Increasing prevalence of hypoxia in shallow waters of U.S. Gulf of Mexico (GoM) estuaries can pose a serious threat to eastern oysters (Crassostrea virginica). Their tolerance to hypoxia, however, is not well characterized, especially at elevated temperatures (>30 °C) typical of GoM estuaries in summer. Moreover, it is unknown whether differences in hypoxia tolerance exist between GoM oyster populations growing in estuaries differing in local environmental conditions. Wild oyster broodstocks were collected from four estuarine sites in Texas (Packery Channel, PC and Aransas Bay, AB) and Louisiana (Calcasieu Lake, CL and Vermilion Bay, VB) and their adult progenies (F1) were tested (Study 1) under continuous hypoxia (<2.0 mg O2 L−1) at 32 °C. Significant differences in hypoxia tolerance were found between F1 populations with calculated median lethal time (LT50) ranging from 3.9 to 12.5 days. PC and CL oysters were the most and least tolerant populations, respectively. The study was repeated twice more (Studies 2 and 3) using PC and CL oysters, and their responses at the organismic, cellular, and biochemical levels were investigated. Valve movement was monitored, and oysters were sampled to measure hemocyte density, plasma protein, calcium and glutathione concentrations, and digestive gland alanine and succinate concentrations after either 3–5 days (Study 2) or 1–3 days (Study 3) of hypoxia exposure. From the onset of hypoxia until their death, oysters stayed opened 13–32% of the time compared to 53–64% under normoxia, but no differences between populations were detected under hypoxia. PC oyster but not CL oyster plasma glutathione concentrations increased significantly in both studies. Under longer (3–5 days) hypoxia exposure, plasma calcium and glutathione concentrations of PC oysters were significantly higher than CL oysters. These results suggest PC oysters were better able to protect tissues against acidosis and oxidative damage during hypoxia and high temperature stress than CL oysters. Overall, our results indicate that oyster populations originating from the GoM vary in their response to hypoxia and high temperature stress and possess differential tolerance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jembe.2022.151840","usgsCitation":"Coxe, N., Casas, S.M., Marshall, D., La Peyre, M., Kelly, M.W., and La Peyre, J.F., 2023, Differential hypoxia tolerance of eastern oysters from the northern Gulf of Mexico at elevated temperature: Journal of Experimental Marine Biology and Ecology, v. 559, 151840, 11 p., https://doi.org/10.1016/j.jembe.2022.151840.","productDescription":"151840, 11 p.","ipdsId":"IP-142239","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499844,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/animalsciences_pubs/2258","text":"External Repository"},{"id":433112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.41593828686382,\n 26.24620150154975\n ],\n [\n -87.8147474310154,\n 30.18094270243091\n ],\n [\n -88.19816867786844,\n 31.00775203702119\n ],\n [\n -95.65904444535767,\n 29.715904561157274\n ],\n [\n -98.19488550583708,\n 27.38532092313082\n ],\n [\n -97.41593828686382,\n 26.24620150154975\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"559","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coxe, Nicholas","contributorId":341331,"corporation":false,"usgs":false,"family":"Coxe","given":"Nicholas","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casas, Sandra M.","contributorId":145452,"corporation":false,"usgs":false,"family":"Casas","given":"Sandra","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":908247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshall, Danielle A.","contributorId":239867,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle A.","affiliations":[{"id":48014,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":908248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelly, Morgan W.","contributorId":341332,"corporation":false,"usgs":false,"family":"Kelly","given":"Morgan","email":"","middleInitial":"W.","affiliations":[{"id":13321,"text":"Texas A & M University","active":true,"usgs":false}],"preferred":false,"id":908250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"La Peyre, Jerome F.","contributorId":177346,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":908252,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254592,"text":"70254592 - 2023 - The Far-Field imprint of the late Paleozoic Ice Age, its demise, and the onset of a dust-house climate across the Eastern Shelf of the Midland Basin, Texas","interactions":[],"lastModifiedDate":"2024-06-04T11:38:15.455109","indexId":"70254592","displayToPublicDate":"2022-11-25T06:35:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1848,"text":"Gondwana Research","active":true,"publicationSubtype":{"id":10}},"title":"The Far-Field imprint of the late Paleozoic Ice Age, its demise, and the onset of a dust-house climate across the Eastern Shelf of the Midland Basin, Texas","docAbstract":"The late Paleozoic is a period of pronounced climatic and tectonic change, characterized by the onset and disappearance of continental-scale glaciers across polar Gondwana, the formation of Pangea, and widespread large igneous province volcanism. The low-latitude equatorial tropics are assumed to be places of persistent warm and wet climatic conditions throughout the Phanerozoic, which through intense silicate weathering, exert a major influence on Earth’s climate via the consumption of atmospheric carbon through carbonic hydrolytic weathering, formation of clay minerals and deliverability of alkalinity to ocean basins. Here we investigate the late Paleozoic sedimentary record of the Eastern Shelf of the Midland Basin in order to refine the climatic and provenance record of this region. The Eastern Shelf of the Midland Basin was situated within the equatorial tropics throughout the late Paleozoic and was connected to the open ocean through a network of fluvial systems that drained into the marine Midland Basin. We present new U-Pb zircon geochronology (19 samples, 2591 analyses) and sedimentary petrography (11 samples, 5800 grain counts), which we integrate with previously published paleobotany, paleosol chemistry and clay mineralogy to provide a holistic climate and tectonic record from this region. We observe major changes in sedimentary processes that we attribute to the formation of Pangea, eustatic changes linked to a dynamic high-latitude glaciation and teleconnections with low latitude hydrology, and a long-term shift in the Earth climate system all of which result in a dynamic sediment provenance history. Late Pennsylvanian and earliest Permian deposits are enriched in zircons with local affinity and interpreted to reflect local uplift and repeat incision across the basin margin, the latter a result of glacioeustatic forcing during an “everwet” climate. A major paleoenvironmental shift occurs in the late early Permian, which is reflected by the transition from fluvial to mixed fluvial-aeolian and ultimately aeolian dominant sedimentation by the late Permian. The transition from fluvial to aeolian dominant sedimentation is accompanied by a change in clay chemistry, sedimentary rock textual maturity, paleosol morphology and a threefold increase in Paleozoic zircons in the mid to late Permian strata. Widespread loess deposits across equatorial Pangea during the Permian have been used to argue for the possibility of equatorial glaciers situated in highland settings during the early Permian. Conversely, our data suggest initiation of a substantial component of aeolian deposition across the field areas, which is coincident with widespread ice loss across high latitude Gondwana, and ultimately highlights the teleconnections between high latitude glaciation and the low latitude hydrologic cycle.
The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains.
Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses.
Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed.
Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
Various (see acknowledgments).
Understanding species' associations with physical habitat conditions is a fundamental goal of ecology. For organisms that occupy lotic ecosystems, relationships to streamflow are of particular importance, but these associations are unstudied for most species. We tested the predictability of fish–microhabitat relationships in river shoals (shallow, rocky areas with relatively swift water flow) using a large data set from the Conasauga River in Georgia, USA. Our objective was to assess the consistency of species-specific relationships with flow-dependent variables (depth, velocity, Reynolds number and Froude number) while accounting for other microhabitat variables (e.g. vegetation). We used data from 8285 seine-sets collected during late summer or autumn at 26 sites over 12 years to relate occurrence and counts of 22 fish species to habitat variables using generalised linear multiple regression models. Results showed that microhabitat models explained a substantial amount of the variation in counts for some species, although other species were poorly predicted. We classified 16 species as velocity specialists and nine species as depth specialists, with six species specialised for depth and velocity and three species classified as depth and velocity generalists. The variability in habitat associations that we observed suggests that species will be unevenly affected by anthropogenic activities that alter flows.
Best management practices (BMPs) have been predominantly used throughout the Chesapeake Bay watershed (CBW) to reduce nutrients and sediments entering streams, rivers, and the bay. These practices have been successful in reducing loads entering the estuary and have shown the potential to reduce other contaminants (pesticides, hormonally active compounds, pathogens) in localized studies and modeled load estimates. However, further understanding of relationships between BMPs and non-nutrient contaminant reductions at regional scales using sampled data would be beneficial. Total estrogenic activity was measured in surface water samples collected over a decade (2008–2018) in 211 undeveloped NHDPlus V2.1 watersheds within the CBW. Bayesian hierarchical modeling between total estrogenic activity and landscape predictors including landcover, runoff, BMP intensity, and a BMP*agriculture intensity interaction term indicates a 96% posterior probability that BMP intensity on agricultural land is reducing total estrogenic activity. Additionally, watersheds with high agriculture and low BMPs had a 49% posterior probability of exceeding an effects-based threshold in aquatic organisms of 1 ng/L but only a 1% posterior probability of exceeding this threshold in high-agriculture, high-BMP watersheds.
Geologic carbon storage (GCS) is likely to be an important part of global efforts to decarbonize the energy industry. Widespread deployment of GCS relies on strategies to maximize CO2 injection rates while minimizing reservoir pressurization that could induce seismicity and/or fluid leakage into groundwater resources. Brine extraction from CO2 storage formations with subsurface reinjection elsewhere could mitigate pressure buildup associated with GCS. Therefore, evaluation of CO2 storage resources should consider the injectivity of produced brine in geologic layers above or below the CO2 storage formation. For this study, a methodology was developed to estimate brine injectivity from formation depth and thickness using flow modeling and optimization techniques. The methodology was demonstrated in the Illinois Basin, where GCS in the Mt. Simon Sandstone is ongoing. Based on pressure constraints and considering only regions of the shallower units where salinity and sealing conditions were met, maximum brine injection rates were estimated within the Mt. Simon and three overlying hydrostratigraphic layers. Results indicate that a large area exists where CO2 injectivity could be optimized by brine extraction and reinjection.
As climate change alters the global environment, it is critical to understand the relationship between shifting climate suitability and species distributions. Key questions include whether observed changes in population abundance are aligned with the velocity and direction of shifts predicted by climate suitability models and if the responses are consistent among species with similar ecological traits. We examined the direction and velocity of the observed abundance-based distribution centroids compared with the model-predicted bioclimatic distribution centroids of 250 bird species across the United States from 1969 to 2011. We hypothesized that there is a significant positive correlation in both direction and velocity between the observed and the modeled shifts. We then tested five additional hypotheses that predicted differential shifting velocity based on ecological adaptability and climate change exposure. Contrary to our hypotheses, we found large differences between the observed and modeled shifts among all studied bird species and within specific ecological guilds. However, temperate migrants and habitat generalist species tended to have higher velocity of observed shifts than other species. Neotropical migratory and wetland birds also had significantly different observed velocities than their counterparts, which may be due to their climate change exposure. The velocity based on modeled bioclimatic suitability did not exhibit significant differences among most guilds. Boreal forest birds were the only guild with significantly faster modeled-shifts than the other groups, suggesting an elevated conservation risk for high latitude and altitude species. The highly idiosyncratic species responses to climate and the mismatch between shifts in modeled and observed distribution centroids highlight the challenge of predicting species distribution change based solely on climate suitability and the importance of non-climatic factors traits in shaping species distributions.
A multi-omics approach was utilized to identify altered biological responses and functions, and to prioritize contaminants to assess the risks of chemical mixtures in the Maumee Area of Concern (AOC), Maumee River, OH, USA. The Maumee AOC is designated by the United States Environmental Protection Agency as having significant beneficial use impairments, including degradation of fish and wildlife populations, bird or animal deformities or reproduction problems, and loss of fish and wildlife habitat. Tree swallow (Tachycineta bicolor) nestlings were collected at five sites along the Maumee River, which included wastewater treatment plants (WWTPs) and industrial land-use sites. Polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated dibenzo p dioxins and furans (PCDD/Fs), and chlorinated pesticide concentrations were elevated in Maumee tree swallows, relative to a remote reference site, Star Lake, WI, USA. Liver tissue was utilized for non-targeted transcriptome and targeted metabolome evaluation. A significantly differentially expressed gene cluster related to a downregulation in cell growth and cell cycle regulation was identified when comparing all Maumee River sites with the reference site. There was an upregulation of lipogenesis genes, such as PPAR signaling (HMGCS2, SLC22A5), biosynthesis of unsaturated fatty acids (FASN, SCD, ELOVL2, and FADS2), and higher lipogenesis related metabolites, such as docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and arachidonic acid (AA) at two industrial land-use sites, Ironhead and Maumee, relative to WWTP sites (Perrysburg and SideCut), and the reference site. Toledo Water, in the vicinity of the other two industrial sites and also adjacent to a WWTP, showed a mix of signals between industrial land-use and WWTP land-use. PAHs, oxychlordane, and PBDEs were determined to be the most likely causes of the differentiation in biological responses, including de novo lipogenesis and biosynthesis of unsaturated fatty acids.
Wastewater treatment plant (WWTP) effluent-dominated streams provide critical habitat for aquatic and terrestrial organisms but also continually expose them to complex mixtures of pharmaceuticals that can potentially impair growth, behavior, and reproduction. Currently, few biomarkers are available that relate to pharmaceutical-specific mechanisms of action. In the experiment reported in this paper, zebrafish (Danio rerio) embryos at two developmental stages were exposed to water samples from three sampling sites (0.1 km upstream of the outfall, at the effluent outfall, and 0.1 km below the outfall) during base-flow conditions from two months (January and May) of a temperate-region effluent-dominated stream containing a complex mixture of pharmaceuticals and other contaminants of emerging concern. RNA-sequencing identified potential biological impacts and biomarkers of WWTP effluent exposure that extend past traditional markers of endocrine disruption. Transcriptomics revealed changes to a wide range of biological functions and pathways including cardiac, neurological, visual, metabolic, and signaling pathways. These transcriptomic changes varied by developmental stage and displayed sensitivity to variable chemical composition and concentration of effluent, thus indicating a need for stage-specific biomarkers. Some transcripts are known to be associated with genes related to pharmaceuticals that were present in the collected samples. Although traditional biomarkers of endocrine disruption were not enriched in either month, a high estrogenicity signal was detected upstream in May and implicates the presence of unidentified chemical inputs not captured by the targeted chemical analysis. This work reveals associations between bioeffects of exposure, stage of development, and the composition of chemical mixtures in effluent-dominated surface water. The work underscores the importance of measuring effects beyond the endocrine system when assessing the impact of bioactive chemicals in WWTP effluent and identifies a need for non-targeted chemical analysis when bioeffects are not explained by the targeted analysis.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159069","usgsCitation":"Meade, E.B., Iwanowicz, L., Neureuther, N., LeFevre, G.H., Kolpin, D., Zhi, H., Meppelink, S.M., Lane, R.F., Schmoldt, A., Mohaimani, A., Mueller, O., and Klaper, R.D., 2023, Transcriptome signatures of wastewater effluent exposure in larval zebrafish vary with seasonal mixture composition in an effluent-dominated stream: Science of the Total Environment, v. 856, no. Part 2, 159069, 12 p., https://doi.org/10.1016/j.scitotenv.2022.159069.","productDescription":"159069, 12 p.","ipdsId":"IP-128563","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12503111/","text":"Publisher Index Page"},{"id":408539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","city":"North Liberty","otherGeospatial":"Muddy Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.59782409667969,\n 41.70624114327587\n ],\n [\n -91.5216064453125,\n 41.70624114327587\n ],\n [\n -91.5216064453125,\n 41.75236092824208\n ],\n [\n -91.59782409667969,\n 41.75236092824208\n ],\n [\n -91.59782409667969,\n 41.70624114327587\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"856","issue":"Part 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meade, Emma B.","contributorId":298074,"corporation":false,"usgs":false,"family":"Meade","given":"Emma","email":"","middleInitial":"B.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":855054,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":855055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Neureuther, Nicklaus","contributorId":298075,"corporation":false,"usgs":false,"family":"Neureuther","given":"Nicklaus","email":"","affiliations":[],"preferred":false,"id":855056,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":855057,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":247493,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855058,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhi, Hui","contributorId":225502,"corporation":false,"usgs":false,"family":"Zhi","given":"Hui","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":855059,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meppelink, Shannon M. 0000-0003-1294-7878","orcid":"https://orcid.org/0000-0003-1294-7878","contributorId":205653,"corporation":false,"usgs":true,"family":"Meppelink","given":"Shannon","email":"","middleInitial":"M.","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}],"preferred":true,"id":855060,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lane, Rachael F. 0000-0001-9202-0612","orcid":"https://orcid.org/0000-0001-9202-0612","contributorId":222471,"corporation":false,"usgs":true,"family":"Lane","given":"Rachael","email":"","middleInitial":"F.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":855061,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmoldt, Angela","contributorId":298078,"corporation":false,"usgs":false,"family":"Schmoldt","given":"Angela","email":"","affiliations":[{"id":64490,"text":"Great Lakes Genomics Center","active":true,"usgs":false}],"preferred":false,"id":855062,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mohaimani, Aurash","contributorId":298079,"corporation":false,"usgs":false,"family":"Mohaimani","given":"Aurash","email":"","affiliations":[{"id":64490,"text":"Great Lakes Genomics Center","active":true,"usgs":false}],"preferred":false,"id":855063,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mueller, Olaf","contributorId":298080,"corporation":false,"usgs":false,"family":"Mueller","given":"Olaf","email":"","affiliations":[{"id":64490,"text":"Great Lakes Genomics Center","active":true,"usgs":false}],"preferred":false,"id":855064,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Klaper, Rebecca D.","contributorId":218114,"corporation":false,"usgs":false,"family":"Klaper","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":false,"id":855065,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70236439,"text":"70236439 - 2023 - Range-wide sources of variation in reproductive rates of northern spotted owls","interactions":[],"lastModifiedDate":"2023-01-18T16:01:33.826947","indexId":"70236439","displayToPublicDate":"2022-08-25T06:36:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Range-wide sources of variation in reproductive rates of northern spotted owls","docAbstract":"We conducted a range-wide investigation of the dynamics of site level reproductive rate of northern spotted owls using survey data from 11 study areas across the sub-species geographic range collected during 1993–2018. Our analytical approach accounted for imperfect detection of owl pairs and misclassification of successful reproduction (i.e., at least one young fledged) and contributed further insights into northern spotted owl population ecology and dynamics. Both nondetection and state misclassification were important, especially because factors affecting these sources of error also affected focal ecological parameters. Annual probabilities of site occupancy were greatest at sites with successful reproduction in the previous year and lowest for sites not occupied by a pair in the previous year. Site-specific occupancy transition probabilities declined over time and were negatively affected by barred owl presence. Overall, the site-specific probability of successful reproduction showed substantial year-to-year fluctuations and was similar for occupied sites that did and did not experience successful reproduction the previous year. Site-specific probabilities for successful reproduction were very small for sites that were unoccupied the previous year. Barred owl presence negatively affected the probability of successful reproduction by northern spotted owls in Washington and California, as predicted, but the effect in Oregon was mixed. The proportions of sites occupied by northern spotted owl pairs showed steep, near-monotonic declines over the study period, with all study areas showing the lowest observed levels of occupancy to date. If trends continue it is likely that northern spotted owls will become extirpated throughout large portions of their range in the coming decades.
Spatiotemporal constraints for Late Cretaceous tectonism across the Colorado Plateau and southern Rocky Mountains (northern Arizona−New Mexico, USA) are interpreted in regards to Laramide orogenic mechanisms. Onset of Laramide arch development is estimated from cooling recorded in representative thermochronologic samples in a three-step process of initial forward models, secondary HeFTy inverse models with informed constraint boxes, and a custom script to statistically estimate timing of rapid cooling from inverse model results. Onset of Laramide basin development is interpreted from increased rates of tectonic subsidence. Onset estimates are compared to published estimates for Laramide timing, and together suggest tectonism commenced ca. 90 Ma in northwestern Arizona and progressed eastward with later onset in north-central New Mexico by ca. 75−70 Ma. The interpreted sweep of onset progressed at a rate of ∼50 km/m.y. and was approximately half the 100−150 km/m.y. rate estimated for Late Cretaceous Farallon-North America convergence during the same timeframe. Previous suggestions that the Laramide tectonic front progressed at a rate similar to convergence via basal traction are not supported by our results. We thereby suggest that (1) a plate margin end load established far field compression and that (2) sequential Laramide-style strain was facilitated by progressive weakening of North American lithosphere from the dehydrating Farallon flat slab. Results are compared to models of sweeping tectonism and magmatism in other parts of the Laramide foreland. Discussions of the utility of the custom script and the potential for stratigraphic constraints to represent only minimum onset estimates are also presented.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36245.1","usgsCitation":"Thacker, J., Karlstrom, K., Kelley, S., Crow, R.S., and Kendall, J., 2023, Late Cretaceous time-transgressive onset of Laramide arch exhumation and basin subsidence across northern Arizona−New Mexico, USA, and the role of a dehydrating Farallon flat slab: GSA Bulletin, v. 135, no. 1-2, p. 389-406, https://doi.org/10.1130/B36245.1.","productDescription":"18 p.","startPage":"389","endPage":"406","ipdsId":"IP-122202","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":445525,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/gsab.s.19362371","text":"External Repository"},{"id":401533,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.818359375,\n 33.90689555128866\n ],\n [\n -103.447265625,\n 33.90689555128866\n ],\n [\n -103.447265625,\n 37.020098201368114\n ],\n [\n -113.818359375,\n 37.020098201368114\n ],\n [\n -113.818359375,\n 33.90689555128866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"135","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2022-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Thacker, Jacob","contributorId":292189,"corporation":false,"usgs":false,"family":"Thacker","given":"Jacob","affiliations":[{"id":62838,"text":"NMBG","active":true,"usgs":false}],"preferred":false,"id":844026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Karl","contributorId":292190,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":844027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Shari","contributorId":292191,"corporation":false,"usgs":false,"family":"Kelley","given":"Shari","affiliations":[{"id":62838,"text":"NMBG","active":true,"usgs":false}],"preferred":false,"id":844028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendall, Jerry","contributorId":292192,"corporation":false,"usgs":false,"family":"Kendall","given":"Jerry","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":844030,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229514,"text":"70229514 - 2023 - Using genetic data to advance stream fish reintroduction science: A case study in brook trout","interactions":[],"lastModifiedDate":"2023-01-18T15:46:36.932566","indexId":"70229514","displayToPublicDate":"2022-03-03T07:12:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using genetic data to advance stream fish reintroduction science: A case study in brook trout","docAbstract":"Widespread extirpation of native fish populations has led to a rise in species reintroduction efforts worldwide. Most efforts have relied on demographic data alone to guide project design and evaluate success. However, the genetic characteristics of many imperiled fish populations including low diversity, local adaptation, and hatchery introgression emphasize the importance of genetic data in the design and monitoring of reintroduction efforts. Focusing on a case study of brook trout (Salvelinus fontinalis) in North Carolina, USA, we show how the combined use of genetic and demographic data can support reintroduction efforts by improving source population selection and providing opportunities to evaluate genetic viability and adaptive potential in restored populations. Using this combined approach, we reintroduced brook trout into a restored stream from two source populations and monitored changes in genetic diversity and population size in source and recipient populations. Three years after the initial translocation, the reintroduced population had comparable density, but higher genetic diversity, than either source population. This study demonstrates the utility of genetic and demographic data for reintroduction efforts, particularly when extant populations are genetically depauperate and maintaining adaptive potential is a primary restoration goal. However, we emphasize the value of continued monitoring at longer temporal and spatial scales to determine the effects of stochastic process on the long-term adaptive capacity and persistence of reintroduced populations. Overall, inclusion of genetic data in reintroduction efforts offers increased ability to meet project goals while simultaneously conserving critical sources of adaptive variation that exist across the landscape.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13662","usgsCitation":"White, S.L., Johnson, T.C., Rash, J.M., Lubinski, B.A., and Kazyak, D., 2023, Using genetic data to advance stream fish reintroduction science: A case study in brook trout: Restoration Ecology, v. 31, no. 1, e13662, 13 p., https://doi.org/10.1111/rec.13662.","productDescription":"e13662, 13 p.","ipdsId":"IP-131735","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":397018,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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Carolina\",\"nation\":\"USA \"}}]}","volume":"31","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":837716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Thomas C","contributorId":245999,"corporation":false,"usgs":false,"family":"Johnson","given":"Thomas","email":"","middleInitial":"C","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":837717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rash, Jacob M","contributorId":218128,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","email":"","middleInitial":"M","affiliations":[{"id":39760,"text":"Division of Inland Fisheries, North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":837718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":837719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":837720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250096,"text":"70250096 - 2022 - Evaluating the influence of the Forestry Reclamation Approach on throughfall quantity in eastern Kentucky","interactions":[],"lastModifiedDate":"2024-06-03T14:45:20.437591","indexId":"70250096","displayToPublicDate":"2023-08-02T06:32:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17091,"text":"Reclamation Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the influence of the Forestry Reclamation Approach on throughfall quantity in eastern Kentucky","docAbstract":"The Appalachian Region is a rich forested ecosystem that has been impacted by coal mining. The Surface Mining Control and Reclamation Act of 1977 was enacted to resolve many of the environmental problems caused by surface mining. Reclamation practices resulted in excessive soil compaction and use of nonnative grasses and shrubs that have altered hydrologic processes. The Forestry Reclamation Approach (FRA) is a best practice for reestablishing forested ecosystems on mined lands in Appalachia. This project evaluated precipitation throughfall in reforested 10- and 20-year-old FRA sites and unmined 100-year-old forest stands as a metric for evaluating the return of forest hydrologic function after reclamation. Stands of coniferous and deciduous trees were evaluated independently for each age class. Throughfall rates were significantly impacted by tree type and age. Throughfall in coniferous trees was less than in deciduous trees, and throughfall in the 10-year-old deciduous trees tended to be highest. Throughfall was also significantly impacted by storm characteristics. Higher rainfall depth and longer duration resulted in significantly larger throughfall depths under both coniferous and deciduous stands, whereas increased intensity increased throughfall depths for the 10- and 100-year-old plots, but not for the 20-year-old plots. As canopy closure occurs in young FRA forests, throughfall rates resemble those reported for young, naturally regenerating forests in the region. Results may help guide management of forested watershed strategies to reduce surface runoff and local flooding on reclaimed surface mined lands.
","language":"English","publisher":"Allen Press","doi":"10.21000/rcsc-202200009","usgsCitation":"Gerlitz, M., Agouridis, C.T., Williamson, T.N., and Barton, C.D., 2022, Evaluating the influence of the Forestry Reclamation Approach on throughfall quantity in eastern Kentucky: Reclamation Sciences, v. 1, p. 13-24, https://doi.org/10.21000/rcsc-202200009.","productDescription":"12 p.","startPage":"13","endPage":"24","ipdsId":"IP-122841","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":445584,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21000/rcsc-202200009","text":"Publisher Index Page"},{"id":422673,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","county":"Breathitt County, Knott County, Perry County","otherGeospatial":"Laurel Fork Mine, Starfire Mine, Robinson Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.18644168615366,\n 37.484780966469884\n ],\n [\n -83.18644168615366,\n 37.41427280145203\n ],\n [\n -83.08730948291287,\n 37.41427280145203\n ],\n [\n -83.08730948291287,\n 37.484780966469884\n ],\n [\n -83.18644168615366,\n 37.484780966469884\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationDate":"2023-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Gerlitz, Morgan","contributorId":331640,"corporation":false,"usgs":false,"family":"Gerlitz","given":"Morgan","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":888322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Agouridis, Carmen T. 0000-0001-9580-6143","orcid":"https://orcid.org/0000-0001-9580-6143","contributorId":150223,"corporation":false,"usgs":false,"family":"Agouridis","given":"Carmen","email":"","middleInitial":"T.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":888323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barton, Chris D. 0000-0003-0692-3079","orcid":"https://orcid.org/0000-0003-0692-3079","contributorId":236883,"corporation":false,"usgs":false,"family":"Barton","given":"Chris","email":"","middleInitial":"D.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":888325,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208532,"text":"sim3420 - 2022 - Regional water table in the Antelope Valley and Fremont Valley groundwater basins, Southwestern Mojave Desert, California, March 2014","interactions":[],"lastModifiedDate":"2026-02-19T17:29:40.380597","indexId":"sim3420","displayToPublicDate":"2023-02-03T06:58:34","publicationYear":"2022","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":"3420","displayTitle":"Regional Water Table in the Antelope Valley and Fremont Valley Groundwater Basins, Southwestern Mojave Desert, California, March 2014","title":"Regional water table in the Antelope Valley and Fremont Valley groundwater basins, Southwestern Mojave Desert, California, March 2014","docAbstract":"Water levels were measured during March 2014 in wells in the Antelope Valley and Fremont Valley groundwater basins, southwestern Mojave Desert, California, in cooperation with the Antelope Valley-East Kern Water District, Palmdale Water District, and Littlerock Creek Irrigation District. A regional water-table map was constructed. Historical water-level data from the USGS National Water Information System (NWIS) database were used to construct water-level hydrographs to show long-term (1917-2014) water-level changes in the Antelope Valley and Fremont Valley groundwater basins.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sim3420","collaboration":"Prepared in cooperation with the Antelope Valley State Water Contractors Association","usgsCitation":"Dick, M.C., Teague, N.F., 2018, Regional water table in the Antelope Valley and Fremont Valley groundwater basins, Southwestern Mojave Desert, California, March 2014: U.S. Geological Survey Scientific Investigations Map 3420, 2 p., https://doi.org/10.3133/sim3420","productDescription":"Data Release; HTML Document; 2 Sheets: 27.89 × 32.94 inches and 27.89 × 32.94 inches","ipdsId":"IP-075082","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500196,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114336.htm","linkFileType":{"id":5,"text":"html"}},{"id":412692,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://ca.water.usgs.gov/projects/antelope-valley/"},{"id":402402,"rank":1,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3420/sim3420_sheet1.pdf","text":"Sheet 1","size":"104 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3420 Sheet 1 of 2","linkHelpText":"- Regional water table in the Antelope Valley and Fremont Valley groundwater basins, southwestern Mojave Desert, California, March 2014"},{"id":402405,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3420/covrthb.jpg"},{"id":402403,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3420/sim3420_sheet2.pdf","text":"Sheet 2","size":"65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3420 Sheet 2 of 2","linkHelpText":"- Regional water-table change in the Antelope Valley and Fremont Valley groundwater basins, southwestern Mojave Desert, California, Spring 1996–2014"},{"id":402404,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3420/versionHist.txt"},{"id":405486,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IQIP0L","text":"Regional water table Contours of the Antelope Valley and Fremont Valley groundwater basins, Southwestern Mojave Desert, California, March 2014","description":"Dick, M.C., Teague, N.F., Fenton, N.C., 2022, Regional water table Contours of the Antelope Valley and Fremont Valley groundwater basins, Southwestern Mojave Desert, California, March 2014: U.S. Geological Survey data release, [available at https://doi.org/10.5066/P9IQIP0L]."}],"country":"United States","state":"California","otherGeospatial":"Mohave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.90567382213953,\n 36.106446138965794\n ],\n [\n -117.90567382213953,\n 34.61990913772064\n ],\n [\n -114.85277111098179,\n 34.61990913772064\n ],\n [\n -114.85277111098179,\n 36.106446138965794\n ],\n [\n -117.90567382213953,\n 36.106446138965794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1: June 2016; Version 2: March 2017; Version. 3: July 2020; Version 4: June 2022","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This 1:24,000-scale geologic map of the Silver Zone Pass quadrangle lies in the southern Toano Range in Elko County, Nevada. Metamorphic and sedimentary strata of the quadrangle range from Neoproterozoic to Permian in age. Important intrusions include the Late Jurassic (ca. 159 Ma) Silver Zone Pass pluton and Cretaceous Toano Spring pluton. In particular, the Silver Zone Pass pluton involves undeformed dikes that crosscut metamorphic foliations and the pluton is associated with pluton-margin anticlines. Interpretation of these characteristics suggests that the pluton was syn-kinematic with respect to metamorphism and strain, thus requiring a phase of Late Jurassic deformation. A Miocene rhyolite lava is of particular interest as one of the few topaz-bearing volcanic rocks in Nevada. A major detachment fault places non-metamorphosed Paleozoic rocks over low-grade Paleozoic and Proterozoic rocks. High-angle normal faults tilted the range in several blocks, and Miocene Humboldt Formation were deposited on, and faulted against, bedrock. Rocks of the Toano Range are bounded by broad valleys on the east and west, with the eastern basin being at much lower elevation than the western basin. Pleistocene lakes, which created distinctive beach deposits, occupied both basins, with Lake Bonneville on the east and Lake Waring on the west. Silver Zone Pass owes its low relief to the enhanced weathering and erosion of the rock within the pass, a granodiorite pluton. The weathering has created some unusual landforms such as tors.
As North Carolina’s only native salmonid, Brook Trout Salvelinus fontinalis is a fish of considerable ecological and cultural significance in the state, but anthropogenic alterations to the landscape and introductions of nonnative salmonids have fragmented and reduced its native range. As a result, the North Carolina Wildlife Resources Commission (NCWRC) has enacted numerous efforts to help conserve the species. Annual demographic surveys of self-sustaining Brook Trout populations have been on-going since 1978, which have also included successful efforts to document previously unidentified populations. Beginning in earnest during the 1990s, allozyme testing was used to assess patterns of hatchery introgression, with over 480 collections genotyped at the creatine kinase locus. In 2010, the NCWRC began using microsatellite markers to conduct an extensive survey of Brook Trout genetic diversity and variation. To date, 541 Brook Trout collections representing 11,090 individuals have been genotyped at 12 microsatellite loci. These data have provided insights into evolutionary relationships among populations, spatial patterns of genetic diversity, and the extent of hatchery introgression within populations. Ultimately, increased understanding of genetic diversity and relatedness have been informative for determining that Brook Trout management in North Carolina is likely best enacted at the level of individual populations. Moreover, we have used these data to actively guide stream restoration and population reintroduction activities. Over the last 15 years, NCWRC and its partners have used genetic data to prioritize habitat enhancement activities and guide 17 Brook Trout population reintroduction projects. In the future, we plan to continue expanding the microsatellite genetic baseline while also exploring the utility of phylogenomic analyses to inform Brook Trout conservation activities. Genetic and genomic approaches have great potential to improve the efficacy of conservation actions for Brook Trout in North Carolina and throughout its native range.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of Wild Trout XIII","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Wild Trout XIII","conferenceDate":"September 27-30, 2022","conferenceLocation":"West Yellowstone, MT","language":"English","publisher":"Wild Trout Symposium","usgsCitation":"Rash, J., Kazyak, D., White, S.L., and Lubinski, B.A., 2022, Utilization of genetic data to inform native Brook Trout conservation in North Carolina, in Proceedings of Wild Trout XIII, West Yellowstone, MT, September 27-30, 2022, p. 158-163.","productDescription":"6 p.","startPage":"158","endPage":"163","ipdsId":"IP-143335","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":413954,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildtroutsymposium.com/proceedings.php","linkFileType":{"id":5,"text":"html"}},{"id":413955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.04921892394643,\n 35.16380575406262\n ],\n [\n -80.26699801469118,\n 36.54940722798543\n ],\n [\n -81.72273121506292,\n 36.60331819137433\n ],\n [\n -82.20159275018115,\n 36.10046486828409\n ],\n [\n -82.88139191858609,\n 35.905982228760394\n ],\n [\n -83.58707291840865,\n 35.49691451561709\n ],\n [\n -84.00314930521716,\n 35.43636411766791\n ],\n [\n -84.19291110790027,\n 35.20050466686614\n ],\n [\n -84.40371695037375,\n 34.915348088734206\n ],\n [\n -83.13188407946514,\n 34.95778267269911\n ],\n [\n -82.04921892394643,\n 35.16380575406262\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rash, Jacob","contributorId":202482,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":866100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239159,"text":"dr1165 - 2022 - Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","interactions":[],"lastModifiedDate":"2023-01-03T11:51:43.361028","indexId":"dr1165","displayToPublicDate":"2022-12-30T09:43:53","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1165","displayTitle":"Range-wide Population Trend Analysis for Greater Sage-Grouse (Centrocercus urophasianus)—Updated 1960–2021","title":"Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","docAbstract":"Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021) and includes the addition of three analytical updates: (1) identification of population nadirs (lowest points within cycles) at the lek (breeding ground) and neighborhood cluster (group of leks) spatial scales, (2) truncation of prior distributions on rate of change in apparent abundance values to more realistic boundaries for leks with missing data, and (3) addition of 2 years of population lek count data (2020 and 2021) to the current dataset (1953–2021). Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.6). Cumulative declines were 42.5, 65.6, and 80.1 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that range-wide populations continued to decline during 2020 and 2021, although two climate clusters (eastern area and Bi-State area) have shown growth in population abundance in recent years, indicating they have surpassed a recent population abundance nadir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1165","issn":"2771-9448","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021: Data Report 1165, 16 p., https://doi.org/10.3133/dr1165.","productDescription":"Report: viii, 16 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-144163","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":411225,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"U.S. Geological Survey data release","linkHelpText":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (1960–2021)"},{"id":411222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1165/dr1165.pdf","text":"Report","size":"8.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1165"},{"id":411223,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1165/images"},{"id":411221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1165/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.19101705712868,\n 48.89284566335482\n ],\n [\n -122.13131256755497,\n 48.37855671670232\n ],\n [\n -121.63700211579436,\n 47.85380781194411\n ],\n [\n -121.86067071873805,\n 47.51242609246373\n ],\n [\n -121.87668371107034,\n 47.11115078292244\n ],\n [\n -122.4532512741522,\n 46.70567448488106\n ],\n [\n -122.57397075207928,\n 46.22739431053469\n ],\n [\n -122.36606239056442,\n 45.90712886523926\n ],\n [\n -121.91588031540189,\n 45.358384504867246\n ],\n [\n -122.45322729336237,\n 44.953343420279765\n ],\n [\n -122.52983521148596,\n 44.52144457126775\n ],\n [\n -122.54530349941209,\n 44.259263341793\n ],\n [\n -122.85735468588939,\n 43.86827650155766\n ],\n [\n -123.01704364538307,\n 43.46272668287682\n ],\n [\n -123.01026119844099,\n 43.21452795313556\n ],\n [\n -123.10511846238558,\n 42.807780288627384\n ],\n [\n -122.77301525682884,\n 42.07884845828369\n ],\n [\n -122.62454526743153,\n 41.5133850817748\n ],\n [\n -122.14925905063672,\n 40.83036532174481\n ],\n [\n -121.68837858072334,\n 40.49667518946566\n ],\n [\n -121.57750448671058,\n 39.80789795232664\n ],\n [\n -121.16256808423407,\n 39.215880289775015\n ],\n [\n -120.64471039578123,\n 38.57511631320122\n ],\n [\n -119.84212022937817,\n 37.43985090599355\n ],\n [\n -118.83488568782542,\n 36.55782111752346\n ],\n [\n -118.59349323888452,\n 35.83229168807691\n ],\n [\n -117.40450808029976,\n 35.69535862053445\n ],\n [\n -116.43105077565428,\n 35.459239916109155\n ],\n [\n -114.6229823251931,\n 35.413666627683824\n ],\n [\n -96.18756245429773,\n 35.03931170375198\n ],\n [\n -94.57905716272413,\n 37.1225590274522\n ],\n [\n -94.53555587839759,\n 39.07884303955356\n ],\n [\n -95.83917624651616,\n 40.61806593890233\n ],\n [\n -96.37123578579417,\n 42.39205565405334\n ],\n [\n -96.46625839716233,\n 45.31077773932529\n ],\n [\n -97.30155203348903,\n 48.980368873698694\n ],\n [\n -122.19101705712868,\n 48.89284566335482\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
https://www.usgs.gov/centers/werc
Contact Pubs Warehouse
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Understanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age-specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions (Eumetopias jubatus) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local- and basin-scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population-level responses to future climate variability.
Population models often require detailed information on sex-, age-, or size-specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage- or size-structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class-specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high-resolution output of latent population structure that is partially observed during the monitoring process.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4321","usgsCitation":"Lawson, A.J., Jodice, P.G., Rainwater, T., Dunham, K.D., Hart, M., Butfiloski, J.W., Wilkinson, P., and Moore, C., 2022, Hidden in plain sight: Integrated population models to resolve partially observable latent population structure: Ecosphere, v. 13, e4321, 22 p., https://doi.org/10.1002/ecs2.4321.","productDescription":"e4321, 22 p.","ipdsId":"IP-137983","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4321","text":"Publisher Index Page"},{"id":430217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University","active":true,"usgs":false}],"preferred":false,"id":903451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Kylee Denise 0000-0002-9249-0590","orcid":"https://orcid.org/0000-0002-9249-0590","contributorId":296991,"corporation":false,"usgs":true,"family":"Dunham","given":"Kylee","email":"","middleInitial":"Denise","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Morgan","contributorId":338673,"corporation":false,"usgs":false,"family":"Hart","given":"Morgan","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butfiloski, Joseph W.","contributorId":338675,"corporation":false,"usgs":false,"family":"Butfiloski","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilkinson, Philip M.","contributorId":338676,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Philip M.","affiliations":[{"id":54598,"text":"Tom Yawkey Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":903455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Clinton 0000-0001-7782-3994 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0001-7782-3994","contributorId":338679,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903456,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239065,"text":"ofr20221119 - 2022 - Hydrologic effects of leakage from the Catskill Aqueduct on the bedrock-aquifer system near High Falls, New York, November 2019–January 2020","interactions":[],"lastModifiedDate":"2026-03-30T20:55:17.842923","indexId":"ofr20221119","displayToPublicDate":"2022-12-27T14:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1119","displayTitle":"Hydrologic Effects of Leakage from the Catskill Aqueduct on the Bedrock-Aquifer System near High Falls, New York, November 2019–January 2020","title":"Hydrologic effects of leakage from the Catskill Aqueduct on the bedrock-aquifer system near High Falls, New York, November 2019–January 2020","docAbstract":"Historical observations by the New York City Department of Environmental Protection (NYCDEP) indicate that the Rondout pressure tunnel has been leaking in the vicinity of the hamlet of High Falls, New York. In the 74 days from November 11, 2019, to January 23, 2020, NYCDEP shut down and partially dewatered the pressure tunnel for inspection and repairs. On November 5–7, 2019 (during normal tunnel operations), and on January 21–22, 2020 (when the tunnel was shut down), the U.S. Geological Survey used a network of 31 groundwater wells to collect water-level elevations and determine the potentiometric surface of the bedrock aquifer adjacent to the Rondout pressure tunnel. When the tunnel was fully pressurized during normal operations, water levels indicated a two-mile-long groundwater mound which trended northeastward, approximately along the regional strike of the bedrock units. The mound ranged in elevation from 250 to 300 feet (ft) above the North American Vertical Datum of 1988 and extended from 1,500 ft southwest of a suspected leak at the Rondout pressure tunnel to about 8,500 ft northeast of the possible leak. During the 74-day shutdown, during which the aqueduct was nonoperational, this groundwater mound decreased in magnitude and extent as it reverted to equilibrium conditions. This resulted in a flattening of the potentiometric surface, represented by two remnant groundwater plateaus.
Water-level differences were calculated for wells that may be affected by potential tunnel leakage to determine the influence on the local bedrock aquifer. The five largest water-level differences (77, 61, 49, 42, and 41 ft) occurred in wells that were generally aligned with the northeastward trend of regional bedrock strike; these wells may penetrate the karstic Helderberg Group bedrock unit. Near the suspected tunnel leak, the Helderberg Group overlies the Binnewater Sandstone and the High Falls Shale, both of which produced substantial groundwater inflows during the construction of the Rondout pressure tunnel. Water levels in wells penetrating the Shawangunk Formation just east of Rondout Creek, where the unit is in contact with the High Falls Shale, and in wells penetrating the Esopus Shale, which is adjacent to the Helderberg Group and northwest of the tunnel leak, may be affected by tunnel leakage. It is unclear if water levels in a well 9,000 ft northwest of the suspected tunnel leak are influenced by the tunnel leakage, by another source of artificial recharge, or by both. This well penetrates the Onondaga Limestone in the northwestern part of the study area. An unconsolidated aquifer composed of stratified gravel, sand, silt, and clay overlies the limestone bedrock in this part of study area―additional study is required to determine if this unconsolidated aquifer is affected by tunnel leakage.
Robert Francis Breault, Center Director
New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
The U.S. Geological Survey (USGS) completed an assessment in 2007 of the undiscovered, technically recoverable, continuous gas potential of Cretaceous–Tertiary coal beds of the onshore areas and State waters of the northern Gulf of Mexico Coastal Plain. The assessment was based on geologic elements including hydrocarbon source rocks, availability of suitable reservoir rocks, and hydrocarbon accumulations in three coalbed gas total petroleum systems (TPSs) identified in the region: (1) the Olmos Coalbed Gas TPS (Upper Cretaceous), (2) the Wilcox Coalbed Gas TPS (Paleocene–Eocene), and (3) the Cretaceous-Tertiary Coalbed Gas TPS. Four continuous assessment units (AUs) were defined within these three TPSs: (1) the Cretaceous Olmos Coalbed Gas AU, (2) the Rio Escondido Basin Olmos Coalbed Gas AU, (3) the Wilcox Coalbed Gas AU, and (4) the Cretaceous-Tertiary Coalbed Gas AU, which was not quantitatively assessed and which includes all other Cretaceous and Tertiary coal beds that are not included in the other AUs.
This USGS assessment estimated a mean of 4.06 trillion cubic feet of undiscovered, technically recoverable, continuous coalbed gas resources in the four AUs that were assessed. Nearly all of the undiscovered continuous gas resources that were estimated (95 percent, or 3.86 trillion cubic feet of gas [TCFG]) were in the Wilcox Coalbed Gas AU. The continuous gas resources resided in coalbed reservoirs. Gas sourced from these coal beds may also occur as conventional accumulations in adjacent or interlayered sandstones that were not included in this assessment of continuous resources. The assessment was conducted via the established USGS methodology for continuous petroleum accumulations and reflects estimates of undiscovered resources based on vertical (nonhorizontal) drilling technology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171167","usgsCitation":"Warwick, P.D., 2022, Geologic assessment of undiscovered gas resources in Cretaceous–Tertiary coal beds of the U.S. Gulf of Mexico Coastal Plain: U.S. Geological Survey Open-File Report 2017–1167, 52 p., https://doi.org/10.3133/ofr20171167.","productDescription":"Report: vi, 52 p.; 3 Appendixes","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-017257","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":501520,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113996.htm","linkFileType":{"id":5,"text":"html"}},{"id":410558,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/ofr20171111","text":"Open-File Report 2017–1111","linkHelpText":"- Geologic assessment of undiscovered conventional oil and gas resources in the Lower Paleogene Midway and Wilcox Groups, and the Carrizo Sand of the Claiborne Group, of the Northern Gulf coast region"},{"id":410555,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix1.pdf","text":"Appendix 1","size":"165 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Cretaceous Olmos Coalbed Gas Assessment Unit (50470281)"},{"id":410985,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20171167/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2017-1167"},{"id":410553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1167"},{"id":410552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1167/coverthb.jpg"},{"id":409355,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167.XML"},{"id":409356,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2017/1167/images/"},{"id":410556,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix2.pdf","text":"Appendix 2","size":"161 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Rio Escondido Basin Olmos Coalbed Gas Assessment Unit (53000281)"},{"id":410557,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1167/ofr20171167_appendix3.pdf","text":"Appendix 3","size":"168 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Input Data Form for the Wilcox Coalbed Gas Assessment Unit (50470381)"}],"country":"United States","otherGeospatial":"U.S. Gulf of Mexico Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.32775336731807,\n 26.212616580411137\n ],\n [\n -81.93279691237665,\n 26.212616580411137\n ],\n [\n -81.3178237043738,\n 38.82626189520937\n ],\n [\n -99.67916662903421,\n 38.491360932976605\n ],\n [\n -102.44654606504763,\n 38.43753817164347\n ],\n [\n -102.40261940733356,\n 36.63809827557699\n ],\n [\n -102.4904727227622,\n 31.785348237738653\n ],\n [\n -99.32775336731807,\n 26.212616580411137\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator, Energy Resources Program
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