Coastal landscapes evolve in response to sea-level rise (SLR) through a variety of geologic processes and ecological feedbacks. When the SLR rate surpasses the rate at which these processes build elevation and drive lateral migration, inundation is likely.
To examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States.
Using an existing probabilistic framework, we quantify the probability of inundation, a measure of vulnerability, under different SLR scenarios on the coastal landscape. Resistant areas—wherein a dynamic response is anticipated—are defined as unlikely (p < 0.33) to inundate. Results are assessed regionally for different land cover types and at 26 sites representing varying levels of land cover diversity.
Modeling results suggest that by the 2050s, 44% of low-lying, habitable land in the region is unlikely to inundate, further declining to 36% by the 2080s. In addition to a decrease in SLR resistance with time, these results show an increasing uncertainty that the coastal landscape will continue to evolve in response to SLR as it has in the past. We also find that resistance to SLR is correlated with land cover composition, wherein sites containing land cover types adaptable to SLR impacts show greater potential to undergo biogeomorphic state shifts rather than inundating with time.
Our findings support other studies that have highlighted the importance of ecological composition and diversity in stabilizing the physical landscape and suggest that flexible planning strategies, such as adaptive management, are particularly well suited for SLR preparation in diverse coastal settings.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-020-01136-z","usgsCitation":"Lentz, E.E., Zeigler, S.L., Thieler, E.R., and Plant, N.G., 2021, Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast: Landscape Ecology, v. 36, p. 223-241, https://doi.org/10.1007/s10980-020-01136-z.","productDescription":"9 p.","startPage":"223","endPage":"241","ipdsId":"IP-101344","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-020-01136-z","text":"Publisher Index Page"},{"id":380684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.169921875,\n 44.10336537791152\n ],\n [\n -70.751953125,\n 44.26093725039923\n ],\n [\n -73.05908203125,\n 42.261049162113856\n ],\n [\n -76.7724609375,\n 39.639537564366684\n ],\n [\n -78.37646484375,\n 37.666429212090605\n ],\n [\n -77.71728515624999,\n 36.58024660149866\n ],\n [\n -75.34423828125,\n 36.43896124085945\n ],\n [\n -75.73974609375,\n 37.3002752813443\n ],\n [\n -74.0478515625,\n 39.977120098439634\n ],\n [\n -71.9384765625,\n 40.81380923056958\n ],\n [\n -69.80712890625,\n 41.178653972331674\n ],\n [\n -69.89501953125,\n 42.049292638686836\n ],\n [\n -70.64208984375,\n 42.8115217450979\n ],\n [\n -69.169921875,\n 44.10336537791152\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2020-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":169601,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805385,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":805386,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218686,"text":"70218686 - 2021 - Uncertainty in critical source area predictions from watershed-scale hydrologic models","interactions":[],"lastModifiedDate":"2021-03-05T13:28:39.226167","indexId":"70218686","displayToPublicDate":"2020-11-07T07:25:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in critical source area predictions from watershed-scale hydrologic models","docAbstract":"Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or ‘targeted’ for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%–46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.
Time series of water‐year runoff for 2,109 hydrologic units (HUs) across the conterminous United States (CONUS) for the 1900 through 2014 period were used to identify drought and pluvial (i.e., wet) periods. Characteristics of the drought and pluvial events including frequency, duration, and severity were examined and compared. Additionally, a similar analysis was performed using gridded tree‐ring reconstructions of the Palmer Drought Severity Index (PDSI) for the period 1475 through 2005 to place the drought and pluvial characteristics determined using water‐year runoff for 1900 through 2014 in the context of multi‐century climate variability. The temporal and spatial variability of droughts and pluvials determined using runoff for the 1900 through 2014 period indicated that most drought events in the CONUS occurred before about 1970, whereas most pluvial periods occurred after about 1970. This change in the frequencies of drought and pluvial events around 1970 was largely related to an increase in fall (October through December) precipitation across much of the central United States. Also, the duration and severity of droughts and pluvials identified using runoff for the 1900 through 2014 period generally were not significantly different from the drought and pluvial characteristics identified using the PDSI for the 1475 through 2005 period.
Changing environments of temperature, precipitation and moisture availability can affect vegetation in ecosystems, by affecting regeneration from the seed bank. Our objective was to explore the responses of soil seed bank germination to climate-related environments along geographic gradients. We collected seed banks in baldcypress (Taxodium distichum) swamps along the Mississippi River and the Gulf of Mexico Coast in the United States, which have distinct temperature and/or precipitation gradients, and germinated them in a greenhouse. The frequency, richness and seed density of species germinated from the seed bank were compared between various geographic locations, experimental water regimes (saturated, flooded) and wetland types (tidal, non-tidal and inland swamps). We also analyzed the relationship of seed density to the environment by using a Non-metric Multi-dimensional Scaling (NMDS) model. Sixty-one species germinated from the seed bank, differing in pattern by geographic location, experimental water regime and wetland type. The foundation species (i.e., T. distichum and Cephalanthus occidentalis) germinated with a niche affinity for the northern part of the latitudinal gradient (Tennessee and Illinois) and these species may shift northward with climate change. Some species had higher seed density in the locations that were subject to more persistent drought conditions (e.g., Texas) including Cyperus rotundus and Gratiola virginiana, indicating that these species may be better adapted to sites with high temperature and low precipitation. In contrast, certain species including Saururus cernuus and Ludwigia palustris were present throughout the range of these gradients, and so may be more resilient to any future climate shifts. We found that the regeneration potential of baldcypress swamps might be altered by changes in local and climate environment because of nuances of responses of seed banks to climates along latitudinal and longitudinal gradients. Our study can help predict vegetation regeneration potential to climate change environments depending on the ability of these species to disperse and maintain seed banks.
Community occupancy models estimate species‐specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates towards the community mean can mask differences among groups. Infinite mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species‐level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP‐COM with different dimensions (i.e., different numbers of model parameters with species‐level DP random effects) and under varying cluster differences. Because the DP‐COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP‐COM model to a bird dataset from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community level covariate coefficients on occupancy probability were generally unbiased with (near‐) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP‐COM were similar. The DP‐COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP‐COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP‐COM, and considering the computational burden for the DP models, we recommend using the DP‐COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP‐COM.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2249","usgsCitation":"Sollmann, R., Eaton, M.J., Link, W., Mulundo, P., Ayebare, S., Prinsloo, S., Plumptre, A.J., and Johnson, D., 2021, A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity: Ecological Applications, v. 31, no. 2, e2249, https://doi.org/10.1002/eap.2249.","productDescription":"e2249","ipdsId":"IP-090810","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":454310,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2249","text":"External Repository"},{"id":380583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Sollmann, Rahel 0000-0002-1607-2039","orcid":"https://orcid.org/0000-0002-1607-2039","contributorId":244998,"corporation":false,"usgs":false,"family":"Sollmann","given":"Rahel","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":805121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":805123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Link, William 0000-0002-9913-0256","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":221718,"corporation":false,"usgs":true,"family":"Link","given":"William","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":805122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mulundo, Paul","contributorId":245000,"corporation":false,"usgs":false,"family":"Mulundo","given":"Paul","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":805124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayebare, Samuel","contributorId":245001,"corporation":false,"usgs":false,"family":"Ayebare","given":"Samuel","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":805125,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prinsloo, Sarah","contributorId":245002,"corporation":false,"usgs":false,"family":"Prinsloo","given":"Sarah","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":805126,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Plumptre, Andrew J.","contributorId":213154,"corporation":false,"usgs":false,"family":"Plumptre","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":805127,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, D.S.","contributorId":245003,"corporation":false,"usgs":false,"family":"Johnson","given":"D.S.","affiliations":[{"id":17856,"text":"National Marine Fisheries Service, NOAA","active":true,"usgs":false}],"preferred":false,"id":805128,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70217747,"text":"70217747 - 2021 - Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations","interactions":[],"lastModifiedDate":"2021-02-01T14:29:48.935866","indexId":"70217747","displayToPublicDate":"2020-11-02T06:35:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations","docAbstract":"Growing worldwide concern over uranium contamination of groundwater resources has placed an emphasis on understanding uranium transport dynamics and potential toxicity in groundwater-surface water systems. In this study, we utilized novel in-situ sampling methods to establish the location and magnitude of contaminated groundwater entry into a receiving surface water environment, and to investigate the speciation and potential bioavailability of uranium in groundwater and surface water. Streambed temperature mapping successfully identified the location of groundwater entry to the Little Wind River, downgradient from the former Riverton uranium mill site, Wyoming, USA. Diffusive equilibrium in thin-film (DET) samplers further constrained the groundwater plume and established sediment pore water solute concentrations and patterns. In this system, evidence is presented for attenuation of uranium-rich groundwater in the shallow sediments where surface water and groundwater interaction occurs. Surface water grab and DET sampling successfully detected an increase in river uranium concentrations where the groundwater plume enters the Little Wind River; however, concentrations remained below environmental guideline levels. Uranium speciation was investigated using diffusive gradients in thin-film (DGT) samplers and geochemical speciation modelling. Together, these investigations indicate uranium may have limited bioavailability to organisms in the Little Wind River and, possibly, in other similar sites in the western U.S.A. This could be due to ion competition effects or the presence of non- or partially labile uranium complexes. Development of methods to establish the location of contaminated (uranium) groundwater entry to surface water environments, and the potential effects on ecosystems, is crucial to develop both site-specific and general conceptual models of uranium behavior and potential toxicity in affected ground and surface water environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.143314","usgsCitation":"Byrne, P.A., Fuller, C.C., Naftz, D.L., Runkel, R.L., Lehto, N.J., and Dam, W., 2021, Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations: Science of the Total Environment, v. 761, 143314, 11 p., https://doi.org/10.1016/j.scitotenv.2020.143314.","productDescription":"143314, 11 p.","ipdsId":"IP-121496","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":454315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1776309","text":"Publisher Index Page"},{"id":382831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","city":"Riverton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.39832305908203,\n 43.00816202648563\n ],\n [\n -108.33995819091797,\n 43.00816202648563\n ],\n [\n -108.33995819091797,\n 43.03175685183966\n ],\n [\n -108.39832305908203,\n 43.03175685183966\n ],\n [\n -108.39832305908203,\n 43.00816202648563\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"761","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick A.","contributorId":247578,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","email":"","middleInitial":"A.","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":809453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":809454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lehto, Niklas J","contributorId":248588,"corporation":false,"usgs":false,"family":"Lehto","given":"Niklas","email":"","middleInitial":"J","affiliations":[{"id":49952,"text":"Lincoln University","active":true,"usgs":false}],"preferred":false,"id":809457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dam, William L","contributorId":248589,"corporation":false,"usgs":false,"family":"Dam","given":"William L","affiliations":[{"id":49955,"text":"Conserve-Prosper LLC","active":true,"usgs":false}],"preferred":false,"id":809458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223195,"text":"70223195 - 2021 - A generic soil velocity model that accounts for near-surface conditions and deeper geologic structure","interactions":[],"lastModifiedDate":"2021-08-17T12:18:34.915965","indexId":"70223195","displayToPublicDate":"2020-10-30T07:16:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3418,"text":"Soil Dynamics and Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A generic soil velocity model that accounts for near-surface conditions and deeper geologic structure","docAbstract":"Near-surface soil conditions can significantly alter the amplitude and frequency content of incoming ground motions – often with profound consequences for the built environment – and are thus important inputs to any ground-motion prediction. Previous soil-velocity models (SVM) have predicted shear-wave velocity profiles based on the time-averaged shear-wave velocity in the upper 30 m (VS30). This article presents a generic soil-velocity model that accounts both for near-surface conditions (VS30) and deeper geologic structure, as represented to the depth at which the profile reaches a velocity of 1.0 km/s (Z1.0). To demonstrate the advantages of our new SVM, we apply it to the Cascadia Region of North America, where numerous geologic basins and glaciated landscapes give rise to a wide range of VS30 and Z1.0 combinations. This soil velocity model yields good estimates of site response across all site conditions, and significantly improves upon a model calibrated using only VS30 data. In conjunction with existing models that describe the deep velocity structure of the region (e.g., (Stephenson et al., 2017) [27]; the proposed model is particularly suited for use in regional-scale predictions of site response, liquefaction, landslides, infrastructure damage, and loss. The proposed methodology is broadly applicable to the development of SVMs elsewhere, and with improved understanding of near-surface and deep velocity structures, can facilitate more accurate ground-motion predictions globally.
International efforts to restore degraded ecosystems will continue to expand over the coming decades, yet the factors contributing to the effectiveness of long‐term restoration across large areas remain largely unexplored. At large scales, outcomes are more complex and synergistic than the additive impacts of individual restoration projects. Here, we propose a cumulative‐effects conceptual framework to inform restoration design and implementation and to comprehensively measure ecological outcomes. To evaluate and illustrate this approach, we reviewed long‐term restoration in several large coastal and riverine areas across the US: the greater Florida Everglades; Gulf of Mexico coast; lower Columbia River and estuary; Puget Sound; San Francisco Bay and Sacramento–San Joaquin Delta; Missouri River; and northeastern coastal states. Evidence supported eight modes of cumulative effects of interacting restoration projects, which improved outcomes for species and ecosystems at landscape and regional scales. We conclude that cumulative effects, usually measured for ecosystem degradation, are also measurable for ecosystem restoration. The consideration of evidence‐based cumulative effects will help managers of large‐scale restoration capitalize on positive feedback and reduce countervailing effects.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2274","usgsCitation":"Diefenderfer, H.L., Steyer, G., Harwell, M.C., LoSchiavo, A.J., Neckles, H.A., Burdick, D.M., Johnson, G.E., Buenau, K.E., Trujillo, E., Callaway, J.C., Thom, R.M., Ganju, N., and Twilley, R.R., 2021, Applying cumulative effects to strategically advance large‐scale ecosystem restoration: Frontiers in Ecology and the Environment, v. 19, no. 2, p. 108-117, https://doi.org/10.1002/fee.2274.","productDescription":"10 p.","startPage":"108","endPage":"117","ipdsId":"IP-107430","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":454325,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2274","text":"Publisher Index Page"},{"id":385579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0002-7364-286X","orcid":"https://orcid.org/0000-0002-7364-286X","contributorId":205456,"corporation":false,"usgs":false,"family":"Callaway","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37110,"text":"Dept. of Environmental Science, University of San Francisco, 2130 Fulton St., San Francisco, CA 94117","active":true,"usgs":false}],"preferred":false,"id":815463,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thom, Ronald M. 0000-0002-8639-6709","orcid":"https://orcid.org/0000-0002-8639-6709","contributorId":257985,"corporation":false,"usgs":false,"family":"Thom","given":"Ronald","email":"","middleInitial":"M.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":815464,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815465,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Twilley, Robert R.","contributorId":34585,"corporation":false,"usgs":false,"family":"Twilley","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":815466,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70216772,"text":"70216772 - 2021 - Evaluation of seismic hazard models with fragile geologic features","interactions":[],"lastModifiedDate":"2021-01-19T16:04:47.842953","indexId":"70216772","displayToPublicDate":"2020-10-28T09:20:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of seismic hazard models with fragile geologic features","docAbstract":"We provide an overview of a 2019 workshop on the use of fragile geologic features (FGFs) to evaluate seismic hazard models. FGFs have been scarcely utilized in the evaluation of seismic hazard models, despite nearly 30 yr having passed since the first recognition of their potential value. Recently, several studies have begun to focus on the implementation of FGFs in seismic hazard modeling. The workshop was held to capture a “snapshot” of the state‐of‐the‐art in FGF work and to define key research areas that would increase confidence in FGF‐based evaluation of seismic hazard models. It was held at the annual meeting of the Southern California Earthquake Center on 8 September 2019, and the conveners were Mark Stirling (University of Otago, New Zealand) and Michael Oskin (University of California, Davis). The workshop attracted 44 participants from a wide range of disciplines. The main topics of discussion were FGF fragility age estimation (age at which an FGF achieved its current fragile geometry), fragility estimation, FGF‐based evaluation of seismic hazard models, and ethical considerations relating to documentation and preservation of FGFs. There are now many scientists working on, or motivated to work on, FGFs, and more types of FGFs are being worked on than just the precariously balanced rock (PBR) variety. One of the ideas presented at the workshop is that fragility ages for FGFs should be treated stochastically rather than assuming that all share a common age. In a similar vein, new studies propose more comprehensive methods of fragility assessment beyond peak ground acceleration and peak ground velocity‐based approaches. Two recent studies that apply PBRs to evaluate probabilistic seismic hazard models use significantly different methods of evaluation. Key research needs identified from the workshop will guide future, focused efforts that will ultimately facilitate the uptake of FGFs in seismic hazard analysis.
The global climate has been changing over the last century due to greenhouse gas emissions and will continue to change over this century, accelerating without effective global efforts to reduce emissions. Ticks and tick-borne diseases (TTBDs) are inherently climate-sensitive due to the sensitivity of tick lifecycles to climate. Key direct climate and weather sensitivities include survival of individual ticks, and the duration of development and host-seeking activity of ticks. These sensitivities mean that in some regions a warming climate may increase tick survival, shorten life-cycles and lengthen the duration of tick activity seasons. Indirect effects of climate change on host communities may, with changes in tick abundance, facilitate enhanced transmission of tick-borne pathogens. High temperatures, and extreme weather events (heat, cold, and flooding) are anticipated with climate change, and these may reduce tick survival and pathogen transmission in some locations. Studies of the possible effects of climate change on TTBDs to date generally project poleward range expansion of geographical ranges (with possible contraction of ranges away from the increasingly hot tropics), upslope elevational range spread in mountainous regions, and increased abundance of ticks in many current endemic regions. However, relatively few studies, using long-term (multi-decade) observations, provide evidence of recent range changes of tick populations that could be attributed to recent climate change. Further integrated ‘One Health’ observational and modeling studies are needed to detect changes in TTBD occurrence, attribute them to climate change, and to develop predictive models of public- and animal-health needs to plan for TTBD emergence.
","language":"English","publisher":"Entomological Society of America","doi":"10.1093/jme/tjaa220","usgsCitation":"Ogden, N.H., Beard, C.B., Ginsberg, H., and Tsao, J.I., 2021, Possible effects of climate change on ixodid ticks and the pathogens they transmit: Predictions and observations: Journal of Medical Entomology, v. 58, no. 4, p. 1536-1545, https://doi.org/10.1093/jme/tjaa220.","productDescription":"10 p.","startPage":"1536","endPage":"1545","ipdsId":"IP-121581","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":454330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjaa220","text":"Publisher Index Page"},{"id":379905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ogden, Nicholas H.","contributorId":147667,"corporation":false,"usgs":false,"family":"Ogden","given":"Nicholas","email":"","middleInitial":"H.","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":803337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Charles B.","contributorId":148018,"corporation":false,"usgs":false,"family":"Beard","given":"Charles","email":"","middleInitial":"B.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":803338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":803339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsao, Jean I.","contributorId":140905,"corporation":false,"usgs":false,"family":"Tsao","given":"Jean","email":"","middleInitial":"I.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":803340,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216439,"text":"70216439 - 2021 - Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona","interactions":[],"lastModifiedDate":"2021-05-14T11:50:15.092644","indexId":"70216439","displayToPublicDate":"2020-10-28T07:37:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona","docAbstract":"Completion of Glen Canyon Dam in 1963 resulted in complete elimination of sediment delivery from the upstream Colorado River basin to Grand Canyon and nearly complete control of spring snowmelt floods responsible for creating channel and bar morphology. Management of the river ecosystem in Grand Canyon National Park now relies on dam-release floods to redistribute tributary-derived sediment accumulated on the channel bed to higher-elevation sandbars. Here, we used multivariate mixing analysis of sediment elemental compositions to evaluate the extent to which flood deposits derive from tributary-supplied sand compared to reworked, relict predam sediment. The concentrations of seven major and trace elements (Fe, Ca, K, Ti, Rb, Sr, and Zr) were measured in very fine−, fine-, and medium-grained sand from flood deposits using X-ray fluorescence and interpreted using a Bayesian mixing model to characterize the proportion of sand originating from the Paria River, the only major tributary within the study reach. Flood deposits from the 2013 and 2014 controlled floods contained 69% ± 16% and 84% ± 20% Paria River−derived material, respectively, with substantial variation among sites. Based on a sand mass balance, we calculated that under decreasing storage conditions since 1963, ∼77%−83% of the annual Paria River sand flux needs to be retained within the mass of active sand stored in Marble Canyon each year to reach the observed concentration of Paria River sand at sample locations. This finding suggests that the use of controlled floods may continue to be effective for sandbar maintenance, provided sand inputs from the Paria River do not decline.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35642.1","usgsCitation":"Chapman, K.A., Best, R.J., Smith, M.E., Mueller, E.R., Grams, P.E., and Parnell, R., 2021, Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona: Geological Society of America Bulletin, v. 133, no. 5-6, p. 1141-1156, https://doi.org/10.1130/B35642.1.","productDescription":"16 p.","startPage":"1141","endPage":"1156","ipdsId":"IP-116064","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436648,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C0IN56","text":"USGS data release","linkHelpText":"Tributary sand input data, Colorado River, Grand Canyon National Park, Arizona"},{"id":380590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 35.71083783530009\n ],\n [\n -111.258544921875,\n 35.71083783530009\n ],\n [\n -111.258544921875,\n 36.958671131530316\n ],\n [\n -114.027099609375,\n 36.958671131530316\n ],\n [\n -114.027099609375,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"5-6","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Chapman, Katherine A. kchapman@usgs.gov","contributorId":5368,"corporation":false,"usgs":true,"family":"Chapman","given":"Katherine","email":"kchapman@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":805134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Best, Rebecca J.","contributorId":198804,"corporation":false,"usgs":false,"family":"Best","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":805135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, M. Elliot","contributorId":255572,"corporation":false,"usgs":false,"family":"Smith","given":"M.","email":"","middleInitial":"Elliot","affiliations":[],"preferred":false,"id":805136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mueller, Erich R. 0000-0001-8202-154X emueller@usgs.gov","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":4930,"corporation":false,"usgs":true,"family":"Mueller","given":"Erich","email":"emueller@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812615,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parnell, Roderic A.","contributorId":41922,"corporation":false,"usgs":true,"family":"Parnell","given":"Roderic A.","affiliations":[],"preferred":false,"id":812616,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216003,"text":"70216003 - 2021 - Carrying capacity of spatially distributed metapopulations","interactions":[],"lastModifiedDate":"2021-01-19T16:35:18.526261","indexId":"70216003","displayToPublicDate":"2020-10-28T07:32:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Carrying capacity of spatially distributed metapopulations","docAbstract":"Carrying capacity is a key concept in ecology. A body of theory, based on the logistic equation, has extended predictions of carrying capacity to spatially distributed, dispersing populations. However, this theory has only recently been tested empirically. The experimental results disagree with some theoretical predictions of when they are extended to a population dispersing randomly in a two-patch system. However, they are consistent with a mechanistic model of consumption on an exploitable resource (consumer–resource model). We argue that carrying capacity, defined as the total equilibrium population, is not a fundamental property of ecological systems, at least in the context of spatial heterogeneity. Instead, it is an emergent property that depends on the population’s intrinsic growth and dispersal rates.
The Black-capped Petrel or Diablotin Pterodroma hasitata has a fragmented and declining population estimated at c.1,000 breeding pairs. On land, the species nests underground in steep ravines with dense understorey vegetation. The only confirmed breeding sites are located in the mountain ranges of Hispaniola in the Caribbean, where habitat loss and degradation are continuing threats. Other nesting populations may still remain undiscovered but, to locate them, laborious in situ nest searches must be conducted over expansive geographical areas. To focus nest-search efforts more efficiently, we analysed the environmental characteristics of Black-capped Petrel nesting habitat and modeled suitable habitat on Hispaniola using openly available environmental datasets. We used a univariate generalized linear model to compare the habitat characteristics of active Black-capped Petrel nests sites with those of potentially available sites (i.e. random pseudo-absences). Elevation, distance to coast, and the influence of tree cover and density emerged as important environmental variables. We then applied multivariate generalized linear models to these environmental variables that showed a significant relationship with petrel nesting activity. We used the top performing model of habitat suitability model to create maps of predicted suitability for Hispaniola. In addition to areas of known petrel activity, the model identified possible nesting areas for Black-capped Petrels in habitats not previously considered suitable. Based on model results, we estimated the total area of predicted suitable nesting habitat for Black-capped Petrels on Hispaniola and found that forest loss due to hurricanes, forest fires, and encroachment from agriculture had severely decreased availability of predicted suitable habitat between 2000 and 2018.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0959270920000490","usgsCitation":"Satge, Y.G., Rupp, E., Brown, A.J., and Jodice, P.G., 2021, Habitat modelling locates nesting areas of the endangered Black-capped Petrel Pterodroma hasitata on Hispaniola and identifies habitat loss: Bird Conservation International, v. 31, no. 4, p. 573-590, https://doi.org/10.1017/S0959270920000490.","productDescription":"18 p.","startPage":"573","endPage":"590","ipdsId":"IP-115786","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454339,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0959270920000490","text":"Publisher Index Page"},{"id":436651,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWJPBD","text":"USGS data release","linkHelpText":"Nesting habitat suitability for the Black-capped Petrel Pterodroma hasitata on Hispaniola, Supplementary Material"},{"id":395771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Dominican Republic, Haiti","otherGeospatial":"Hispaniola","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.09326171875,\n 18.823116948090494\n ],\n [\n -69.2138671875,\n 19.621892180319374\n ],\n [\n -71.136474609375,\n 20.159098270646936\n ],\n [\n -72.916259765625,\n 20.24158281954221\n ],\n [\n -73.71826171874999,\n 19.766703551716976\n ],\n [\n -74.92675781249999,\n 18.396230138028827\n ],\n [\n -73.201904296875,\n 17.602139123350838\n ],\n [\n -71.47705078125,\n 17.45547257997284\n ],\n [\n -68.79638671875,\n 17.90556881196468\n ],\n [\n -68.21411132812499,\n 18.3336694457713\n ],\n [\n -68.09326171875,\n 18.823116948090494\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Satge, Y. 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J.","contributorId":197185,"corporation":false,"usgs":false,"family":"Brown","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":834275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834276,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215758,"text":"70215758 - 2021 - Surface elevation change evaluation in mangrove forests using a low‐cost, rapid‐scan terrestrial laser scanner","interactions":[],"lastModifiedDate":"2021-01-19T16:39:46.16129","indexId":"70215758","displayToPublicDate":"2020-10-26T08:13:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7183,"text":"Limnology and Oceanography Methods","active":true,"publicationSubtype":{"id":10}},"title":"Surface elevation change evaluation in mangrove forests using a low‐cost, rapid‐scan terrestrial laser scanner","docAbstract":"Mangrove forests have adapted to sea level rise (SLR) increases by maintaining their forest floor elevation via belowground root growth and surface sediment deposits. Researchers use surface elevation tables (SETs) to monitor surface elevation change (SEC) in mangrove forests, after which this information is used to assess SLR resiliency or to dictate active forest management for vulnerable systems. This method requires significant investments in terms of time and human resources and is limited in the number of points it can measure per plot. We use a low‐cost, portable terrestrial laser scanning (TLS) system to assess SEC for three mangrove forests on Pohnpei Island (Federated States of Micronesia). Cloth simulation filtering was used for ground detection, after which results were refined by filtering points using angular orientation. Digital elevation models then were generated via kriging interpolation for data collected in 2017 and 2019, after which the heights of corresponding points were compared across years. Extreme elevation changes, due to disturbances such as footprints or fallen logs, were removed using interquartile range analysis. The TLS‐obtained average SEC ranged between −6.92 and +6.01 mm, which exhibited an average consistency of 72% when compared to simultaneously collected SET data (root mean square error = 1.36 mm). We contend that this approach represents an improvement over the manual method, where very few points typically are used, that is, ≅ 36 points vs. ≅ 30,000 points in the case of TLS, and could contribute to improved monitoring and management of these rapidly changing forest environments.
The western purple martin (Progne subis arboricola), an avian insectivore, is a species of conservation concern throughout the Pacific Northwest. Compared to the well-studied eastern subspecies (Progne subis subis), little is known of the life history and biology of the western subspecies. Availability of breeding habitat is believed to be a major limiting factor for western purple martins in forested habitat, but fundamental information on their current distribution and selection of nesting habitat is deficient. To fill this gap, we compared habitat characteristics at three spatial scales (snag-level, stand-level [48.6 ha], landscape-level [314 ha]) surrounding nest snags occupied by purple martins in western Oregon to unoccupied sites. We found habitat for nesting purple martins was defined by the presence of moderately decayed snags with nest cavities, located well away from closed-canopy forest in sufficiently large (>15 ha) open areas. Our modeling efforts suggested suitable habitat was rare within the study region because: 1) snags were scarce on private industrial forest lands and 2) large disturbed patches were uncommon on federal lands. We conclude that a disturbance regime characterized by infrequent but major stand-replacing events, such as fire or timber harvest, is likely the key to maintaining breeding habitat for purple martins in upland forests in western Oregon.
System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.
The Failure Forecast Method (FFM) was introduced as an empirical model for forecasting catastrophic material failures related to natural hazards, such as landslides and volcanic eruptions, with mixed success. During the 2018 eruption of Kilauea volcano, Hawaii, the draining of the summit magma reservoir into the Lower East Rift Zone resulted in the formation of a new caldera at the summit. I tested the applicability of the FFM to caldera collapse by analyzing the cyclical earthquake swarms and ground deformation that occurred between 62 sudden major caldera collapse events. The progression of both the cumulative moment release of the cyclical earthquakes and the GNSS displacement show a major change in mid-June. In late May through early June, the progression of the parameters is consistent with strain localization or creep progression related to the development or activation of the ring fault system. From late June until the end of the eruption, parameter progression is roughly steady with initial accelerating increases in cumulative moment and displacement that shift to approximately linear progression. Analysis of repeating earthquake families in the cyclical swarms showed that the behavior of the repeaters was consistent with that of the cyclical swarms as a whole and suggested that each family undergoes its own progression of activation to termination. While the FFM analysis identified the system change in mid-June, it did not demonstrate an ability to forecast collapse events or the end of the eruption.
The scarcity of strong ground motion records presents a challenge for making reliable performance assessments of tall buildings whose seismic design is controlled by large-magnitude and close-distance earthquakes. This challenge can be addressed using broadband ground-motion simulation methods to generate records with site-specific characteristics of large-magnitude events. In this paper, simulated site-specific earthquake seismograms, developed through a related project that was organized through the Southern California Earthquake Center (SCEC) Ground Motion Simulation Validation (GMSV) Technical Activity Group, are used for nonlinear response history analyses of two archetype tall buildings for sites in San Francisco, Los Angeles, and San Bernardino. The SCEC GMSV team created the seismograms using the Broadband Platform (BBP) simulations for five site-specific earthquake scenarios. The two buildings are evaluated using nonlinear dynamic analyses under comparable record suites selected from the simulated BBP catalog and recorded motions from the NGA-West database. The collapse risks and structural response demands (maximum story drift ratio, peak floor acceleration, and maximum story shear) under the BBP and NGA suites are compared. In general, this study finds that use of the BBP simulations resolves concerns about estimation biases in structural response analysis which are caused by ground motion scaling, unrealistic spectral shapes, and overconservative spectral variations. While there are remaining concerns that strong coherence in some kinematic fault rupture models may lead to an overestimation of velocity pulse effects in the BBP simulations, the simulations are shown to generally yield realistic pulse-like features of near-fault ground motion records.
","language":"English","publisher":"Wiley","doi":"10.1002/eqe.3364","usgsCitation":"Zhong, K., Lin, T., Deierlein, G., Graves, R., Silva, F., and Luco, N., 2021, Tall building performance-based seismic design using SCEC broadband platform site-specific ground motion simulations: Earthquake Engineering and Structural Dynamics, v. 50, no. 1, p. 81-98, https://doi.org/10.1002/eqe.3364.","productDescription":"18 p.","startPage":"81","endPage":"98","ipdsId":"IP-119067","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":454358,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2346/88059","text":"External Repository"},{"id":387857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles, San Bernardino, San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.39790916442873,\n 37.78178983833927\n ],\n [\n -122.3886823654175,\n 37.78178983833927\n ],\n [\n -122.3886823654175,\n 37.78921753609959\n ],\n [\n -122.39790916442873,\n 37.78921753609959\n ],\n [\n -122.39790916442873,\n 37.78178983833927\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.26359510421753,\n 34.05214385256302\n ],\n [\n -118.25709342956542,\n 34.05214385256302\n ],\n [\n -118.25709342956542,\n 34.0581704783008\n ],\n [\n -118.26359510421753,\n 34.0581704783008\n ],\n [\n -118.26359510421753,\n 34.05214385256302\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.29613304138182,\n 34.09673784258847\n ],\n [\n -117.27553367614746,\n 34.09673784258847\n ],\n [\n -117.27553367614746,\n 34.11812893261633\n ],\n [\n -117.29613304138182,\n 34.11812893261633\n ],\n [\n -117.29613304138182,\n 34.09673784258847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhong, Kuanshi","contributorId":264127,"corporation":false,"usgs":false,"family":"Zhong","given":"Kuanshi","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":820928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Ting","contributorId":264128,"corporation":false,"usgs":false,"family":"Lin","given":"Ting","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":820929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deierlein, Greg","contributorId":264129,"corporation":false,"usgs":false,"family":"Deierlein","given":"Greg","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":820930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":820931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silva, Fabio","contributorId":264130,"corporation":false,"usgs":false,"family":"Silva","given":"Fabio","email":"","affiliations":[{"id":54387,"text":"SCEC","active":true,"usgs":false}],"preferred":false,"id":820932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820933,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229080,"text":"70229080 - 2021 - Balancing transferability and complexity of species distribution models for rare species conservation","interactions":[],"lastModifiedDate":"2022-02-28T15:12:14.941976","indexId":"70229080","displayToPublicDate":"2020-10-20T09:07:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Balancing transferability and complexity of species distribution models for rare species conservation","docAbstract":"Species distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species occurrence is unknown (i.e., transferability). Spatially structured cross-validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross-validation folds affects model complexity and predictions. We developed SDMs using multiple cross-validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations.
Idaho, USA.
We delineated cross-validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross-validation approach, we selected optimal regularization and model complexity based on out-of-sample predictive ability.
Delineation of cross-validation folds substantially affected resulting model complexity and extrapolated predictions. All cross-validation approaches resulted in models with apparently high out-of-sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values.
Subtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross-validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross-validation may not guard against over-parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.
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Nolan A.","contributorId":287004,"corporation":false,"usgs":false,"family":"Helmstetter","given":"Nolan","email":"","middleInitial":"A.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":836429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":836430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldberg, Amanda R.","contributorId":280029,"corporation":false,"usgs":false,"family":"Goldberg","given":"Amanda R.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":836431,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219196,"text":"70219196 - 2021 - Signatures of hydrologic function across the critical zone observatory network","interactions":[],"lastModifiedDate":"2021-03-30T12:05:44.485187","indexId":"70219196","displayToPublicDate":"2020-10-18T06:50:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Signatures of hydrologic function across the critical zone observatory network","docAbstract":"Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026635","usgsCitation":"Wlostowski, A.N., Molotch, N.P., Anderson, S.P., Brantley, S.L., Chorover, J., Dralle, D., Kumar, P., Li, L., Lohse, K.A., Mallard, J., McIntosh, J.C., Murphy, S.F., Parrish, E., Safeeq, M., Seyfried, M., Shi, Y., and Harman, C., 2021, Signatures of hydrologic function across the critical zone observatory network: Water Resources Research, v. 57, no. 3, e2019WR026635, 28 p., https://doi.org/10.1029/2019WR026635.","productDescription":"e2019WR026635, 28 p.","ipdsId":"IP-117846","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr026635","text":"Publisher Index 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Eric","contributorId":256760,"corporation":false,"usgs":false,"family":"Parrish","given":"Eric","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":813184,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Safeeq, Mohammad 0000-0003-0529-3925","orcid":"https://orcid.org/0000-0003-0529-3925","contributorId":77814,"corporation":false,"usgs":false,"family":"Safeeq","given":"Mohammad","email":"","affiliations":[{"id":6641,"text":"University of California at Merced","active":true,"usgs":false}],"preferred":false,"id":813185,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Seyfried, Mark 0000-0001-8081-0713","orcid":"https://orcid.org/0000-0001-8081-0713","contributorId":256763,"corporation":false,"usgs":false,"family":"Seyfried","given":"Mark","email":"","affiliations":[{"id":51849,"text":"United States Department of Agriculture - Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813186,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shi, Yuning 0000-0003-0118-5847","orcid":"https://orcid.org/0000-0003-0118-5847","contributorId":256765,"corporation":false,"usgs":false,"family":"Shi","given":"Yuning","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":813187,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Harman, Ciaran 0000-0002-3185-002X","orcid":"https://orcid.org/0000-0002-3185-002X","contributorId":242780,"corporation":false,"usgs":false,"family":"Harman","given":"Ciaran","email":"","affiliations":[{"id":48526,"text":"Department of Environmental Health and Engineering, Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":813188,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70254939,"text":"70254939 - 2021 - Hierarchical computing for hierarchical models in ecology","interactions":[],"lastModifiedDate":"2024-06-12T00:14:56.560958","indexId":"70254939","displayToPublicDate":"2020-10-17T19:13:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Hierarchical computing for hierarchical models in ecology","docAbstract":"Increasing aridity is a challenge for forest managers and reducing stand density to minimize competition is a recognized strategy to mitigate drought impacts on growth. In many dry forests, the most widespread and common forest management programs currently being implemented focus on restoration of historical stand structures, primarily to minimize fire risk and enhance watershed function. The implications of these restoration projects for drought vulnerability are not well understood. Here, we examined how planned restoration treatments in the Four Forests Restoration Initiative, the largest forest restoration project in the United States, would alter landscape‐scale patterns of forest growth and drought vulnerability throughout the 21st century. Using drought‐growth relationships developed within the landscape, we considered a suite of climate and treatment scenarios and estimated average forest growth and the proportion of years with extremely low growth as a measure of vulnerability to long‐term decline. Climatic shifts projected for this landscape include higher temperatures and shifting seasonal precipitation that promotes lower soil moisture availability in the early growing season and greater hot‐dry stress, conditions negatively associated with tree growth. However, drought severity and the magnitude of future growth declines was moderated by the thinning treatments. Compared to historical conditions, proportional growth in mid‐century declines by ~40% if thinning ceases or continues at the status quo pace. By comparison, proportional growth declines by only 20% if the Four Forest Restoration Initiative treatments are fully implemented, and < 10% if stands are thinned even more intensively than currently planned. Furthermore, restoration treatments resulted in dramatically fewer years with extremely low growth in the future, a recognized precursor to forest decline and eventual tree mortality. Benefits from density reduction for mitigating drought‐induced growth declines are more apparent in mid‐century and under RCP4.5 than under RCP8.5 at the end of the century. Future climate is inherently uncertain, and our results only reflect the climate projections from the representative suite of models examined. Nevertheless, these results indicate that forest restoration projects designed for other objectives also have substantial benefits for minimizing future drought vulnerability in dry forests and provide additional incentive to accelerate the pace of restoration.
Releases of oil and gas (OG) wastewaters can have complex effects on stream-water quality and downstream organisms, due to sediment-water interactions and groundwater/surface water exchange. Previously, elevated concentrations of sodium (Na), chloride (Cl), barium (Ba), strontium (Sr), and lithium (Li), and trace hydrocarbons were determined to be key markers of OG wastewater releases when combined with Sr and radium (Ra) isotopic compositions. Here, we assessed the persistence of an OG wastewater spill in a creek in North Dakota using a combination of geochemical measurements and modeling, hydrologic analysis, and geophysical investigations. OG wastewater comprised 0.1 to 0.3% of the stream-water compositions at downstream sites in February and June 2015 but could not be quantified in 2016 and 2017. However, OG-wastewater markers persisted in sediments and pore water for 2.5 years after the spill and up to 7.2-km downstream from the spill site. Concentrations of OG wastewater constituents were highly variable depending on the hydrologic conditions. Electromagnetic measurements indicated substantially higher electrical conductivity under the bank adjacent to a seep 7.2 km downstream from the spill site. Geomorphic investigations revealed mobilization of sediment is an important contaminant transport process. Labile Ba, Ra, Sr, and ammonium (NH4) concentrations extracted from sediments indicated sediments are a long-term reservoir of these constituents, both in the creek and on the floodplain. Using the drivers of ecological effects identified at this intensively studied site we identified 41 watersheds across the North Dakota landscape that may be subject to similar episodic inputs from OG wastewater spills. Effects of contaminants released to the environment during OG waste management activities remain poorly understood; however, analyses of Ra and Sr isotopic compositions, as well as trace inorganic and organic compound concentrations at these sites in pore-water provide insights into potentials for animal and human exposures well outside source-remediation zones.
The eastern Adirondack Highlands of northern New York host dozens of iron oxide-apatite (IOA) deposits containing magnetite and rare earth element (REE)-bearing apatite. We use new aeromagnetic, aeroradiometric, ground gravity, and sample petrophysical and geochemical data to image and understand these deposits and their geologic framework. Aeromagnetic total field data reflect highly magnetic leucogranite host rock and major structures that likely served as fluid conduits for the hydrothermal system. Bandpass filtering of the aeromagnetic data reveals individual deposits that were verified in the field or from historical records. A three-dimensional inversion for magnetic susceptibility images these deposits at depth, allowing inference of plunge directions and relative size. Radiometric data highlight variations in the surface geology and several large tailings piles that contain REE-bearing apatite. Within the host rock, eTh (equivalent Th), K and the eTh/K ratio are variable with high eTh/K near several of the IOA deposits. Areas with elevated K or low eTh/K representing potassic alteration appear to be rare; instead elevated eTh/K ratios likely reflect widespread sodic alteration overprinting potassic alteration. Bouguer gravity anomalies show limited correspondence to the surface geology, radiometric data, or magnetic data, but do exhibit ~10-km wide highs in areas where deposits are observed. Two-dimensional forward models of the gravity and magnetic data show that deeper dense material beneath the leucogranite is quantitatively feasible. If these dense rocks represent intrusions that were emplaced or still cooling at the time of mineralization, they may have served as a heat source that helped to drive the hydrothermal system. Combining datasets, we find that deposits occur towards the distal ends of major structures within the host leucogranite and mostly above gravity highs. The geophysical modeling thus suggests that IOA deposits formed in structural, thermal, and chemical traps near the distal ends of the hydrothermal system.
","language":"English","publisher":"Society for Exploration Geophysics","doi":"10.1190/geo2019-0783.1","usgsCitation":"Shah, A.K., Taylor, R.D., Walsh, G.J., and Phillips, J., 2021, Integrated geophysical imaging of rare-earth-element-bearing iron oxide-apatite deposits in the eastern Adirondack Highlands, New York: Geophysics, v. 86, no. 1, p. B37-B54, https://doi.org/10.1190/geo2019-0783.1.","productDescription":"18 p.","startPage":"B37","endPage":"B54","ipdsId":"IP-117777","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/geo2019-0783.1","text":"Publisher Index Page"},{"id":380582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2451171875,\n 43.42100882994726\n ],\n [\n -73.6083984375,\n 43.42100882994726\n ],\n [\n -73.6083984375,\n 44.809121700077355\n ],\n [\n -76.2451171875,\n 44.809121700077355\n ],\n [\n -76.2451171875,\n 43.42100882994726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805129,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":245004,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Gregory J. 0000-0003-4264-8836 gwalsh@usgs.gov","orcid":"https://orcid.org/0000-0003-4264-8836","contributorId":873,"corporation":false,"usgs":true,"family":"Walsh","given":"Gregory","email":"gwalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":805131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}