"Publisher Index Page"},{"id":435667,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AL0PN","text":"USGS data release","linkHelpText":"Data to create and evaluate distribution models for invasive species for different geographic extents"},{"id":409981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","noUsgsAuthors":false,"publicationDate":"2022-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":858156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":858157,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":858158,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belamaric, Pairsa 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":299593,"corporation":false,"usgs":false,"family":"Belamaric","given":"Pairsa","affiliations":[{"id":64897,"text":"Student Contractor to the USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":858159,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237881,"text":"70237881 - 2022 - Hurdles to developing quantitative decision support for Endangered Species Act resource allocation","interactions":[],"lastModifiedDate":"2022-10-31T12:01:40.279728","indexId":"70237881","displayToPublicDate":"2022-10-04T06:58:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"title":"Hurdles to developing quantitative decision support for Endangered Species Act resource allocation","docAbstract":"The U.S. Fish and Wildlife Service oversees the recovery of many species protected by the U.S. Endangered Species Act (ESA). Recent research suggests that a structured approach to allocating conservation resources could increase recovery outcomes for ESA listed species. Quantitative approaches to decision support can efficiently allocate limited financial resources and maximize desired outcomes. Yet, developing quantitative decision support under real-world constraints is challenging. Approaches that pair research teams and end-users are generally the most effective. However, co-development requires overcoming “hurdles” that can arise because of differences in the mental models of the co-development team. These include perceptions that: (1) scarce funds should be spent on action, not decision support; (2) quantitative approaches are only useful for simple decisions; (3) quantitative tools are inflexible and prescriptive black boxes; (4) available data are not good enough to support decisions; and (5) prioritization means admitting defeat. Here, we describe how we addressed these misperceptions during the development of a prototype resource allocation decision support tool for understanding trade-offs in U.S. endangered species recovery. We describe how acknowledging these hurdles and identifying solutions enabled us to progress with development. We believe that our experience can assist other applications of developing quantitative decision support for resource allocation.