An experiment along with simulation modeling was applied to study the combinations of herbicide treatment and biological control that best limit invasive water hyacinth (Pontederia crassipes, formerly Eichhornia crassipes) in freshwater aquatic systems. The experiment consisted of 14 different treatments of P. crassipes in 1.67 m2 outdoor tank mesocosms. Seven treatments were with and seven were without insect biological control agents, Neochetina eichhorniae. In both of the sets of seven treatments, there was one no-herbicide treatment, a one-time full-strength herbicide treatment with 40 %, 80 % and 100 % coverage of the P. crassipes, and a one-time half-strength herbicide treatment with 40 %, 80 %, and 100 % surface area coverage. An overarching hypothesis was that leaving part of a tank unsprayed, providing habitat for the maintenance of biological control agents, would optimize control. Data from the experiment, measured on five days over the 167-day period, were used to calibrate a difference equation model of P. crassipes with and without the biological control agent. The model was then used to project longer term dynamics of the system. The model predicted that an initial one-time herbicide treatment, combined with application of the biocontrol agent at 80 % areal coverage, could maintain P. crassipes at levels lower than the carrying capacity of the plant's biomass over the long term, though not enough that N. eichhorniae would be considered, by itself, a highly effective control. However, the results suggest that a combination of biocontrol with 80 % spraying coverage every 600 days or so would be an effective integrated biocontrol strategy for maintaining decreased P. crassipes biomass at low levels over the long term.
This report addresses system characterization of the Pléiades Neo satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Pléiades Neo is a constellation of four identical very-high-resolution optical satellites operated by Airbus Defence and Space. The first two satellites, Pléiades Neo-3 and -4, were launched in April and August 2021, respectively. The next two satellites, launched in December 2022, did not reach orbit because of Vega-C launch vehicle failure. Pléiades Neo provides several technical improvements to previous Pléiades-HR satellites, including the addition of coastal aerosol (deep blue) and red edge spectral bands, with improved ground sample distance and swath. The Pléiades Neo satellites were designed and built by Airbus Defence and Space with the high-resolution, multispectral imager for Earth imaging and use the S950 optical satellite bus. The high-resolution sensor on Pléiades Neo collects Earth data in the visible and near-infrared region with six bands and a panchromatic band. The satellites can operate off nadir to achieve a revisit of less than 1 day. More information on Pléiades Neo satellites and sensors is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium#) and from the manufacturer (https://www.intelligence-airbusds.com/imagery/constellation/pleiades-Neo/).
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Pléiades Neo has an interior geometric performance in the range of 0.01 meter (m; 0.008 pixel) to −0.017 m (−0.014 pixel) in band-to-band registration; an exterior geometric performance in the range of −7.015 m (−0.702 pixel) to 3.846 m (0.385 pixel) offset in comparison to Sentinel-2 using ground control points of 2.2 to 7.2 m (95-percent circular error); a radiometric performance in the range of −0.070 (minimum) to −0.053 (maximum) in offset and 1.107 (minimum) to 1.202 (maximum) in slope; and a spatial performance in the range of 1.002 to 1.226 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.22 to 0.34 (bands 2–7).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System Characterization of Earth Observation Sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030P","usgsCitation":"Cantrell, S.J., Sampath, A., Vrabel, J.C., Bresnahan, P., Anderson, C., Kim, M., and Park, S., 2023, System characterization report on the Pléiades Neo Imager (ver. 1.1, April 2024), chap. P of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 52 p., https://doi.org/10.3133/ofr20211030P.","productDescription":"Report: vi, 52 p.; Version History","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-154436","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":422656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/p/coverthb2.jpg"},{"id":422657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/p/ofr20211030p.pdf","text":"Report","size":"21.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–P"},{"id":422658,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/p/ofr20211030p.XML"},{"id":428107,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/p/versionHist.txt","text":"Version History","size":"1.99 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: November 16, 2023; Version 1.1: April 29, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The 2012 Little Bear Fire caused substantial vegetation loss in the Eagle Creek Basin of south-central New Mexico. This loss was expected to alter the localized hydrologic response to precipitation by creating conditions that amplify surface runoff, which might alter the geomorphology of North Fork Eagle Creek, a major tributary to Eagle Creek. To monitor short-term geomorphic change, annual geomorphic surveys of North Fork Eagle Creek were conducted from 2017 to 2021. The surveys measured 14 cross sections, stream gradients, woody debris accumulations, and pools found within the study reach. During the 2017–21 study period, the study reach experienced multiple high-flow events that resulted from both monsoonal rainfall and snowmelt runoff. Comparisons of the cross-section and channel profile data for the repeat geomorphic surveys indicate localized erosion and deposition occurred as a result of the high-flow events but overall study reach geomorphology shower little change through the study period. Additionally, the number of woody debris accumulations and pools increased during the study period. Evidence from the 5-year geomorphic survey indicates that the North Fork Eagle Creek’s geomorphology did not change substantially during the study period. Wildfire severity and frequency within mountainous regions of the Southwest are projected to increase and their effect on fluvial systems remains uncertain; however, continued geomorphic studies can provide informative insight on watershed post-wildfire resiliency and recovery by establishing baselines that can be used in the event of a future severe wildfire within the Eagle Creek Basin.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Many marine fish species are experiencing population declines, but their extinction risk profiles are largely understudied in comparison to their terrestrial vertebrate counterparts. Selective extinction of marine fish species may result in rapid alteration of the structure and function of ocean ecosystems. In this study, we compiled an ecological trait dataset for 8,185 species of marine ray-finned fishes (class Actinopterygii) from FishBase and used phylogenetic generalized linear models to examine which ecological traits are associated with increased extinction risk, based on the International Union for the Conservation of Nature Red List. We also assessed which threat types may be driving these species toward greater extinction risk and whether threatened species face a greater average number of threat types than non-threatened species. We found that larger body size and/or fishes with life histories involving movement between marine, brackish, and freshwater environments are associated with elevated extinction risk. Commercial harvesting threatens the greatest number of species, followed by pollution, development, and then climate change. We also found that threatened species, on average, face a significantly greater number of threat types than non-threatened species. These results can be used by resource managers to help address the heightened extinction risk patterns we found.
Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.
","language":"English","publisher":"MDPI","doi":"10.3390/su152215887","usgsCitation":"Mekonen, S.S., Boyce, S.E., Mohammed, A.K., Flint, L.E., Flint, A., and Disse, M., 2023, Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley: Sustainability, v. 15, no. 22, 15887, 25 p., https://doi.org/10.3390/su152215887.","productDescription":"15887, 25 p.","ipdsId":"IP-146940","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su152215887","text":"Publisher Index Page"},{"id":426968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Kobo Valley, Riya Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.36,\n 12.88\n ],\n [\n 39.36,\n 11.92\n ],\n [\n 39.84,\n 11.92\n ],\n [\n 39.84,\n 12.88\n ],\n [\n 39.36,\n 12.88\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonen, Sisay Simachew","contributorId":333048,"corporation":false,"usgs":false,"family":"Mekonen","given":"Sisay","email":"","middleInitial":"Simachew","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, Abdella K.","contributorId":333049,"corporation":false,"usgs":false,"family":"Mohammed","given":"Abdella","email":"","middleInitial":"K.","affiliations":[{"id":79718,"text":"Hydraulic and Water Resources Engineering, Arba Minch University","active":true,"usgs":false}],"preferred":false,"id":897194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":897195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L 0000-0002-5118-751X","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":239656,"corporation":false,"usgs":false,"family":"Flint","given":"Alan L","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":897196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Disse, Markus","contributorId":333050,"corporation":false,"usgs":false,"family":"Disse","given":"Markus","email":"","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250262,"text":"70250262 - 2023 - Global dataset of soil organic carbon in tidal marshes","interactions":[],"lastModifiedDate":"2023-11-30T13:27:20.115335","indexId":"70250262","displayToPublicDate":"2023-11-11T07:25:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Global dataset of soil organic carbon in tidal marshes","docAbstract":"Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.
Volcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade-long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state-of-the-art models of meteorology, emissions, and dispersion. The framework is open-source and introduces a new dynamic plume-rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12-day eruption period model performance varied between days with trade and non-trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JD039281","usgsCitation":"Moisseeva, N., Businger, S., and Elias, T., 2023, VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i: Journal of Geophysical Research Atmospheres, v. 128, no. 22, e2023JD039281, 14 p., https://doi.org/10.1029/2023JD039281.","productDescription":"e2023JD039281, 14 p.","ipdsId":"IP-153377","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jd039281","text":"Publisher Index Page"},{"id":463904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.7156069329796,\n 19.56532392435213\n ],\n [\n -155.7156069329796,\n 19.345990597059426\n ],\n [\n -155.48166856061957,\n 19.345990597059426\n ],\n [\n -155.48166856061957,\n 19.56532392435213\n ],\n [\n -155.7156069329796,\n 19.56532392435213\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moisseeva, Nadya 0000-0001-7317-1597","orcid":"https://orcid.org/0000-0001-7317-1597","contributorId":335180,"corporation":false,"usgs":false,"family":"Moisseeva","given":"Nadya","email":"","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":918411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Businger, Steven","contributorId":345757,"corporation":false,"usgs":false,"family":"Businger","given":"Steven","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":918412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918413,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250390,"text":"70250390 - 2023 - A review of grass carp and related species literature on diet, behavior, toxicology, and physiology focused on informing development of controls for invasive grass carp populations in North America","interactions":[],"lastModifiedDate":"2023-12-06T13:22:40.49949","indexId":"70250390","displayToPublicDate":"2023-11-10T07:21:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"title":"A review of grass carp and related species literature on diet, behavior, toxicology, and physiology focused on informing development of controls for invasive grass carp populations in North America","docAbstract":"Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument’s pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over
Adaptive plasticity in thermal tolerance traits may buffer organisms against changing temperatures, making such responses of particular interest in the face of global climate change. Although population variation is integral to the evolvability of this trait, many studies inferring proxies of physiological vulnerability from thermal tolerance traits extrapolate data from one or a few populations to represent the species. Estimates of physiological vulnerability can be further complicated by methodological effects associated with experimental design. We evaluated how populations varied in their acclimation capacity (i.e., the magnitude of plasticity) for critical thermal maximum (CTmax) in two species of tailed frogs (Ascaphidae), cold-stream specialists. We used the estimates of acclimation capacity to infer physiological vulnerability to future warming. We performed CTmax experiments on tadpoles from 14 populations using a fully factorial experimental design of two holding temperatures (8 and 15°C) and two experimental starting temperatures (8 and 15°C). This design allowed us to investigate the acute effects of transferring organisms from one holding temperature to a different experimental starting temperature, as well as fully acclimated responses by using the same holding and starting temperature. We found that most populations exhibited beneficial acclimation, where CTmax was higher in tadpoles held at a warmer temperature, but populations varied markedly in the magnitude of the response and the inferred physiological vulnerability to future warming. We also found that the response of transferring organisms to different starting temperatures varied substantially among populations, although accounting for acute effects did not greatly alter estimates of physiological vulnerability at the species level or for most populations. These results underscore the importance of sampling widely among populations when inferring physiological vulnerability, as population variation in acclimation capacity and thermal sensitivity may be critical when assessing vulnerability to future warming.
Debris flows regularly traverse bedrock channels that dissect steep landscapes, but our understanding of bedrock erosion by debris flows and their impact on steepland morphology is still rudimentary. Quantitative models of steep bedrock channel networks are based on geomorphic transport laws designed to represent erosion by water-dominated flows. To quantify the impact of debris flow erosion on steep channel network form, it is first necessary to develop methods to estimate spatial variations in bulk debris flow properties (e.g., flow depth, velocity) throughout the channel network that can be integrated into landscape evolution models. Here, we propose and evaluate two methods to estimate spatial variations in bulk debris flow properties along the length of a channel profile. We incorporate both methods into a model designed to simulate the evolution of longitudinal channel profiles that evolve in response to debris flow and fluvial processes. To explore this model framework, we propose a general family of debris flow erosion laws where erosion rate is a function of debris flow depth and channel slope. Model results indicate that erosion by debris flows can explain the occurrence of a scaling break in the slope–area curve at low-drainage areas and that upper-network channel morphology may be useful for inferring catchment-averaged erosion rates in quasi-steady landscapes. Validating specific forms of a debris flow incision law, however, would require more detailed model–data comparisons in specific landscapes where input parameters and channel morphometry can be better constrained. Results improve our ability to interpret topographic signals within steep channel networks and identify observational targets critical for constraining a debris flow incision law.
Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a. Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.
Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote (Canis latrans), bobcat (Lynx rufus), red fox (Vulpes vulpes), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana)) at 12 study sites along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from camera traps to calculate the density of five focal species. Coyote density ranged from 0.5 to 0.93 individuals/km2. Raccoon density ranged from 0.19 to 20.25 individuals/km2. Bobcat density ranged from 0 to 1.06 individuals/km2. Opossum density ranged from 0 to 3.43 individuals/km2. Red fox density ranged from 0 to 0.10 individuals/km2. Coyote and raccoon density showed a positive relationship with anthropogenic noise. Opossum density increased with HUD. Red Fox and bobcat density showed a negative relationship with forest area and a positive relationship with distance to water respectively, however confidence intervals for both species overlapped zero. The density estimates we report based on camera trap data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species. Our second analysis consisted of taking camera level density estimates and treating them as detection rates corrected for camera viewshed and animal movement. Coyote and raccoon detection rate showed a positive relationship with anthropogenic noise. Red Fox detection rate was positively related to developed open space, and negatively related to distance to water. Similarly to red fox, opossums detection rate was higher in areas with more developed open space. We found no evidence that bobcat density or detection rate varied with any of the landcover or anthropogenic variables we measured.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2023.e02716","usgsCitation":"McTigue, L., and DeGregorio, B.A., 2023, Effects of landcover on mesocarnivore density and detection rate along an urban to rural gradient: Global Ecology and Conservation, v. 48, e02716, 14 p., https://doi.org/10.1016/j.gecco.2023.e02716.","productDescription":"e02716, 14 p.","ipdsId":"IP-149643","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":441657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2023.e02716","text":"Publisher Index Page"},{"id":432148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","city":"Fayetteville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.703264661285,\n 36.581012567484365\n ],\n [\n -94.703264661285,\n 35.69179592709919\n ],\n [\n -93.38479477781208,\n 35.69179592709919\n ],\n [\n -93.38479477781208,\n 36.581012567484365\n ],\n [\n -94.703264661285,\n 36.581012567484365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McTigue, Leah","contributorId":310420,"corporation":false,"usgs":false,"family":"McTigue","given":"Leah","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907389,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250689,"text":"70250689 - 2023 - Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","interactions":[],"lastModifiedDate":"2023-12-27T12:49:16.223157","indexId":"70250689","displayToPublicDate":"2023-11-08T06:46:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7120,"text":"Limnology & Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","docAbstract":"In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) with lake DOC, TN, and TP concentrations and high frequency in situ monitoring of dissolved oxygen. We found that stream load stoichiometry indicated lake stoichiometry for C : N and C : P (r2 = 0.74 and r2 = 0.84, respectively), but not for N : P (r2 = 0.04). As we found a strong positive correlation between TN and TP, we only used TP in our statistical models. For the catchment model, GPP and R were best predicted by DOC load, TP load, and load N : P (R2 = 0.85 and R2 = 0.82, respectively). For the lake model, GPP and R were best predicted by TP concentrations (R2 = 0.86 and R2 = 0.67, respectively). The inclusion of N : P in the catchment model, but not the lake model, suggests that both N and P regulate metabolism and that organisms may be responding more strongly to catchment inputs than lake resources. Our models predicted NEP poorly, though it is unclear why. Overall, our work stresses the importance of characterizing lake catchment loads to predict metabolic rates, a result that may be particularly important in catchments experiencing changing hydrologic regimes related to global environmental change.
Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 MWdc or more. Ground mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. Analysts from U.S.
Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.
Survey and Lawrence Berkeley National Laboratory collaborated to develop the United States Large-Scale Solar Photovoltaic Database (USPVDB). Using Energy Information Administration (EIA) data, locations of LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available in an interactive web application, and a number of downloadable formats, including: comma-separated value spreadsheet (CSV), application programming interface (API), and GIS shapefile.
","language":"English","publisher":"Nature","doi":"10.1038/s41597-023-02644-8","usgsCitation":"Fujita, K.S., Ancona, Z.H., Kramer, L., Straka, M., Gautreau, T.E., Robson, D., Garrity, C.P., Hoen, B., and Diffendorfer, J., 2023, Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States: Scientific Data, v. 10, 760, 14 p., https://doi.org/10.1038/s41597-023-02644-8.","productDescription":"760, 14 p.","ipdsId":"IP-152694","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441671,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02644-8","text":"Publisher Index Page"},{"id":435129,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IA3TUS","text":"USGS data release","linkHelpText":"United States 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Sydny","contributorId":331485,"corporation":false,"usgs":false,"family":"Fujita","given":"K.","middleInitial":"Sydny","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":887829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":887830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kramer, Louisa 0000-0002-6776-9768","orcid":"https://orcid.org/0000-0002-6776-9768","contributorId":204878,"corporation":false,"usgs":true,"family":"Kramer","given":"Louisa","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":887831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Straka, Mary","contributorId":331486,"corporation":false,"usgs":false,"family":"Straka","given":"Mary","email":"","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":887832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gautreau, Tandie E.","contributorId":331487,"corporation":false,"usgs":false,"family":"Gautreau","given":"Tandie","email":"","middleInitial":"E.","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":887833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robson, Dana","contributorId":331488,"corporation":false,"usgs":false,"family":"Robson","given":"Dana","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":887834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garrity, Christopher P. 0000-0002-5565-1818 cgarrity@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-1818","contributorId":644,"corporation":false,"usgs":true,"family":"Garrity","given":"Christopher","email":"cgarrity@usgs.gov","middleInitial":"P.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":887835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoen, Ben 0000-0002-9512-5572","orcid":"https://orcid.org/0000-0002-9512-5572","contributorId":204879,"corporation":false,"usgs":false,"family":"Hoen","given":"Ben","email":"","affiliations":[{"id":37001,"text":"DOE Lawrence Berkeley National Labs","active":true,"usgs":false}],"preferred":false,"id":887836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":887837,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250055,"text":"70250055 - 2023 - Empirical estimation of habitat suitability for rare plant restoration in an era of ongoing climatic shifts","interactions":[],"lastModifiedDate":"2023-11-15T12:57:38.715568","indexId":"70250055","displayToPublicDate":"2023-11-07T06:55:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Empirical estimation of habitat suitability for rare plant restoration in an era of ongoing climatic shifts","docAbstract":"Accurate estimates of current and future habitat suitability are needed for species that may require assistance in tracking a shifting climate. Standard species distribution models (SDMs) based on occurrence data are the most common approach for evaluating climatic suitability, but these may suffer from inaccuracies stemming from disequilibrium dynamics and/or an inability to identify suitable climate regions that have no analogues within the current range. An alternative approach is to test performance with experimental introductions, and model suitability from the empirical results. We used this method with the Haleakalā silversword (Argyroxiphium sandwicense subsp. macrocephalum), using a network of out-plant plots across the top of Haleakalā volcano, Hawaiʻi. Over a ~ 5-year period, survival varied strongly across this network and was effectively explained by a simple model including mean rainfall and air temperature. We then applied this model to estimate current climatic suitability for restoration or translocation activities, to define trends in suitability over the past three decades, and to project future suitability through 2051. This empirical approach indicated that much of the current range has low suitability for long-term successful restoration, but also identified areas of high climatic suitability in a region where plants do not currently occur. These patterns contrast strongly with projections obtained with a standard SDM, which predicted continued suitability throughout the current range. Under continued climatic shifts, these results caution against the common SDM presumption of equilibrium between species’ distributions and their environment, even for long-established native species.
Informed population monitoring efforts are essential for sound management of harvested species, and adaptive strategies that provide detailed information to monitoring efforts often require data inputs from complimentary sources. Movement ecology information is seldom directly incorporated into population monitoring or adaptive harvest management strategies, yet can provide valuable information on species distributions, emigration and immigration rates, and aid in determining optimal population monitoring timing. The Rocky Mountain Population (RMP) of Sandhill Cranes is a harvested population subject to a stringent adaptive harvest management framework and an annual aerial survey to estimate population abundance, but movements of Sandhill Cranes during survey windows, and subsequent changes to harvest quotas based on their movement and distribution have not been investigated. We used seven years of GPS tracking data to estimate state-specific emigration and immigration rates, using a Bayesian multi-state capture-recapture model, among states within the RMP distribution to understand how seasonal crane movements may influence optimal aerial survey timing. We then leveraged these transition probabilities in conjunction with aerial survey count data to model how changes in aerial survey timing and movement-informed crane distribution would influence the current RMP Sandhill Crane adaptive harvest management model resulting in estimated changes to harvest allocation among states based on Sandhill Crane movement. We found that Sandhill Crane emigration from northern states began to increase the week of the aerial survey in late September, and continued to increase as autumn migration progressed into October. As expected, immigration to southern states began as emigration from northern states increased. Importantly, little movement among states occurred prior to the current aerial survey design timing. Overall, we found that current survey timing and shortly thereafter (∼1 week) did not greatly influence estimates of Sandhill Crane distribution, and did not greatly influence the harvest reallocation to each state until mid to late October (range of −42–+52 tag allocation change), much later than the current survey design would allow. Using GPS locations, we found that optimal population monitoring efforts could be improved to account for both detection and seasonal movements, while minimally influencing current adaptive harvest management strategies to stakeholders. Linking movement ecology with population monitoring efforts and subsequently adaptive harvest management strategies yields insightful information that can be beneficial for conservation planning, decision-making, and optimal species management of a migratory bird.
The Federal Highway Administration and State departments of transportation nationwide need an efficient method to assess potential adverse effects of highway stormwater runoff on receiving waters to optimize stormwater-treatment decisions. To this end, the U.S. Geological Survey, in cooperation with the Federal Highway Administration and the North Carolina Department of Transportation (NCDOT), developed a decision-support software tool based on a statewide version of the Stochastic Empirical Loading and Dilution Model (SELDM). This decision-support tool is designed to identify potential adverse effects of highway runoff by using a criterion based on a measurable change in water quality from a surrogate pollutant. The NCDOT worked with the North Carolina Department of Environmental Quality to select a 25-percent change in suspended sediment concentration as the decision-rule criterion for identifying measurable downstream water-quality change; this selection was based on available data and widely accepted stormwater monitoring uncertainties. Development of the statewide tool and its application to the Piedmont ecoregion are described in this report. Because SELDM can be applied to build a similar decision-support tool in any State, this report describes practice-ready methods that other State departments of transportation and municipal permittees can use to streamline environmental permitting and project delivery while protecting the environment.
Hydraulic design engineers can use this decision-support tool to establish stormwater-treatment goals for highway construction or improvement projects without having to learn SELDM or interpret its statistical output. The tool is a spreadsheet that determines if a selected highway segment can directly discharge highway runoff, if the highway segment can discharge runoff following treatment using a basic vegetated conveyance best management practice (BMP), or if treatment using an advanced BMP is needed to minimize effects of discharges on downstream water quality. To use the tool, hydraulic design engineers obtain upstream-basin characteristics from the U.S. Geological Survey StreamStats application and highway-site characteristics from preliminary design plans. They then enter these characteristics in the decision-support tool, which identifies the necessary stormwater-treatment goal.
The Piedmont ecoregion was used as a case study to demonstrate the type of information the decision-support tool can provide. In this ecoregion, 100 percent of direct discharges meet the water-quality criterion when the drainage-area ratio is less than about 0.007 acres of highway per square mile of upstream basin. Advanced BMPs are needed in 100 percent of basins with drainage-area ratios greater than about 50 acres per square mile. Between these drainage-area ratios, the selection of direct discharge, a basic vegetated conveyance BMP, or an advanced BMP is a function of highway-site and upstream-basin properties.
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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey (USGS) North Atlantic-Appalachian Region is developing the regionwide capacity to provide timely science support for decision-makers attempting to enhance carbon removal, sequestration, and emissions mitigation to meet national atmospheric carbon reduction goals. The U.S. Environmental Protection Agency (EPA) reported that in 2021, the fourteen States and the District of Columbia in the northeastern region account about for approximately 18 percent of the total national greenhouse gas (GHG) emissions. This Fact Sheet provides a summary of USGS science information and ongoing and new investigations or data-collection programs that may help the northeastern region decrease the release of carbon-containing GHG to the atmosphere.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233038","usgsCitation":"Warwick, P.D., Blondes, M.S., Brennan, S.T., Cahan, S.M., Karacan, C.Ö., Kroeger, K.D., and Merrill, M.D., 2023, Geologic carbon management options for the North Atlantic-Appalachian Region: U.S. Geological Survey Fact Sheet 2023–3038, 6 p., https://doi.org/10.3133/fs20233038.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149218","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":422295,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3038/fs20233038.XML"},{"id":422291,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3038/coverthb.jpg"},{"id":422294,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3038/images/"},{"id":422293,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233038/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3038"},{"id":422292,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3038/fs20233038.pdf","text":"Report","size":"4.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3038"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.26577580636953,\n 36.4871201165721\n ],\n [\n -75.46948418608685,\n 38.28668271756587\n ],\n [\n -73.51070016109207,\n 41.05603971547217\n ],\n [\n -71.95706878291493,\n 41.50333448444454\n ],\n [\n -71.00596870454174,\n 42.379951218421894\n ],\n [\n -70.18176569603114,\n 43.65118954946408\n ],\n [\n -67.0599117586491,\n 44.903001098203504\n ],\n [\n -68.03473227073835,\n 47.25857286501821\n ],\n [\n -69.06116066206772,\n 47.51130156334898\n ],\n [\n -71.18990395428646,\n 45.21595765124482\n ],\n [\n -74.64906462208842,\n 44.958321658703426\n ],\n [\n -77.16670105999457,\n 41.68183359906362\n ],\n [\n -82.15942155266379,\n 36.60328960559353\n ],\n [\n -77.26577580636953,\n 36.4871201165721\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Geology, Energy & Minerals Science Center
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Iceland's exposure to major ocean current pathways of the central North Atlantic makes it a useful location for developing long-term proxy records of past marine climate. Such records provide more detailed understanding of the full range of past variability which is necessary to improve predictions of future changes. We constructed a 225-year (1791–2015 CE) master shell growth chronology from 29 shells of Arctica islandica collected at 100 m water depth in southwest Iceland (Faxaflói). The growth chronology provides a robust age model for shell oxygen isotope (δ18Oshell) data produced at annual resolution for 251 years (1765–2015 CE). The temperature reconstruction derived from δ18Oshell shows coherence with May–October local surface temperature records and sea surface temperatures in the North Atlantic region, suggesting it is a useful proxy indicator of water temperature variability at 100 m depth within Faxaflói. Field correlations between the shell-based records and gridded sea surface temperature data reveal strong positive correlations between the 1-year lagged shell growth and temperatures within the subpolar gyre post-1972, suggesting a delayed influence of subpolar gyre dynamics on ecological indicators in southwest Iceland in recent decades. However, the shell growth chronology and δ18Oshell record generally show relatively weak and insignificant correlations with larger region climate indices including the Atlantic Multidecadal Variability, North Atlantic Oscillation, and East Atlantic pattern. Therefore the interannual variations in the newly produced shell-based records appear to reflect more local to regional dynamics around southwest Iceland than large-scale modes of climate variability.
Inland flooding, sea-level rise, and pollution pose challenges for Maine’s infrastructure and natural resources. A highly detailed, three-dimensional (3D) model of the Earth’s surface is allowing the State of Maine to address these challenges in an increasingly comprehensive and timely manner. In addition, highly accurate elevation data facilitate land development, forest management, agricultural practices, and wildlife conservation, all of which are key pillars of Maine’s economy. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed 3D model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Native Bull Trout Salvelinus confluentus populations can be influenced by a variety of stressors operating at multiple spatial scales, making the relative importance of biotic versus abiotic controls difficult to discern at small scales where monitoring and management typically occur. Nonnative Brook Trout S. fontinalis were widely introduced throughout western North America and negatively affect Bull Trout occurrence. Here, we examine reach-scale associations between nonnative Brook Trout and juvenile and stream-resident Bull Trout (i.e., <250 mm) abundances through the lens of a constraining threshold, where nonnative fish exceeding a certain fish density may constrain native fish abundance.
We used a large spatial data set to define the abiotic conditions in which stream-dwelling Brook Trout and Bull Trout smaller than 250 mm typically co-occur in Idaho. Next, we queried multipass electrofishing survey data collected in reaches with abiotic conditions suitable for both species within localized areas where their distributions overlap. We then used two-dimensional Kolmogorov–Smirnov tests to identify threshold Brook Trout densities beyond which Bull Trout less than 250 mm were consistently rare or absent.
Bull Trout smaller than 250 mm were rare or absent where Brook Trout density exceeded 0.54 fish/100 m2 across the full range of abiotic conditions over which both species overlapped. However, Brook Trout rarely occurred in habitats associated with high Bull Trout density (e.g., where mean August water temperatures were 8.2°C).
Our results support existing hypotheses that the long-term co-occurrence of Bull Trout and Brook Trout in stream reaches suitable for both species may be unstable. Because low densities of Brook Trout appear to threaten Bull Trout, additional research is needed to better understand factors driving ongoing range shifts and invasion dynamics in Bull Trout habitat. We provide a simple tool to inform where Brook Trout represent a primary threat to Bull Trout, with potential applications for future monitoring, threat assessments, and conservation efforts.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10443","usgsCitation":"Voss, N.S., Bowersox, B.J., and Quist, M.C., 2023, Reach-scale associations between introduced Brook Trout and juvenile and stream-resident Bull Trout in Idaho: Transactions of American Fisheries Society, v. 152, no. 6, p. 835-848, https://doi.org/10.1002/tafs.10443.","productDescription":"14 p.","startPage":"835","endPage":"848","ipdsId":"IP-151044","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10443","text":"Publisher Index Page"},{"id":430209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.03090213547928,\n 43.207652040414814\n ],\n [\n -111.03202746708995,\n 43.2200826637544\n ],\n [\n -111.0540054745273,\n 44.480723993881384\n ],\n [\n -111.38063590540663,\n 44.7211838491711\n ],\n [\n -112.32600707694,\n 44.55386220030201\n ],\n [\n -112.43093922496412,\n 44.442261666637535\n ],\n [\n -112.72674441255437,\n 44.47967351071486\n ],\n [\n -112.91781898200888,\n 44.39232271637843\n ],\n [\n -113.16440598999255,\n 44.75171823057957\n ],\n [\n -113.95671663335192,\n 45.68966966579467\n ],\n [\n -114.36026790153626,\n 45.48555124417567\n ],\n [\n -114.50723744474614,\n 45.62020392465001\n ],\n [\n -114.35705754509375,\n 45.933639277473816\n ],\n [\n -114.48977698881906,\n 46.13114309755966\n ],\n [\n -114.3482896820553,\n 46.72004445964737\n ],\n [\n -116.11637073698459,\n 48.05164962026552\n ],\n [\n -116.08451122563832,\n 49.02927481424686\n ],\n [\n -117.08709255417494,\n 48.99980579823804\n ],\n [\n -117.00949159691567,\n 46.35873140197231\n ],\n [\n -116.97263402539582,\n 46.10668853292651\n ],\n [\n -116.7397249746474,\n 45.80229784972218\n ],\n [\n -116.49957941400402,\n 45.606539467598054\n ],\n [\n -117.19912136100515,\n 44.563237965950606\n ],\n [\n -117.18632347100339,\n 44.35506960028275\n ],\n [\n -116.92238694239666,\n 44.13046718217083\n ],\n [\n -117.05970457356327,\n 43.781652784475654\n ],\n [\n -117.03090213547928,\n 43.207652040414814\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Voss, Nicholas S.","contributorId":300117,"corporation":false,"usgs":false,"family":"Voss","given":"Nicholas","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":903853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowersox, Brett J.","contributorId":265299,"corporation":false,"usgs":false,"family":"Bowersox","given":"Brett","email":"","middleInitial":"J.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903855,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249893,"text":"70249893 - 2023 - Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc","interactions":[],"lastModifiedDate":"2023-11-04T13:41:33.169601","indexId":"70249893","displayToPublicDate":"2023-11-03T08:38:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc","docAbstract":"Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra-producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska-Aleutian arc, specifically its pre-Holocene spatiotemporal distribution.