The richness and composition of a small mammal community inhabiting semiarid California oak woodland may be changing in response to climate change, but we know little about the causes or consequence of these changes. We applied a capture-mark-recapture model to 17 years (1997–2013) of live trapping data to estimate species-specific abundances. The big-eared woodrat was the most frequently captured species in the area, contributing 58% of total captures. All small mammal populations exhibited seasonal fluctuations, whereas those of the California mouse, brush mouse, and pinyon mouse declined during the study period. We also applied a multispecies dynamic occupancy model to our small mammal detection history data to estimate species richness, occupancy ( ), detection (p), local extinction ( ), and colonization ( ) probabilities, and to discern factors affecting these parameters. We found that decreased from 0.369 ± 0.088 in 1997 to 0.248 ± 0.054 in 2013; was lower during the dry season (May–September) than the wet season (October–April) and was positively influenced by total seasonal rainfall (slope parameter, = 0.859 ± 0.371; 95% CI = 0.132–1.587). Mean mammalian species richness decreased from 11.943 ± 0.461 in 1997 to 7.185 ± 0.425 in 2013. With highly variable climatic patterns expected in the future, especially increased frequency and intensity of droughts, it is important to monitor small mammal communities inhabiting threatened California oak woodlands.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10611","usgsCitation":"Ghimirey, Y.P., Tietje, W.D., Polyakov, A.Y., Hines, J.E., and Oli, M.K., 2023, Decline in small mammal species richness in coastal-central California, 1997–2013: Ecology and Evolution, v. 13, no. 12, e10611, 11 p., https://doi.org/10.1002/ece3.10611.","productDescription":"e10611, 11 p.","ipdsId":"IP-157415","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441442,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10611","text":"Publisher Index Page"},{"id":423789,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Camp Roberts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.94253263683494,\n 35.90849811107087\n ],\n [\n -120.94253263683494,\n 35.64500150778204\n ],\n [\n -120.60126981945214,\n 35.64500150778204\n ],\n [\n -120.60126981945214,\n 35.90849811107087\n ],\n [\n -120.94253263683494,\n 35.90849811107087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghimirey, Yadav P.","contributorId":332600,"corporation":false,"usgs":false,"family":"Ghimirey","given":"Yadav","email":"","middleInitial":"P.","affiliations":[{"id":79505,"text":"Univ. of FL","active":true,"usgs":false}],"preferred":false,"id":890598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tietje, William D.","contributorId":332601,"corporation":false,"usgs":false,"family":"Tietje","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":79506,"text":"Friends of Nature, Nepal","active":true,"usgs":false}],"preferred":false,"id":890599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Polyakov, Anne Y.","contributorId":332602,"corporation":false,"usgs":false,"family":"Polyakov","given":"Anne","email":"","middleInitial":"Y.","affiliations":[{"id":79507,"text":"Univ. of CA","active":true,"usgs":false}],"preferred":false,"id":890600,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":890601,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oli, Madan K. 0000-0001-6944-0061","orcid":"https://orcid.org/0000-0001-6944-0061","contributorId":201302,"corporation":false,"usgs":false,"family":"Oli","given":"Madan","email":"","middleInitial":"K.","affiliations":[{"id":13453,"text":"University of Florida, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":890602,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250456,"text":"70250456 - 2023 - The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management","interactions":[],"lastModifiedDate":"2023-12-12T12:36:33.348716","indexId":"70250456","displayToPublicDate":"2023-12-10T06:34:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5490,"text":"Agricultural & Environmental Letters","onlineIssn":"2471-9625","active":true,"publicationSubtype":{"id":10}},"title":"The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management","docAbstract":"Understanding where, when, and why agroecosystems are changing requires quality information about ecosystems that span land tenure, ecological processes, and spatial scales. Over the past two decades, land management agencies and research groups have adopted a suite of standardized methods for monitoring rangelands, which have been implemented at over 85,000 monitoring locations globally. However, the ability to use these data to understand agroecosystem dynamics and change across scales and across land ownership has been limited because, until now, these data have not been available in a harmonized, accessible format for analyses, modeling, and decision-support tools. We present the Landscape Data Commons, a cyberinfrastructure platform that harmonizes and aggregates standardized agroecosystem data, enables linkages to models, and facilitates analysis and interpretation of data within decision-support tools. The Landscape Data Commons provides a community platform for users to contribute data and develop next-generation tools to support agroecosystem management through the 21st century.
Coastal wetlands are predicted to undergo extensive transformation due to climate and land use change. Baseline maps of coastal wetlands can be used to help assess changes. Found in the upper portion of the estuarine zone, high marsh and salt pannes/flats provide ecosystem goods and services and are particularly important to fish and wildlife. We developed the first map of high marsh and salt pannes/flats along the northern Gulf of Mexico using regional models that included spectral indices related to greenness and wetness from optical satellite imagery, elevation data, irregularly flooded wetland probability information, and synthetic aperture radar backscatter. We found the greatest relative coverage of high marsh along the Texas coast (30% to 65%) and the Florida Panhandle (40%), whereas the greatest relative coverage of salt pannes/flats was along the lower Texas coast (74%) and the middle Texas coast (15%). As part of this effort, we also developed a map that highlighted irregularly flooded wetlands dominated by Juncus roemerianus (black needlerush) for part of the study area. Both maps had an overall accuracy of around 80%. Our results advance the understanding of estuarine marsh zonation and provide a baseline for assessing future transformations.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10106049.2023.2285354","usgsCitation":"Enwright, N., Cheney, W.C., Evans, K., Thurman, H., Woodrey, M.S., Fournier, A., Moon, J.A., Levy, H.E., Cox, J.A., Kappes, P.J., Nyman, J., and Pitchford, J.L., 2023, Mapping high marsh and salt pannes/flats along the northern Gulf of Mexico coast: Geocarto International, v. 38, no. 1, 2285354, 21 p., https://doi.org/10.1080/10106049.2023.2285354.","productDescription":"2285354, 21 p.","ipdsId":"IP-156783","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441455,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10106049.2023.2285354","text":"Publisher Index Page"},{"id":423321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, 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Center","active":true,"usgs":true}],"preferred":true,"id":889851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheney, Wyatt Charles 0000-0003-1009-8411","orcid":"https://orcid.org/0000-0003-1009-8411","contributorId":290681,"corporation":false,"usgs":true,"family":"Cheney","given":"Wyatt","email":"","middleInitial":"Charles","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":889852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Kristine O.","contributorId":301250,"corporation":false,"usgs":false,"family":"Evans","given":"Kristine O.","affiliations":[{"id":65345,"text":"Quantitative Ecology and Spatial Technologies (QuEST) Lab, Department of Wildlife, Fisheries and Aquaculture, Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":889853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thurman, Hana R. 0000-0001-7097-5362","orcid":"https://orcid.org/0000-0001-7097-5362","contributorId":294346,"corporation":false,"usgs":false,"family":"Thurman","given":"Hana R.","affiliations":[{"id":63558,"text":"Cherokee Nation System Solutions, contracted to the U.S. Geological Survey, Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":889854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodrey, Mark S.","contributorId":259212,"corporation":false,"usgs":false,"family":"Woodrey","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":889855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fournier, Auriel 0000-0002-8530-9968","orcid":"https://orcid.org/0000-0002-8530-9968","contributorId":261669,"corporation":false,"usgs":false,"family":"Fournier","given":"Auriel","email":"","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":889856,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":889857,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Levy, Heather E.","contributorId":332275,"corporation":false,"usgs":false,"family":"Levy","given":"Heather","email":"","middleInitial":"E.","affiliations":[{"id":33355,"text":"Tall Timbers Research Station and Land Conservancy","active":true,"usgs":false}],"preferred":false,"id":889858,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cox, James A.","contributorId":332277,"corporation":false,"usgs":false,"family":"Cox","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":33355,"text":"Tall Timbers Research Station and Land Conservancy","active":true,"usgs":false}],"preferred":false,"id":889859,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kappes, Peter J.","contributorId":275193,"corporation":false,"usgs":false,"family":"Kappes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":889860,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nyman, John A.","contributorId":215127,"corporation":false,"usgs":false,"family":"Nyman","given":"John A.","affiliations":[{"id":39183,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton","active":true,"usgs":false}],"preferred":false,"id":889861,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pitchford, Jonathan L.","contributorId":301251,"corporation":false,"usgs":false,"family":"Pitchford","given":"Jonathan","email":"","middleInitial":"L.","affiliations":[{"id":52643,"text":"Grand Bay National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":889862,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70250541,"text":"70250541 - 2023 - Satellite telemetry reveals space use of diamondback terrapins","interactions":[],"lastModifiedDate":"2023-12-15T12:39:45.182424","indexId":"70250541","displayToPublicDate":"2023-12-08T06:36:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Satellite telemetry reveals space use of diamondback terrapins","docAbstract":"Movement and space use information of exploited and imperiled coastal species is critical to management and conservation actions. While satellite telemetry has been successfully used to document movements of marine turtles, the large tag sizes available have limited use on smaller turtle species. We used small Argos-based satellite tags to document movement patterns of diamondback terrapins (Malaclemys terrapin), the only estuarine turtle species in North America. Movement data from ten terrapins in St. Joseph Bay, Florida were gathered between July 13, 2018 and July 22, 2021. We estimated seasonal space use using the daily locations generated from a Bayesian hierarchical state-space model to calculate minimum convex polygons (95% MCP) and kernel density estimates (50% and 95% KDE). Mean tracking duration was 125 days and mean home range size was 9.4 km2 (95% MCP) and 8.1 km2 (95% KDE). Seagrass habitat comprised 55.8% of all home ranges on average, whereas salt marsh comprised a mean of 3.0%. Mean elevation used by terrapins was − 0.13 m (95% MCP) and -0.35 m (95% KDE). Satellite telemetry provided broad-scale spatiotemporal movement and space use data; however, Argos error produced considerable noise relative to true terrapin movements given their size, speed, and behavior. Terrapin home ranges were greater than previously reported and three of the ten terrapins exhibited repeated long-distance, directed movements within the bay. Small patches of salt marsh habitat were centralized within home ranges, despite comprising only a small percentage for each terrapin. Moreover, the percentage of salt marsh present in each core use area was positively correlated with terrapin mass. Although considered an estuarine species, seagrass habitat comprised a large portion of terrapin home ranges; however, our data did not provide the detail necessary to understand how terrapins were using this habitat. As northward-expanding mangroves continue to infringe upon salt marsh habitat, there is potential for negative impacts to terrapin populations across the northern Gulf of Mexico. As salt marsh habitat continues to be infringed upon by northward-expanding mangroves impacts to terrapins across the northern Gulf of Mexico.
Of all the ways human beings have modified the planet over the last 10,000 years, habitat loss is the most important for other species. To address this most critical threat to biodiversity, governments, non-governmental actors, and the public need to know, in near real-time, where and when habitat loss is occurring. Here we present an integrated habitat modelling system at the range-wide scale for the tiger (Panthera tigris) to measure and monitor changes in tiger habitat at range-wide, national, biome, and landscape scales, as often as the underlying inputs change. We find that after nearly 150 years of decline, effective potential habitat for the tiger seems to have stabilized at around 16% of its indigenous extent (1.817 million km2). As of the 1st of January 2020, there were 63 Tiger Conservation Landscapes in the world, covering 911,920 km2 shared across ten of the 30 modern countries which once harbored tiger populations. Over the last 20 years, the total area of Tiger Conservation Landscapes (TCLs) declined from 1.025 million km2 in 2001, a range-wide loss of 11%, with the greatest losses in Southeast Asia and southern China. Meanwhile, we documented expansions of modelled TCL area in India, Nepal, Bhutan, northern China, and southeastern Russia. We find significant potential for restoring tigers to existing habitats, identified here in 226 Restoration Landscapes. If these habitats had sufficient prey and were tigers able to find them, the occupied land base for tigers might increase by 50%. Our analytical system, incorporating Earth observations, in situ biological data, and a conservation-oriented modelling framework, provides the information the countries need to protect tigers and enhance habitat, including dynamic, spatially explicit maps and results, updated as often as the underlying data change. Our work builds on nearly 30 years of tiger conservation research and provides an accessible way for countries to measure progress and report outcomes. This work serves as a model for objective, range-wide, habitat monitoring as countries work to achieve the goals laid out in the Sustainable Development Goals, the 30×30 Agenda, and the Kunming-Montreal Global Biodiversity Framework.
Coastal restoration through island construction and augmentation is an increasingly common management method in the northern Gulf of Mexico, but evaluating the impacts to shorebird species is difficult. Shorebirds are mostly migratory and many aspects of their life history, including reproduction in some species, occur in other places. In addition, counts or observations of shorebirds made at any given time represent only a portion of the population and that proportion may change with site conditions such as time of day and weather. Dynamic occupancy models can account for imperfect detection and produce estimates of the proportion of area occupied over time as a way to track trends in bird utilization over time.
In this chapter we report on occupancy trends for five focal shorebird species from Caminada Headland and Whiskey Island: American Oystercatcher (Haematopus palliatus), Piping Plover (Charadrius melodus), Red Knot (Calidris canutus), Snowy Plover (Charadrius nivosus), and Wilson’s Plover (Charadrius wilsonia). We examined up to nine years of surveys at the two sites to model long-term trends in occupancy rates from a period spanning before, during, and after restoration. Our objective was to determine if there was change in occupancy over time (trend) during the restoration period.
Field sampling was conducted as described in Chapter 1 for the five focal species in this chapter. To create spatial units for occupancy analysis we used a grid of 53 unique cells at Caminada Headland and 26 unique cells at Whiskey Island. All observations of the species were located into spatial units using GIS and presence of each species in each cell was determined for each visit within a sampling season. Sampling seasons were defined for species as the period of time where they were most likely to be using the study area, and corresponded with wintering (August–May), breeding (April–August), or staging (March–October). We did not include covariates for initial occupancy or colonization and site survival because these were small sites with homogenous habitat. We did account for time of year within a season by treating survey date as a covariate of detection probability and allowed it to vary throughout the season. We tested for a trend in occupancy over time by determining if the estimated slope through the series of annual occupancy estimates was significantly different from 0.
We conducted 153 total surveys at Caminada Headland from 11 January 2013 to 5 June 2019, and 213 total surveys at Whiskey Island from 7 August 2012 to 19 August 2020. The number of surveys per year varied by species and site and range from 6–23 per species annually at Caminada Headland and 11–28 per species annually at Whiskey Island. Occupancy trends were able to be assessed for all species at both sites, with the exception of the American Oystercatcher, which were only observed in sufficient numbers to estimate occupancy at Whiskey Island. We found a significant positive trend in occupancy from 0.325 to 0.741 for American Oystercatcher at Whiskey Island. There was no significant trend in Piping Plover occupancy at Caminada Headland where occupancy was consistently high (0.910 to 0.947). At Whiskey Island, Piping Plover occupancy estimates increased from 0.574 to 0.908 during the study period which was a significant increase. At Caminada Headland and Whiskey Island Red Knot occupancy varied from 0.482 to 0.891 and 0.350 to 0.742, respectively, but showed no significant trend over the study period. Snowy Plover occupancy at Caminada Headland increased significantly from 0.295 to 0.785 over the study period. Snowy Plover occupancy also increased significantly at Whiskey Island from 0.442 to 0.906. Wilson’s Plover occupancy declined slightly during the study period from 0.946 to 0.935 at Caminada Headland, and from 0.858 to 0.736 at Whiskey Island, but the decline was not significant at either site.
We found no evidence that occupancy declined significantly for any of the species during the period prior to, during, and after restoration. We did find a significant increasing trend in occupancy for Snowy Plover at Caminada Headland and a significant increasing trend for American Oystercatcher, Piping Plover, and Snowy Plover at Whiskey Island. Our modeling results indicate that sampling such as this is sufficient for occupancy modeling and can provide a robust metric for comparison over time or among sites. In future research we plan to investigate the sample size needed to detect a trend. We are currently conducting a power analysis to determine the minimum amount of sampling necessary to have power to detect a trend based on the detection probabilities from this study.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Evaluation of restoration for avian species at Caminada Headland and Whiskey Island in Louisiana","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","collaboration":"University of Louisiana Lafayette, Barataria-Terrebonne National Estuary Program, Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Waddle, J., Barrow, W., Jeske, C., Schulz, J., Dobbs, R., LeBlanc, D., Anderson, A.N., Greary, B., Zenzal, T.J., Enwright, N., Thurman. Hana, and Lee, D., 2023, Site occupancy of focal shorebird species at Whiskey Island and Caminada Headland, Louisiana 2012–2020, 36 p.","productDescription":"36 p.","ipdsId":"IP-160700","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":482175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":482120,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cims.coastal.la.gov/RecordDetail.aspx?Root=0&sid=26011"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waddle, J. Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":215911,"corporation":false,"usgs":true,"family":"Waddle","given":"J. 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Jr. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":224399,"corporation":false,"usgs":true,"family":"Zenzal","given":"Theodore","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":927505,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217771,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":927506,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thurman. Hana","contributorId":350960,"corporation":false,"usgs":false,"family":"Thurman. 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This work presents a chromosome-level genome assembly and annotation from a male individual. We used PacBio long reads and HiRise scaffolding to generate a high-quality genomic assembly of 1.87 Gb distributed in 327 scaffolds, with an N50 of 279 Mb and an L50 of 3. Approximately 98.4% of the genome is contained in 14 scaffolds, with 6 large scaffolds (334–127 Mb) representing macrochromosomes and 8 small scaffolds (63–22 Mb) representing microchromosomes. Using standard gene modeling and transcriptomic data, we predicted 17,902 protein-coding genes on the genome. The repeat content is characterized by a large proportion of long interspersed nuclear elements that are relatively old. Synteny analysis revealed some microchromosomes with high repeat content are more prone to rearrangements but that both macro- and microchromosomes are well conserved across reptiles. We identified scaffold 14 as the X chromosome. This microchromosome presents perfect dosage compensation where the single X of males has the same expression levels as two X chromosomes in females. Finally, we estimated the effective population size for U. nigricaudus was extremely low, which may reflect a reduction in polymorphism related to it becoming a peninsular endemic.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gbe/evad210","usgsCitation":"Davalos-Dehullu, E., Baty, S.M., Fisher, R., Scott, P.A., Dolby, G.A., Munguia-Vega, A., and Cortez, D., 2023, Chromosome-level genome assembly of the blacktail brush lizard, Urosaurus nigricaudus, reveals dosage compensation in an endemic lizard: Genome Biology and Evolution, v. 15, no. 12, evad210, 14 p., https://doi.org/10.1093/gbe/evad210.","productDescription":"evad210, 14 p.","ipdsId":"IP-159626","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441467,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gbe/evad210","text":"Publisher Index Page"},{"id":423832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Davalos-Dehullu, Elizabeth","contributorId":332610,"corporation":false,"usgs":false,"family":"Davalos-Dehullu","given":"Elizabeth","email":"","affiliations":[{"id":79515,"text":"Centro de Ciencias Genómicas, UNAM, Mexico","active":true,"usgs":false}],"preferred":false,"id":890659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baty, Sarah M.","contributorId":332611,"corporation":false,"usgs":false,"family":"Baty","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":890660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":890661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Peter A.","contributorId":258813,"corporation":false,"usgs":false,"family":"Scott","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":52299,"text":"Dept of Ecology and Evolutionary Biology & La Kretz Center for Calif Conservation Science, Institute of the Environ & Sustainability, UCLA, 90095; West Texas A&M Univ, Dept of Life, Earth, and Environ Sciences. Canyon, Texas 79016","active":true,"usgs":false}],"preferred":false,"id":890662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dolby, Greer A. 0000-0002-5923-0690","orcid":"https://orcid.org/0000-0002-5923-0690","contributorId":222726,"corporation":false,"usgs":false,"family":"Dolby","given":"Greer","email":"","middleInitial":"A.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":890663,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Munguia-Vega, Adrian","contributorId":264559,"corporation":false,"usgs":false,"family":"Munguia-Vega","given":"Adrian","affiliations":[{"id":40855,"text":"UA","active":true,"usgs":false}],"preferred":false,"id":890664,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cortez, Diego","contributorId":332612,"corporation":false,"usgs":false,"family":"Cortez","given":"Diego","email":"","affiliations":[{"id":79515,"text":"Centro de Ciencias Genómicas, UNAM, Mexico","active":true,"usgs":false}],"preferred":false,"id":890665,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250263,"text":"70250263 - 2023 - Reservoir stratification modulates the influence of impoundments on fish mercury concentrations along an arid land river system","interactions":[],"lastModifiedDate":"2023-12-21T14:55:07.757402","indexId":"70250263","displayToPublicDate":"2023-12-05T11:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir stratification modulates the influence of impoundments on fish mercury concentrations along an arid land river system","docAbstract":"Impoundment is among the most common hydrologic alterations with impacts on aquatic ecosystems that can include effects on mercury (Hg) cycling. However, landscape-scale differences in Hg bioaccumulation between reservoirs and other habitats are not well characterized nor are the processes driving these differences. We examined total Hg (THg) concentrations of Smallmouth Bass (Micropterus dolomieu) collected from reservoir, tailrace, and free-flowing reaches along an 863 km segment of the Snake River, USA, a semiarid river with 22 impoundments along its course. Across three size-classes (putative 1-year-old, first reproductive, and harvestable sized fish), THg concentrations in reservoirs and tailraces averaged 76% higher than those in free-flowing segments. Among reservoirs, THg concentrations were highest in reservoirs with inconsistent stratification patterns, 47% higher than annually stratified, and 144% higher than unstratified reservoirs. Fish THg concentrations in tailraces immediately downstream of stratified reservoirs were higher than those below unstratified (38–130%) or inconsistently stratified (32–79%) reservoirs. Stratification regimes influenced the exceedance of fish and human health benchmarks, with 52–80% of fish from stratifying reservoirs and downstream tailraces exceeding a human consumption benchmark, compared to 6–17% where stratification did not occur. These findings suggest that impoundment and stratification play important roles in determining the patterns of Hg exposure risk across the landscape.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c04646","usgsCitation":"Willacker, J., Eagles-Smith, C., Chandler, J., Naymik, J., Myers, R., and Krabbenhoft, D.P., 2023, Reservoir stratification modulates the influence of impoundments on fish mercury concentrations along an arid land river system: Environmental Science & Technology, v. 57, no. 30, p. 21313-21326, https://doi.org/10.1021/acs.est.3c04646.","productDescription":"14 p.","startPage":"21313","endPage":"21326","ipdsId":"IP-154496","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":441470,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c04646","text":"Publisher Index Page"},{"id":435112,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94VRPSL","text":"USGS data release","linkHelpText":"Mercury in smallmouth bass from the Snake River, USA, 2013-2022"},{"id":423092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"30","noUsgsAuthors":false,"publicationDate":"2023-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":221744,"corporation":false,"usgs":true,"family":"Willacker","given":"James","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":889218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":889219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandler, Jim","contributorId":332006,"corporation":false,"usgs":false,"family":"Chandler","given":"Jim","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":889220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naymik, Jesse","contributorId":229386,"corporation":false,"usgs":false,"family":"Naymik","given":"Jesse","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":889221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myers, Ralph","contributorId":172701,"corporation":false,"usgs":false,"family":"Myers","given":"Ralph","email":"","affiliations":[{"id":12541,"text":"Idaho Power Company, P.O. Box 70, Boise ID 83707","active":true,"usgs":false}],"preferred":false,"id":889222,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":889223,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250280,"text":"sim3496 - 2023 - Surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle, Inyo and San Bernardino Counties, California","interactions":[],"lastModifiedDate":"2026-02-19T17:36:45.966647","indexId":"sim3496","displayToPublicDate":"2023-12-05T10:20:40","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3496","displayTitle":"Surficial Geologic Map of the Owlshead Mountains 30' x 60' Quadrangle, Inyo and San Bernardino Counties, California","title":"Surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle, Inyo and San Bernardino Counties, California","docAbstract":"The surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle depicts the distribution and characteristics of surficial-deposit materials and neotectonic deformation for an area of approximately 5,000 square kilometers (km2) located in the western Basin and Range Province of eastern California. The map represents a new compilation of the surficial geology that encompasses deposits within the late Pliocene to Quaternary. The map is based primarily on new mapping conducted between 2001 and 2009. Map compilation was supported by field observations distributed across the map area, combined with reference to several published and unpublished mapping sources that mostly emphasized neotectonic deformation. The surficial-deposit units included in the map follow a classification scheme that systematically denotes depositional process, relative age, and any secondary sedimentologic or morphologic characteristics. Identification, correlation, and age estimation of map units are based primarily on the relative degree of development of certain time-dependent characteristics such as surface morphology, including local dissection and surface preservation, surface clast modification, and degree of soil development; these characteristics are implicitly incorporated into unit designations. The map represents a detailed and regionally uniform synthesis of the late Neogene geology for this large area that provides a framework applicable to many interpretative studies, such as regional patterns of deposition and dissection; surface drainage development and evolution; and the distribution, style, and timing of neotectonic deformation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3496","usgsCitation":"Menges, C.M., and Cossette, P.M., 2023, Surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle, Inyo and San Bernardino Counties, California: U.S. Geological Survey Scientific Investigations Map 3496, pamphlet 46 p., 2 sheets, 1:62,500, https://doi.org/10.3133/sim3496.","productDescription":"Report: iv, 46 p.; 2 Sheet: 59.92 × 42.54 inches and 50.57 × 30.83 inches; Database; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107100","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500199,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115656.htm","linkFileType":{"id":5,"text":"html"}},{"id":423113,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3496/sim3496_database.zip","text":"Database","size":"140 MB","linkFileType":{"id":6,"text":"zip"}},{"id":423112,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3496/sim3496_sheet2.pdf","text":"Sheet 2","size":"1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":423109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3496/covrthb_.jpg"},{"id":423110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3496/sim3496_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":423111,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3496/sim3496_sheet1.pdf","text":"Sheet 1","size":"26 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.00,\n 36.00\n ],\n [\n -117.00,\n 35.30\n ],\n [\n -116.00,\n 35.30\n ],\n [\n -116.00,\n 36.00\n ],\n [\n -117.00,\n 36.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Vehicles are an important source for N deposition that may negatively impact roadside ecosystems. While elevated roadside N deposition has been found in many locations, it is not yet known if vehicle emissions cause measurable increases of N deposition in complex, mountainous terrain adjacent to roads. To address this, this study investigated the effect of vehicle N emissions on throughfall (through trees) and open N deposition in a high traffic corridor in mountainous terrain of Rocky Mountain National Park, Colorado, USA. We measured bulk (wet + dry) atmospheric N deposition in throughfall and open samplers along two transects of 750 (throughfall) and 225 (open) m moving away from the road using ion exchange resin (IER) collectors. Contrary to most studies of roadside N deposition, we found no influence of road proximity on inorganic N deposition in throughfall or open sites, possibly due to terrain complexity. Interactions with vegetation modified regional N deposition; throughfall sites had 69% higher nitrate (NO3−) deposition than open sites and larger trees were associated with higher ammonium (NH4+) deposition as compared to smaller trees. When comparing to regional sites that are part of national monitoring networks, we confirmed that our estimates were unaffected by vehicle emissions as our throughfall IER collectors had similar total inorganic N deposition as wet + dry deposition from regional sites (8.64–13.56 vs 10.72–12.14 g N ha−1 day−1, respectively). These findings do not negate vehicles as a local source of N emissions but suggest elevated N deposition adjacent to busy roads cannot be assumed for complex terrains. Instead, environmental variables may be more important drivers than proximity to roads in topographically complex ecosystems.
Chaco Culture National Historical Park (CCNHP), located in northwestern New Mexico, protects the greatest concentration of Chacoan historical sites in the American Southwest. Geologically, CCNHP is located within the San Juan structural basin, which consists in part of complex Cretaceous stratigraphy and hosts a variety of energy resources. As part of a larger study to investigate the vulnerability of water resources at CCNHP related to oil and natural gas extraction activities, a MODFLOW groundwater model of the Mancos Shale and Gallup Sandstone units was created by the U.S. Geological Survey, as a part of a cooperative study with the National Park Service, to assess advective groundwater flow paths and traveltimes. The model determined that groundwater flow directions currently trend from south-southeast to north-northwest within the vicinity of CCNHP, groundwater traveltime through the Gallup Sandstone ranges from thousands to tens of thousands of years, and traveltime through the Mancos Shale may range from millions to tens of millions of years. The capture zone of the main CCNHP well (referred to as the “Chaco well”) extends to the south-southeast and ranges in width from approximately 1 to 12 miles, depending on pumping rate. Eighteen inactive hydrocarbon related wells are located within the capture zone and within 10 kilometers of the Chaco well. Given model estimates of traveltimes of groundwater in the Gallup Sandstone aquifer, advective groundwater transport to the Chaco well would take approximately 430 years from the nearest inactive hydrocarbon related wells. Differencing of historical and modern-day potentiometric surfaces of the Gallup Sandstone indicate a drop in groundwater levels between 34 and 96 feet within the CCNHP boundaries. Hydraulic fracturing, simulated as increased hydraulic conductivity zones, decreased groundwater traveltimes (from millions to thousands of years) and acted as permeable pathways from the Mancos Shale to the Gallup Sandstone.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235097","issn":"2328-0328","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Shephard, Z.M., Ritchie, A., Linhoff, B.S., and Lunzer, J.J., 2023, Groundwater flow model investigation of the vulnerability of water resources at Chaco Culture National Historical Park related to unconventional oil and gas development: U.S. Geological Survey Scientific Investigations Report 2023–5097, 39 p., https://doi.org/10.3133/sir20235097.","productDescription":"Report: viii, 39 p.; Data Release","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-138645","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501063,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115655.htm","linkFileType":{"id":5,"text":"html"}},{"id":422266,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5097/sir20235097.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5097 XML"},{"id":422264,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5097/coverthb.jpg"},{"id":422268,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98LTTER","text":"USGS data release","linkHelpText":"MODFLOW-2005 and MODPATH models in support of groundwater flow model investigation of water resources at Chaco Culture National Historical Park: U.S. Geological Survey data release"},{"id":422267,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235097/full","description":"SIR 2023-5097 HTML"},{"id":422265,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5097/sir20235097.pdf","size":"3.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5097 pdf"},{"id":422263,"rank":1,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5097/images"}],"country":"United States","state":"Arizona, New Mexico","otherGeospatial":"Chaco Culture National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.11403857536405,\n 36.95597564044496\n ],\n [\n -109.61728071597331,\n 36.95597564044496\n ],\n [\n -109.61728071597331,\n 34.720533250530835\n ],\n [\n -106.11403857536405,\n 34.720533250530835\n ],\n [\n -106.11403857536405,\n 36.95597564044496\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Traditional methods for estimating abundance of fish populations are not feasible in some systems due to complex population structure and constraints on sampling effort. Lincoln’s estimator provides a technique that uses harvest and harvest rate to estimate abundance. Using angler catch data allows assumptions of the estimator to be addressed without relying on methods that could be prohibitively field-intensive or costly. Historic estimates of White Sturgeon Acipenser transmontanus abundance in the Sacramento–San Joaquin River basin have been obtained using mark–recapture methods; however, White Sturgeon population characteristics often cause violations of model assumptions, such as population closure and independent capture probabilities. We developed a version of Lincoln’s estimator using a joint likelihood, estimated abundance of White Sturgeon in the Sacramento–San Joaquin River basin in 2015 using this method and empirical data and assessed accuracy and precision of estimates in a simulation study. Estimating abundance using harvest and harvest rate, as represented by our model framework, has the potential to be precise and accurate. The joint likelihood–based approach fitted using Bayesian methods is advantageous because it includes all sources of variation in a single model. Precision of abundance estimates was low with application of the model to White Sturgeon in the Sacramento–San Joaquin River basin and to similar conditions in a simulated dataset. Using simulation, precision and accuracy increased with increases in the number of high-reward and standard tags released, tag reporting rate, tag retention rate, and harvest rate. Results demonstrate potential sources of error when using this approach and suggest that increasing the number of tagged fish and tag reporting rate are potential actions to improve precision and accuracy of abundance estimates of the model.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-22-057","usgsCitation":"Ulaski, M., McCormick, J., Quist, M.C., and Jackson, Z., 2023, Leveraging angler effort to inform fisheries management: Using harvest and harvest rate to estimate abundance of White Sturgeon: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 324-336, https://doi.org/10.3996/JFWM-22-057.","productDescription":"13 p.","startPage":"324","endPage":"336","ipdsId":"IP-132349","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-22-057","text":"Publisher Index Page"},{"id":430146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.73722335339393,\n 38.33070681831987\n ],\n [\n -122.73722335339393,\n 37.37437014467528\n ],\n [\n -121.27664864102258,\n 37.37437014467528\n ],\n [\n -121.27664864102258,\n 38.33070681831987\n ],\n [\n -122.73722335339393,\n 38.33070681831987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ulaski, Marta","contributorId":280108,"corporation":false,"usgs":false,"family":"Ulaski","given":"Marta","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Joshua","contributorId":337819,"corporation":false,"usgs":false,"family":"McCormick","given":"Joshua","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Zachary","contributorId":338597,"corporation":false,"usgs":false,"family":"Jackson","given":"Zachary","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":903717,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260942,"text":"70260942 - 2023 - Assessment of a new GeoAI foundation model for floodinundation mapping","interactions":[],"lastModifiedDate":"2024-11-18T16:40:32.912016","indexId":"70260942","displayToPublicDate":"2023-12-01T10:26:12","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessment of a new GeoAI foundation model for floodinundation mapping","docAbstract":"Vision foundation models are a new frontier in GeoAI research because of their potential to enable powerful image analysis by analyzing and extracting important image features from vast amounts of geospatial data. This paper evaluates the performance of the first-of-its-kind geospatial foundation model, IBM-NASA’s Prithvi, to support a crucial geospatial analysis task: flood inundation mapping. This model is compared with popular convolutional neural networks and vision transformer-based architectures regarding mapping accuracy for flooded areas. A benchmark dataset, Sen1Floods11, is used in the experiments, and the models' predictability, generalizability, and transferability are evaluated based on both validation datasets and datasets completely unseen by the model. Results show the impressive transferability of the Prithvi model, highlighting its performance advantages in segmenting flooded areas in previously unseen regions. The findings also suggest areas for improvement for the Prithvi model in adopting multi-scale representation learning, developing more end-to-end pipelines for high-level image analysis tasks, and offering more flexibility in allowable input data bands.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23)","conferenceDate":"November 13, 2023","conferenceLocation":"Hamburg Germany","language":"English","publisher":"Association for Computing Machinery","doi":"10.1145/3615886.3627747","usgsCitation":"Li, W., Lee, H., Wang, S., Hsu, C., and Arundel, S., 2023, Assessment of a new GeoAI foundation model for floodinundation mapping, in Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23), Hamburg Germany, November 13, 2023, p. 102-109, https://doi.org/10.1145/3615886.3627747.","productDescription":"8 p.","startPage":"102","endPage":"109","ipdsId":"IP-157614","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":467071,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/2309.14500","text":"External Repository"},{"id":464232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Hyunho","contributorId":346310,"corporation":false,"usgs":false,"family":"Lee","given":"Hyunho","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Sizhe","contributorId":242975,"corporation":false,"usgs":false,"family":"Wang","given":"Sizhe","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsu, Chia-Yu","contributorId":302720,"corporation":false,"usgs":false,"family":"Hsu","given":"Chia-Yu","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":918651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259400,"text":"70259400 - 2023 - A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework","interactions":[],"lastModifiedDate":"2024-10-07T15:11:26.345198","indexId":"70259400","displayToPublicDate":"2023-12-01T10:02:12","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework","docAbstract":"This document serves to fulfill the Coregonine Threats Assessment Science Team’s charge of providing a written recommendation for a methodology to conduct threats assessments for Great Lakes coregonines within the Coregonine Restoration Framework (CRF). Through a series of team meetings that included presentations by experts on five candidate threats assessment frameworks followed by structured deliberations, we came to consensus to recommend the threats assessment framework used by Fisheries and Oceans Canada under Canada’s Species at Risk Act, with three modifications: (1) a conceptual modeling step, (2) the use of a “point spreading” approach to incorporate uncertainty when scoring threats, and (3) the use of a modified Delphi or “estimate-talk-estimate” approach when scoring key elements in the assessment. We recommend that this approach be applied to the spatial units delineated by the CRF Resolve Taxonomy and Gap Analysis science teams. In brief, the assessment process includes providing background information on the spatial unit and threats under assessment, constructing a conceptual model linking threats to key processes and vital rates, and scoring or ranking threats across six elements: likelihood of occurrence, level of impact, strength of evidence, unit-level threat occurrence, unit-level threat frequency, and unit-level threat extent. We provide detailed instructions for completing each step of the assessment and generating associated results, with particular attention paid to our suggested modifications.
The Coregonine Threats Assessment Science Team also conducted two test runs to assess the applicability and effectiveness of our recommended framework for Great Lakes coregonine populations and their threats. We conducted these test runs on two examples of Great Lakes coregonines that represented two extremes of data availability, as well as two different management contexts. We chose Kiyi (Coregonus kiyi) in Lake Ontario as an example of a data-poor, extirpated population, and we chose Cisco (Coregonus artedi) in Lake Superior as an example of a data-rich, extant population. We provide the results of these test runs in Appendices 1-2. We also describe the lessons we learned from these test runs throughout this document and highlighted them in the “Recommendations for avoiding challenges during application” section.
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U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Three-dimensional (3D) geologic and temperature models have been developed for the onshore U.S. Gulf Coast. The results from these models identify areas of moderate- to high-temperature (90°-150°C and >150°C; respectively) geothermal resources at depths <6 km. This modeling study addresses the fundamental challenge of predicting where opportune temperature and lithology coincide. Unlike traditional geothermal systems with surface expressions of hydrothermal circulation (e.g., hot springs, fumaroles, sinter), sedimentary geothermal systems (SGS) are generally hidden. Historically, simplified efforts to predict subsurface temperatures in sedimentary basins have focused on linear temperature extrapolation that does not consider the variable thermal properties of different lithologies or lithologic changes with depth (e.g., compaction, lithification). Therefore, the need to understand basin architecture and predict temperatures in 3D within SGS is paramount to identifying geothermal resources and determining economic feasibility. Basin modeling software has long been used to characterize the subsurface conditions of sedimentary basins, including temperature, in the pursuit of finding hydrocarbons. This tool can also be adapted to evaluate the potential of geothermal resources in a sedimentary basin by predicting the confluence of desirable temperatures and reservoir lithologies. In this work, PetroMod basin modeling software was used to create a regional geologic model of the onshore U.S. Gulf Coast, covering over 500,000 km2 calibrated to temperature data from wells. Inputs include structural surfaces from commercial databases, lithology information derived from published literature, and corrected bottom-hole temperatures (BHT) from over 6,000 wells. The resulting 3D geologic model can be used to predict temperatures throughout the basin. Maps were exported showing the depth, depositional unit, and reservoir lithology at which temperatures of 90°C and 150°C were reached, revealing over 400,000 km2 of moderate- to high-temperature resources at depths <6 km. These maps function as a first-order screening tool to identify areas where low-, moderate-, or high-grade resource potential may exist, based on temperature and if optimal reservoir lithologies or depositional units of interest are present. Depending on the success criteria of a project, the same maps can be exported for any isotherm or incorporate other 1407 Gardner and Birdwell subsurface properties. The methodology employed in this work can be applied in any sedimentary basin with available subsurface data. Further calibration incorporating other data, including pressure and porosity, can expand the utility of basin modeling for geothermal evaluations. Basin modeling is a powerful but underutilized tool for identifying prospective geothermal resources in sedimentary basins.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geothermal Rising Conference","conferenceDate":"October 1-3, 2023","conferenceLocation":"Reno, NV","language":"English","publisher":"Geothermal Rising","usgsCitation":"Gardner, R., and Birdwell, J.E., 2023, Hidden system identification: Basin modeling as a tool for examining sedimentary geothermal resource potential, in Using the Earth to save the Earth, v. 47, Reno, NV, October 1-3, 2023, p. 1407-1414.","productDescription":"8 p.","startPage":"1407","endPage":"1414","ipdsId":"IP-155363","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":495157,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034802","linkFileType":{"id":5,"text":"html"}},{"id":495216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, Rand 0000-0001-8711-5334","orcid":"https://orcid.org/0000-0001-8711-5334","contributorId":316831,"corporation":false,"usgs":true,"family":"Gardner","given":"Rand","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":947936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257118,"text":"70257118 - 2023 - USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling","interactions":[],"lastModifiedDate":"2024-08-12T14:44:46.763484","indexId":"70257118","displayToPublicDate":"2023-12-01T09:41:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling","docAbstract":"No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Monitoring and Response Plan","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee (invasivecarp.us)","usgsCitation":"Brey, M.K., Jackson, P.R., Stanton, J.C., and Fritts, A.K., 2023, USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-151295","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":432459,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}},{"id":432485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261284,"text":"70261284 - 2023 - Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan","interactions":[],"lastModifiedDate":"2024-12-04T15:39:20.098701","indexId":"70261284","displayToPublicDate":"2023-12-01T09:33:40","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan","docAbstract":"The Great Lakes Tectonic Zone (GLTZ) forms the boundary between the Wawa-Abitibi subprovince (north side) and Minnesota River Valley subprovince (south side) within the Archean Superior Province. The GLTZ is concealed for all of its 1100 km length, except south of Marquette in the central Upper Peninsula of Michigan (Sims, 1991; Sims and Day, 1993). Near KI Sawyer, it is exposed as a NW-striking, 2.3 km wide mylonite zone along a strike length of about 11 km, with a mylonitic foliation that dips steeply to the SW (Sims, 1993). The location extent of the GLTZ is unknown to the east where it is concealed beneath Paleozoic sedimentary rocks. We use legacy aeromagnetic data (Daniels et al., 2009) in combination with modern aeromagnetic data (Drenth and Brown, 2020) and ground gravity data to geophysically characterize the GLTZ and map its eastward extent under cover and map additional nearby covered Precambrian tectonic elements.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Institute on Lake Superior Geology proceedings, 69th annual meeting, Eau Claire, Wisconsin, part 1 - Abstracts and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Drenth, B.J., and Cannon, W.F., 2023, Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan, in Institute on Lake Superior Geology proceedings, 69th annual meeting, Eau Claire, Wisconsin, part 1 - Abstracts and proceedings, p. 27-28.","productDescription":"2 p.","startPage":"27","endPage":"28","ipdsId":"IP-151526","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":464752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464741,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcollections.lakeheadu.ca/exhibits/show/ilsg/item/8207"}],"country":"United States","state":"Michigan","otherGeospatial":"Upper Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6,\n 46.5\n ],\n [\n -87.6,\n 46\n ],\n [\n -86.5,\n 46\n ],\n [\n -86.5,\n 46.5\n ],\n [\n -87.6,\n 46.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":920217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":920218,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264392,"text":"70264392 - 2023 - A management-focused population viability analysis for North Atlantic right whales","interactions":[],"lastModifiedDate":"2025-03-14T14:18:14.086582","indexId":"70264392","displayToPublicDate":"2023-12-01T09:10:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NMFS-NEFSC 307","title":"A management-focused population viability analysis for North Atlantic right whales","docAbstract":"The North Atlantic right whale (Eubalaena glacialis) is among the most endangered whale species in the world and has been in decline since 2010. Considerable effort is directed toward its recovery by striving to remove threats. In this report, we describe the development of a population viability analysis for right whales that is designed to assess the current status, evaluate the contributions of various threats, and explore the management interventions needed to achieve recovery. The individual-based model that underlies this analysis accounts for age- and stagespecific survival and reproductive rates, the effects of severe injury from entanglement or vessel strike, and future changes in prey availability and accessibility. Several new or updated empirical analyses supplied parameter estimates, and parametric uncertainty was carefully incorporated into the model results.
We find that under the status quo conditions of 2019, prior to the enactment of new regulations by the U.S. and Canada after 2020, the North Atlantic right whale population would be expected to continue to fall, with a median decline of 75% in 100 years (95% projection interval, –98% to +9% change) and a probability of falling below 50 proven females of 0.934 in 100 years. If the recently enacted regulations reduce entanglement risk by 25%, however, the population would be expected to decrease by 42% over 100 years (95% projection interval –92% to +154% change), with a risk of falling below 50 proven females in 100 years of 0.705. If, instead, the recently enacted regulations reduce entanglement risk by 50%, the population would be expected to increase by 52% in 100 years (95% projection interval –83% to +497% change), with a probability of falling below 50 proven females of 0.349.
Of the 3 primary threats explored in this analysis, the risk of entanglement contributes the most to the long-term risk of quasi-extinction, followed closely by the risk of vessel strike, and much more distantly by a decrease in prey availability. In hypothetical scenarios that fully remove one threat at a time, removal of the entanglement threat alone reduces the probability of falling below 50 proven females in 100 years from 0.934 to 0.053; removal of the vessel strike threat alone reduces it to 0.343; and a return to higher prey conditions, but with both human-related threats still in place, reduces it to 0.875.
We explored a wide range of management intervention scenarios that changed the rate of entanglement risk (e.g., endline reductions, closures, implementation of ropeless/on-demand gear); the effect of entanglement (through use of weak rope technology); the rate of vessel traffic increase over time; and the severity of vessel strike risk through speed restrictions. We found, for example, that reducing entanglement risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.705; reducing vessel strike risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.846; but the combination of reducing both entanglement risk and vessel strike risk by 25% reduces the risk of quasi-extinction to 0.528.
This model and the results it produced are meant to represent an assessment of the current status of North Atlantic right whales using the best available scientific and commercial data and state-of-the-art analytical tools. Our knowledge of the future of the right whale population, however, has limitations. We have endeavored to fully incorporate uncertainty into this model, but there are many areas for continued improvement. We view this model as a living tool that can be improved, adapted, and extended as new data, new methods, and new questions arise.
","language":"English","publisher":"National Oceanic and Atmospheric Administration","doi":"10.25923/dqp2-2r71","usgsCitation":"Runge, M.C., Linden, D., Hostetler, J.A., Borggaard, D., Garrison, L.P., Knowlton, A., Lesage, V., Williams, R., and Pace, R., 2023, A management-focused population viability analysis for North Atlantic right whales: NOAA Technical Memorandum NMFS-NEFSC 307, 93 p., https://doi.org/10.25923/dqp2-2r71.","productDescription":"93 p.","ipdsId":"IP-144310","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linden, Daniel W.","contributorId":229525,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":930630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":930631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borggaard, Diane L","contributorId":352275,"corporation":false,"usgs":false,"family":"Borggaard","given":"Diane L","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrison, Lance P.","contributorId":296893,"corporation":false,"usgs":false,"family":"Garrison","given":"Lance","email":"","middleInitial":"P.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":930633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowlton, Amy R.","contributorId":352046,"corporation":false,"usgs":false,"family":"Knowlton","given":"Amy R.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":930634,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lesage, Véronique","contributorId":352276,"corporation":false,"usgs":false,"family":"Lesage","given":"Véronique","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":930635,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams, Robert A. 0000-0002-2973-8493","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":203802,"corporation":false,"usgs":false,"family":"Williams","given":"Robert A.","affiliations":[{"id":36721,"text":"USGS-Emeritus","active":true,"usgs":false}],"preferred":false,"id":930636,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pace, Richard M III","contributorId":352277,"corporation":false,"usgs":false,"family":"Pace","given":"Richard M","suffix":"III","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930637,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267462,"text":"70267462 - 2023 - Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria","interactions":[],"lastModifiedDate":"2025-05-23T14:09:15.876291","indexId":"70267462","displayToPublicDate":"2023-12-01T09:08:54","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria","docAbstract":"Identifying and analyzing closed depressions in karst areas is important for sinkhole hazard evaluation and land management. We created a sinkhole inventory in the karst region of northwest Puerto Rico using a lidar-derived elevation model acquired in 2018 approximately eleven months after Hurricane Maria. The goal of this project is to develop a geodatabase of sinkhole feature classes (polygons and points), relevant geometric attributes of each feature, and a density raster to portray areas of greater clustering of sinkholes as an input for future sinkhole susceptibility assessment. We used ArcGIS Pro® v3.0 to create closed depression polygons using two semi-automated extraction methods. A fill-difference method was used to capture depressions nine square meters and larger, and a contour tree method was used to capture nested depressions larger than one hundred square meters. Quality checks were conducted to eliminate non-karst depressions, such as human-made depressions and those resulting as artifacts from the automated methods. Geospatial data of land cover, soils, and geology helped to refine the results and improve quality control. The most challenging aspect of this effort was determining a true karst sinkhole from other depressions extracted from the lidar-derived elevation model. Limitations of this semi-automated method include false-positive depressions in the automated results and the exclusion of sinkholes in conducting large-scale eliminations based on landscape attributes. We approached this challenge by combining layers of other geospatial information to evaluate the type of process that could result in a closed depression. This project will help develop an efficient method to visualize karst hazards utilizing lidar-derived elevation models and sinkhole geomorphic expressions. The resulting geodatabase can be used to efficiently identify sinkhole susceptibility and support land management decision-making in karst areas.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th multidisciplinary conference on sinkholes and the engineering and environmental impacts of karst","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"National Cave and Karst Research Institute","usgsCitation":"Smith, L., Doctor, D.H., and Cox, C., 2023, Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria, in Proceedings of the 17th multidisciplinary conference on sinkholes and the engineering and environmental impacts of karst, v. 17, p. 239-248.","productDescription":"10 p.","startPage":"239","endPage":"248","ipdsId":"IP-148086","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":486487,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcommons.usf.edu/sinkhole_2022/ProceedingswithProgram/"},{"id":486498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.30291697689199,\n 18.559923243339426\n ],\n [\n -67.30291697689199,\n 18.330982046375794\n ],\n [\n -66.86489408952299,\n 18.228131112250153\n ],\n [\n -66.11901997545601,\n 18.330982046375794\n ],\n [\n -66.11901997545601,\n 18.559923243339426\n ],\n [\n -67.30291697689199,\n 18.559923243339426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Lillian G.","contributorId":355868,"corporation":false,"usgs":false,"family":"Smith","given":"Lillian G.","affiliations":[{"id":36213,"text":"University of Redlands","active":true,"usgs":false}],"preferred":false,"id":938308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cox, Cheyenne L.","contributorId":355869,"corporation":false,"usgs":false,"family":"Cox","given":"Cheyenne L.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":938310,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265060,"text":"70265060 - 2023 - Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023","interactions":[],"lastModifiedDate":"2025-04-01T14:07:00.224393","indexId":"70265060","displayToPublicDate":"2023-12-01T09:02:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023","docAbstract":"Red Knots (Calidris canutus rufa) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). We conducted a mark-recapture-resight investigation to estimate the passage population of Red Knots at Delaware Bay in 2023. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The 2023 passage population size was estimated at 39,361 (95% credible interval: 33,724–47,556). Although there is broad overlap in the credible intervals for population estimates from 2020–2023, the population estimate for 2023 was below 40,000 birds for only the second time since 2011. Horseshoe crabs have been harvested for use as bait in eel (Anguilla rostrata) and whelk (Busycon) fisheries since at least 1990. In the late 1990s and early 2000s, the number of Red Knots counted during aerial surveys at Delaware Bay declined from ~50,000 to ~13,000 and some avian conservation biologists hypothesized that horseshoe crab harvest levels in the 1990s prevented sufficient refueling for successful migration to the Arctic breeding grounds, reproduction, and survival for the remainder of the annual cycle. Since 2013, the harvest of horseshoe crabs in the Delaware Bay region has been managed using an Adaptive Resource Management (ARM) framework. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat for Red Knots and other migrating shorebirds. For annual harvest recommendations, the ARM framework requires annual estimates of horseshoe crab population size and the Red Knot stopover population size. The 2023 population size estimate will inform harvest recommendations in the next management cycle for decision making by the Atlantic States Marine Fisheries Commission.
","language":"English","publisher":"Delaware Division of Fish and Wildlife","usgsCitation":"Lyons, J.E., 2023, Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-159297","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":484058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484043,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.alpha.delaware.gov/fish-wildlife/conservation/shorebirds/research/"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.08367028503963,\n 38.719590525784895\n ],\n [\n -74.91863610562206,\n 38.97012711441013\n ],\n [\n -74.8543007475435,\n 39.15256976333487\n ],\n [\n -75.3829695595777,\n 39.44695257658836\n ],\n [\n -75.51443746521609,\n 39.64539134375613\n ],\n [\n -75.65429693929858,\n 39.619540213656876\n ],\n [\n -75.56198968640427,\n 39.36698574058866\n ],\n [\n -75.42772459128453,\n 39.14606207448017\n ],\n [\n -75.3745779911329,\n 38.9679523390096\n ],\n [\n -75.08367028503963,\n 38.719590525784895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":932440,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250953,"text":"70250953 - 2023 - Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies","interactions":[],"lastModifiedDate":"2024-01-13T14:57:56.409648","indexId":"70250953","displayToPublicDate":"2023-12-01T08:55:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies","docAbstract":"The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.