Evaluation of tsunami disaster risk for a coastal region requires reliable estimation of tsunami hazard, for example, wave amplitude close to the shore. Observed tsunami data are scarce and have poor spatial coverage, and for this reason probabilistic tsunami hazard analysis (PTHA) traditionally relies on numerical simulation of “synthetic” tsunami generation and propagation toward the coast. Such an approach has been extensively studied in the past and it is widely recognized as an important disaster-risk mitigation tool. PTHA can not only provide less uncertain and spatially coherent hazard estimates in comparison with classical empirical data analysis which is restricted at the tide gauge stations, but also local inundation information. In this paper, we explore a purely statistical alternative to traditional PTHA for evaluation of tsunami amplitude hazard. Here, we use tide gauge measurements of tsunami amplitude along the western United States, specifically California and Oregon, and develop a spatial Bayesian hierarchical model (BHM) to assess tsunami hazard from far-field earthquake sources at various recurrence intervals. The configuration of our model incorporates latent Gaussian fields that utilize information on the distance between tide gauges as well as on the continental shelf width, that is, a covariate linked to potential dissipative effects on wave energy as the tsunami travels over shallow water. Through our BHM, we produce spatially continuous probabilistic maps of far-field tsunami hazard which can aid comprehensive tsunami disaster risk reduction and management.
Monroe County is in southeastern West Virginia, encompassing an area of 474 square miles. The area consists of karst and siliciclastic aquifers of Ordovician, Silurian, Devonian, and Mississippian age and is in parts of two physiographic provinces: the Valley and Ridge Province to the east of Peters Mountain, and the Appalachian Plateau Province to the west of Peters Mountain. This study was developed in response to inquiries from the Monroe County Commission requesting assessment of the water resources of the county to better understand the quantity of the county’s groundwater resources, for both current [2023] and future demand, and to provide information to support protection and management of the county’s valuable groundwater resources.
Various products were developed for this study that provide knowledge with respect to water availability and contamination susceptibility of the karst aquifers within the county. U.S. Geological Survey (USGS) geologists conducted extensive geologic mapping in support of the project, producing (1) a countywide bedrock geologic map, (2) a countywide hydrogeologic map, and (3) a light detection and ranging (lidar)-derived countywide digital elevation model and associated sinkhole map. A significant part of this work was to map in detail the Greenbrier Group at the formation level, which prior to this study had only partially been completed. The report also includes (4) a description of the lithologic units identified as part of the geologic mapping process.
U.S. Geological Survey hydrologists completed several additional products for the hydrology part of the effort, including development of (1) a countywide potentiometric surface (water-table) map, (2) a countywide base-flow stream assessment, (3) countywide water-budget estimates, (4) well log surveys for 15 wells to better understand subsurface controls on groundwater flow within the study area, (5) two groundwater tracer tests to better refine the groundwater divide from the northern and southern parts of the karst aquifer in Monroe County; and finally, based on all available data collected for the study including the potentiometric surface map, geologic map, current [2023] and legacy fluorometric groundwater tracer tests, and base-flow stream assessments, (6) groundwater-basin delineations were reassessed for principal groundwater basins within the Greenbrier aquifer.
In Monroe County, four principal hydrogeologic settings produce large yields of water for residential, agricultural, and other uses. The most relied upon water-bearing zone with respect to current [2023] public water supply is from springs along Peters Mountain. These springs are derived from intervals of fractured sandstone and resultant alluvial deposits. Groundwater flows downslope through these permeable alluvial deposits and discharges at the contact with less permeable strata, such as the Reedsville Shale. The second most relied upon water-bearing zone in Monroe County is within the karstic Greenbrier Group aquifer, in which the basal Hillsdale Limestone overlies the less permeable Maccrady Shale. This geologic contact between the Hillsdale Limestone and Maccrady Shale is not only targeted as a source of water for agricultural supply but also is targeted as a source of water for residential supply. The third most relied upon water-bearing zone is composed of shallow perched aquifers within the Greenbrier Group. The discontinuous nature of these perched aquifers makes mapping their extent impossible, but they are related to permeable geologic strata, such as karstified limestones with solutionally enhanced permeability that overlies less permeable shale or chert bedrock. During geologic mapping of the county, several of these perched aquifers were documented in the Pickaway, Union, and Alderson Limestones. A fourth zone consists of springs from Ordovician carbonates at the base of Peters Mountain, which are influenced by sinking streams as well as upwelling along faults. In terms of water quantity, the most sustainable springs are those having deeper-sourced flows.
Public supplies are a principal source of water used for residential and commercial supply in the region, accounting for 0.49 million gallons per day (Mgal/d) of fresh-water withdrawals (0.14 Mgal/d of groundwater and 0.35 Mgal/d of surface water) for residential and commercial use and serving 6,645 individuals (49.2 percent of the population). An estimated 6,861 people, (50.8 percent of the population) primarily rely on private wells or other unregulated sources, such as springs, and withdraw 0.55 Mgal/d of groundwater for their residential use. Public water supply in the region is primarily (71.4 percent) derived from springs and augmented by stream withdrawals (backup sources mainly during low-flow periods), with the remaining portion (28.6 percent) derived from groundwater withdrawals from wells. For rural residents, however, 100 percent of their withdrawals are derived from groundwater (wells or springs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235121","isbn":"978-1-4113-4541-6","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection, the West Virginia Department of Health & Human Resources, and the Monroe County Commission","usgsCitation":"Kozar, M.D., Doctor, D.H., Jones, W.K., Chien, N., Cox, C.E., Orndorff, R.C., Weary, D.J., Weaver, M.R., McAdoo, M.A., and Parker, M., 2023, Hydrogeology, karst, and groundwater availability of Monroe County, West Virginia: U.S. Geological Survey Scientific Investigations Report 2023–5121, 82 p., https://doi.org/10.3133/sir20235121.","productDescription":"Report: xii, 81 p.; 4 Appendixes, 5 Data Releases","numberOfPages":"81","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-153904","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Introduction: Forecasting range shifts in response to climate change requires accurate species distribution models (SDMs), particularly at the margins of species' ranges. However, most studies producing SDMs rely on sparse species occurrence datasets from herbarium records and public databases, along with random pseudoabsences. While environmental covariates used to fit SDMS are increasingly precise due to satellite data, the availability of species occurrence records is still a large source of bias in model predictions. We developed distribution models for hybridizing sister species of western and eastern Joshua trees (Yucca brevifolia and Y. jaegeriana, respectively), iconic Mojave Desert species that are threatened by climate change and habitat loss.
Methods: We conducted an intensive visual grid search of online satellite imagery for 672,043 0.25 km2 grid cells to identify the two species' presences and absences on the landscape with exceptional resolution, and field validated 29,050 cells in 15,001 km of driving. We used the resulting presence/absence data to train SDMs for each Joshua tree species, revealing the contemporary environmental gradients (during the past 40 years) with greatest influence on the current distribution of adult trees.
Results: While the environments occupied by Y. brevifolia and Y. jaegeriana were similar in total aridity, they differed with respect to seasonal precipitation and temperature ranges, suggesting the two species may have differing responses to climate change. Moreover, the species showed differing potential to occupy each other's geographic ranges: modeled potential habitat for Y. jaegeriana extends throughout the range of Y. brevifolia, while potential habitat for Y. brevifolia is not well represented within the range of Y. jaegeriana.
Discussion: By reproducing the current range of the Joshua trees with high fidelity, our dataset can serve as a baseline for future research, monitoring, and management of this species, including an increased understanding of dynamics at the trailing and leading margins of the species' ranges and potential for climate refugia.
The use of species distribution models has proliferated, providing insights for sustainable management of migratory species in a globally changing environment. However, many of these models are based on statistical relationships developed from historical conditions that may not perform well under changing or even analogous conditions caused by climate change. In this paper, we used a mechanistic species distribution model called GR3D (Global Repositioning Dynamics for Diadromous Fish Distribution) to examine the integrated dynamics of American shad (Alosa sapidissima) populations across their native range along the Eastern U.S. coast, where the species demonstrates latitudinal variations in life histories and reproductive strategies. The initial design of the model was adapted to incorporate region-specific parameterization to fit the species ecology. Then, a sensitivity analysis was performed to test the influences of uncertain processes regarding American shad distribution at sea, straying and reproduction on key characteristics of the species distribution. The sensitivity analysis showed the influence of the Allee effect (i.e., “depensatory” process) and the homing rate (i.e., fidelity to the breeding sites) on the probability of presence and abundances among catchments and metapopulations estimated by the model. Contrary to the homing rate, the distance of straying did not change the estimated number of metapopulations or abundances. Homing strength, however, was quite influential. The integration of complex migration patterns during the marine phase (i.e., wintering and summering offshore areas) provided more likely estimates of the species' overall distribution. Overall, our study illustrated the utility of incorporating factors governing the large-scale distribution of migratory species to improve local management.
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) estimates source characteristics of significant damaging earthquakes, aiming to place events within their seismotectonic framework. Contextualizing the 8 September 2023, Mw 6.8 Al Haouz, Morocco, earthquake is challenging, because it occurred in an enigmatic region of active surface faulting, and low seismicity yet produced significant damage and loss of life. Here, we present the rapid earthquake source products produced by the USGS NEIC, describing how the source model was derived using both seismic and geodetic observations. Our analysis indicates that the earthquake was the result of oblique‐reverse faulting in the lower crust on either a steeply north‐dipping fault or a moderately south‐dipping fault. Finite‐slip models using seismic and geodetic data reveal a compact source, with slip occurring at depths of 15–35 km. The causative fault is not apparent, because the rupture did not break the surface, and it is not possible to definitively attribute the earthquake to a known structure. The earthquake centroid depth of 25 km is noteworthy, because it shows slip extending beyond common estimates of seismogenic depth. This earthquake highlights that the seismogenic processes associated with mountain building in this wide plate boundary region are poorly understood.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230040","usgsCitation":"Yeck, W.L., Hatem, A.E., Goldberg, D.E., Barnhart, W.D., Jobe, J.A., Shelly, D.R., Villasenor, A., Benz, H., and Earle, P.S., 2023, Rapid Source Characterization of the 2023 Mw 6.8 Al Haouz, Morocco, Earthquake: The Seismological Record, v. 3, no. 4, p. 357-366, https://doi.org/10.1785/0320230040.","productDescription":"10 p.","startPage":"357","endPage":"366","ipdsId":"IP-159241","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":441434,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230040","text":"Publisher Index Page"},{"id":427297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Morocco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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wbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":294678,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":897812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897813,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. 0000-0003-2783-5158 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USGS","active":true,"usgs":false}],"preferred":false,"id":897816,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897817,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253008,"text":"70253008 - 2023 - Do seeding and seedling planting result in similar restored plant communities?","interactions":[],"lastModifiedDate":"2024-04-16T15:31:55.690301","indexId":"70253008","displayToPublicDate":"2023-12-11T10:29:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Do seeding and seedling planting result in similar restored plant communities?","docAbstract":"Restoration practitioners often face a tradeoff between low cost but risky seeding vs expensive but more reliable seedling planting to meet revegetation goals. Knowing under what environmental and management conditions direct seeding vs seedling planting benefit different species could improve restoration practice.
We compared seed emergence to planted-seedling survival among perennial herbaceous species commonly used in restoration across eight experimental restoration sites on the Colorado Plateau, USA. We used linear models to assess relationships between emergence and survival among species, and to assess the effects of site climate and seeding pre-treatments on those relationships.
We found that among species, emergence was positively correlated with survival in the cooler sites, meaning that species with high emergence also had high survival and vice versa, but had no relationship in the hottest sites. Furthermore, pre-treatments to enhance soil moisture in seeded plots, specifically microtopography (pits) and mulch, also resulted in positive relationships between emergence and survival among species, while seeding without additional soil pre-treatments did not. Seedling planting cost 14 times as much as direct seeding alone, dropping to nine times as much when pre-treatments were combined with seeding.
Taken together, these results suggest that investments in seedling planting at hotter dryland sites, or in creating microtopography or mulching prior to seeding across sites, are likely to promote establishment success compared to simple seeding methods in degraded dryland ecosystems. These findings also identify opportunities for hybrid seeding and planting approaches that balance tradeoffs between risk and cost, respectively.
","language":"English","publisher":"Wiley","doi":"10.1111/avsc.12758","usgsCitation":"Butterfield, B.J., and Munson, S.M., 2023, Do seeding and seedling planting result in similar restored plant communities?: Applied Vegetation Science, v. 26, no. 4, e12758, 8 p., https://doi.org/10.1111/avsc.12758.","productDescription":"e12758, 8 p.","ipdsId":"IP-152987","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":427815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":898908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250482,"text":"70250482 - 2023 - Panel review of Ground Motion Characterization Model in 2023 NSHM","interactions":[],"lastModifiedDate":"2023-12-13T12:57:53.52112","indexId":"70250482","displayToPublicDate":"2023-12-11T06:56:26","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Panel review of Ground Motion Characterization Model in 2023 NSHM","docAbstract":"The 2023 National Seismic Hazard Model (NSHM; Petersen et al., 2023) has two major components – a seismic source characterization (SSC) model and a ground motion characterization (GMC) model. The US Geological Survey (USGS) established separate panels to review and provide input on these two models. Both panels are advisory, meaning that they provide input on technical issues for consideration by the USGS NSHM team, but they do not have decision making authority. Here, we report on the activities and recommendations of the Ground Motion Characterization Panel, made up of the authors of this review. Final modeling decisions are presented in separate USGS documents, including Petersen et al., (2023) and Moschetti et al., (2023). Where modeling decisions depart from our recommendations, the rationale is explained in those publications.
Mangroves and saltmarshes are biogeochemical hotspots storing carbon in sediments and in the ocean following lateral carbon export (outwelling). Coastal seawater pH is modified by both uptake of anthropogenic carbon dioxide and natural biogeochemical processes, e.g., wetland inputs. Here, we investigate how mangroves and saltmarshes influence coastal carbonate chemistry and quantify the contribution of alkalinity and dissolved inorganic carbon (DIC) outwelling to blue carbon budgets. Observations from 45 mangroves and 16 saltmarshes worldwide revealed that >70% of intertidal wetlands export more DIC than alkalinity, potentially decreasing the pH of coastal waters. Porewater-derived DIC outwelling (81 ± 47 mmol m−2 d−1 in mangroves and 57 ± 104 mmol m−2 d−1 in saltmarshes) was the major term in blue carbon budgets. However, substantial amounts of fixed carbon remain unaccounted for. Concurrently, alkalinity outwelling was similar or higher than sediment carbon burial and is therefore a significant but often overlooked carbon sequestration mechanism.
Compared to non-urban environments, cities host ecological communities with altered taxonomic diversity and functional trait composition. However, we know little about how these urban changes take shape over time. Using historical bee (Apoidea: Anthophila) museum specimens supplemented with online repositories and researcher collections, we investigated whether bee species richness tracked urban and human population growth over the past 118 years. We also determined which species were no longer collected, whether those species shared certain traits, and if collector behavior changed over time. We focused on Wake County, North Carolina, United States where human population size has increased over 16 times over the last century along with the urban area within its largest city, Raleigh, which has increased over four times. We estimated bee species richness with occupancy models, and rarefaction and extrapolation curves to account for imperfect detection and sample coverage. To determine if bee traits correlated with when species were collected, we compiled information on native status, nesting habits, diet breadth, and sociality. We used non-metric multidimensional scaling to determine if individual collectors contributed different bee assemblages over time. In total, there were 328 species collected in Wake County. We found that although bee species richness varied, there was no clear trend in bee species richness over time. However, recent collections (since 2003) were missing 195 species, and there was a shift in trait composition, particularly lost species were below-ground nesters. The top collectors in the dataset differed in how often they collected bee species, but this was not consistent between historic and contemporary time periods; some contemporary collectors grouped closer together than others, potentially due to focusing on urban habitats. Use of historical collections and complimentary analyses can fill knowledge gaps to help understand temporal patterns of species richness in taxonomic groups that may not have planned long-term data.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.17060","usgsCitation":"Ruzi, S., Youngsteadt, E., Cherveny, A., Kettenbach, J., Levenson, H., Carley, D., Collazo, J.A., and Irwin, R., 2023, Bee species richness through time in an urbanizing landscape of the southeastern United State: Global Change Biology, v. 30, no. 1, e17060, 18 p., https://doi.org/10.1111/gcb.17060.","productDescription":"e17060, 18 p.","ipdsId":"IP-157583","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":481065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.17060","text":"Publisher Index Page"},{"id":480993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","county":"Wake County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.89,35.8676],[-78.8298,35.8689],[-78.8056,35.9281],[-78.7609,35.9176],[-78.751,35.9307],[-78.7372,35.941],[-78.714,35.9729],[-78.7009,36.0068],[-78.6985,36.0131],[-78.7048,36.0091],[-78.7077,36.0087],[-78.7076,36.0132],[-78.7052,36.0223],[-78.7085,36.0287],[-78.7102,36.0287],[-78.713,36.0278],[-78.7164,36.0283],[-78.7232,36.0334],[-78.726,36.0343],[-78.7272,36.0334],[-78.7278,36.0289],[-78.7324,36.0267],[-78.7353,36.0199],[-78.7422,36.0209],[-78.75,36.026],[-78.7551,36.0283],[-78.7545,36.0301],[-78.7511,36.0323],[-78.7499,36.035],[-78.747,36.0395],[-78.7492,36.0427],[-78.7503,36.0468],[-78.7519,36.0491],[-78.7564,36.0532],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218]]]},\"properties\":{\"name\":\"Wake\",\"state\":\"NC\"}}]}","volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruzi, Selina A.","contributorId":349803,"corporation":false,"usgs":false,"family":"Ruzi","given":"Selina A.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Youngsteadt, Elsa","contributorId":349804,"corporation":false,"usgs":false,"family":"Youngsteadt","given":"Elsa","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherveny, April Hamblin","contributorId":349805,"corporation":false,"usgs":false,"family":"Cherveny","given":"April Hamblin","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kettenbach, Jessica","contributorId":349806,"corporation":false,"usgs":false,"family":"Kettenbach","given":"Jessica","affiliations":[{"id":34928,"text":"Independent Researcher","active":true,"usgs":false}],"preferred":false,"id":924838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levenson, Hannah K.","contributorId":349807,"corporation":false,"usgs":false,"family":"Levenson","given":"Hannah K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carley, Danesha Seth","contributorId":349808,"corporation":false,"usgs":false,"family":"Carley","given":"Danesha Seth","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":924841,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Irwin, Rebecca E.","contributorId":349809,"corporation":false,"usgs":false,"family":"Irwin","given":"Rebecca E.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924842,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250622,"text":"70250622 - 2023 - Decline in small mammal species richness in coastal-central California, 1997–2013","interactions":[],"lastModifiedDate":"2023-12-20T12:54:43.939749","indexId":"70250622","displayToPublicDate":"2023-12-10T06:49:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Decline in small mammal species richness in coastal-central California, 1997–2013","docAbstract":"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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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. Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":927497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barrow, Wylie","contributorId":350958,"corporation":false,"usgs":false,"family":"Barrow","given":"Wylie","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":927498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeske, Clint W","contributorId":240737,"corporation":false,"usgs":false,"family":"Jeske","given":"Clint W","affiliations":[],"preferred":false,"id":927499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulz, Jessica","contributorId":330111,"corporation":false,"usgs":false,"family":"Schulz","given":"Jessica","affiliations":[{"id":52994,"text":"New Hampshire Department of Environmental Services","active":true,"usgs":false}],"preferred":false,"id":927500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobbs, Robert C. 0000-0002-9079-7249 rdobbs@usgs.gov","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":200300,"corporation":false,"usgs":false,"family":"Dobbs","given":"Robert C.","email":"rdobbs@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":927501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LeBlanc, Delaina","contributorId":330122,"corporation":false,"usgs":false,"family":"LeBlanc","given":"Delaina","email":"","affiliations":[{"id":78819,"text":"Barataria-Terrebonne National Estuary","active":true,"usgs":false}],"preferred":false,"id":927502,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, Amanda Nicole 0000-0003-3930-3896","orcid":"https://orcid.org/0000-0003-3930-3896","contributorId":224400,"corporation":false,"usgs":true,"family":"Anderson","given":"Amanda","email":"","middleInitial":"Nicole","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":927503,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Greary, Brock","contributorId":350959,"corporation":false,"usgs":false,"family":"Greary","given":"Brock","affiliations":[{"id":83379,"text":"University of Pennsylvania School of Veterinary Medicine","active":true,"usgs":false}],"preferred":false,"id":927504,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zenzal, Theodore J. 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Hana","affiliations":[{"id":64427,"text":"Cherokee Nation System Solutions","active":true,"usgs":false}],"preferred":false,"id":927507,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lee, Darin L.","contributorId":215856,"corporation":false,"usgs":false,"family":"Lee","given":"Darin L.","affiliations":[{"id":13608,"text":"Louisiana Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":927508,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70250634,"text":"70250634 - 2023 - Chromosome-level genome assembly of the blacktail brush lizard, Urosaurus nigricaudus, reveals dosage compensation in an endemic lizard","interactions":[],"lastModifiedDate":"2023-12-21T12:48:14.338615","indexId":"70250634","displayToPublicDate":"2023-12-06T06:46:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3832,"text":"Genome Biology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Chromosome-level genome assembly of the blacktail brush lizard, Urosaurus nigricaudus, reveals dosage compensation in an endemic lizard","docAbstract":"Urosaurus nigricaudus is a phrynosomatid lizard endemic to the Baja California Peninsula in Mexico. 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":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","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. 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