Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.
Hydrological and bioclimatic processes that lead to drought may stress plants and wildlife, restructure plant community type and architecture, increase monotypic stands and bare soils, facilitate the invasion of non‐native plant species and accelerate soil erosion. Our study focuses on the impact of a paucity of Colorado River surface flows from the United States (U.S.) to Mexico. We measured change in riparian plant greenness and water use over the past two decades using remotely sensed measurements of vegetation index (VI), evapotranspiration (ET), and a new annualized Phenology Assessment Metric (PAM) for ET. We measure these long‐term (2000‐2019) metrics and their short‐term (2014‐2019) response to an environmental, pulse flow in 2014, as prescribed under Minute 319 of the 1944 Water Treaty between the two nations. In subsequent years, small directed flows were provided to restoration areas under Minute 323. We use 250 m MODIS and 30 m Landsat imagery to evaluate three vegetation indices (NDVI, EVI, EVI2). We select EVI2 to parameterize an optical‐based ET algorithm and test the relationship between ET from Landsat and MODIS by regression approaches. Our analyses show significant decreases in VIs and ET for both the 20‐year and post‐pulse 5‐year periods. Over the last 20 years, EVI Landsat declined 34% (30% by EVIMODIS) and ETLandsat‐EVI declined 38% (27% by ETMODIS‐EVI), overall ca. 1.61 mmd‐1 or 476 mmyr‐1 drop in ET. Over the 5 years since the 2014 pulse flow, EVI Landsat declined 20% (13% by EVIMODIS) and ETLandsat‐EVI declined 23% (4% by ETMODIS‐EVI) with a 0.77 mmd‐1 or a 209 mmyr‐1 5‐year drop in ET. Data and change maps show the pulse flow contributed enough water to slow the rate of loss, but only for the very short‐term (1‐2 years). These findings are critically important as they suggest further deterioration of biodiversity, wildlife habitat and key ecosystem services due to anthropogenic diversions of water in the U.S. and Mexico and from land clearing, fires, and plant‐related drought which affect hydrological processes.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13911","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Chavoshi Borujeni, S., Jarchow, C., Gómez‐Sapiens, M., Nouri, H., Herrmann, S.M., and Didan, K., 2020, Ecohydrological responses to surface flow across borders: Two decades of changes in vegetation greenness and water use in the riparian corridor of the Colorado River Delta: Hydrological Processes, v. 34, no. 25, p. 4851-4883, https://doi.org/10.1002/hyp.13911.","productDescription":"33 p.; Data Release","startPage":"4851","endPage":"4883","ipdsId":"IP-117414","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":378804,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":436784,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PGDJ1","text":"USGS data release","linkHelpText":"Colorado River Delta Project: A compilation of vegetation indices, phenology assessment metrics, estimates of evapotranspiration and change maps for seven reaches of the delta's 150 km region, for nearly the last two decades"}],"country":"Mexico, United States","otherGeospatial":"Colorado River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.15869140624999,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 31.606609719226917\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":799708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":799709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chavoshi Borujeni, Sattar","contributorId":241612,"corporation":false,"usgs":false,"family":"Chavoshi Borujeni","given":"Sattar","email":"","affiliations":[{"id":48363,"text":"Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Centre, AREEO, Isfahan, Iran","active":true,"usgs":false}],"preferred":false,"id":799710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":799711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gómez‐Sapiens, Marth M.","contributorId":241615,"corporation":false,"usgs":false,"family":"Gómez‐Sapiens","given":"Marth M.","affiliations":[],"preferred":false,"id":799732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":799733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":799735,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218272,"text":"70218272 - 2020 - Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","interactions":[],"lastModifiedDate":"2021-02-23T13:33:50.365096","indexId":"70218272","displayToPublicDate":"2020-09-19T07:30:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","docAbstract":"Water‐resources managers are challenged with maintaining a balance among beneficial uses throughout river networks and need robust means of assessing potential risks to aquatic life resulting from flow alterations. This study generated ecological limit functions from species‐streamflow relations to quantify potential fish richness response to flow alteration and compared results to currently accepted streamflow management guidelines. Modeled responses of absolute richness change were watershed specific and varied among sample sets derived from hydrologic unit classifications of different sizes (large HUC 6 basins to regional scale HUC 8). With a 20% flow reduction, 10% of HUC 8 predicted a richness decrease in one or more taxa. While absolute richness change was consistent across streams within a HUC, percent richness change was stream size dependent. Comparisons with Instream Flow Incremental Methodology habitat models predicted habitat loss greater than percent richness change; however, predictions for habitat and richness decreased similarly as stream size decreased. Watershed‐specific responses from flow reductions could allow water‐resources management decisions to be made locally based on the predicted richness change for certain sized streams. Quantitative results highlight the utility of a richness‐based framework for generating watershed‐specific risk assessments that validate and inform currently employed water‐resources management practices.
Novel forms of drought are emerging globally, due to climate change, shifting teleconnection patterns, expanding human water use, and a history of human influence on the environment that increases the probability of transformational ecological impacts. These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today’s grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21st century.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oneear.2020.08.019","usgsCitation":"Crausbay, S.D., Betancourt, J.L., Bradford, J., Cartwright, J.M., Dennison, W., Dunham, J.B., Enquist, C.A., Frazier, A.G., Hall, K.R., Littell, J., Luce, C.H., Palmer, R., Ramirez, A.R., Rangwala, I., Thompson, L., Walsh, B.M., and Carter, S., 2020, Unfamiliar territory: Emerging themes for ecological drought research and management: One Earth, v. 3, no. 3, p. 337-353, https://doi.org/10.1016/j.oneear.2020.08.019.","productDescription":"17 p.","startPage":"337","endPage":"353","ipdsId":"IP-110891","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern 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A unifying feature among these national parks, monuments, and historic sites is mixed-grass prairie, which not only provides background scenery but is the very foundation of many park missions. As recognition of the prairie’s importance to park fundamental resources and values has grown, so too has the realization that invasive plants threaten these values by reducing native species diversity, altering food webs, and marring the visitor experience. Parks manage invasive species despite uncertainties in treatment effectiveness because management cannot wait for research to provide definitive answers. Under these circumstances, adaptive management (AM) is an appropriate approach. In the NGP, we formed a collaborative adaptive vegetation management team to apply AM towards reducing invasive species (with a focus on exotic annual grasses) and improving native vegetation conditions. 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","language":"English","publisher":"University of California at Berkeley","doi":"10.5070/P536349865","usgsCitation":"Ashton, I.W., Symstad, A., Baldwin, H., Post van der Burg, M., Bekedam, S., Borgman, E., Haar, M., Hogan, T., Rockwood, S., Swanson, D., Thomson, C., and Wienk, C., 2020, A new decision support tool for collaborative adaptive vegetation management in northern Great Plains national parks: Parks Stewardship Forum, v. 3, no. 36, 11 p., https://doi.org/10.5070/P536349865.","productDescription":"11 p.","ipdsId":"IP-115660","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5070/p536349865","text":"Publisher Index 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The Collections process ensures consistent data quality through time and across all the Landsat sensors with a few modifications to the metadata. The consistent data products from Collections are more conducive for applications such as time-series analysis, because the data can be used without a need for sensor-specific geometric or radiometric adjustments. The Collections process also allows for calibration improvements from updated reference sources, model trending, and enhanced algorithms that are grouped together and applied at one time, thus limiting the operational impacts to users. The first collection, Collection-1 was released in 2016, and Collection-2 is expected to be released in 2020. This paper addresses the geometric improvements to the Collection -2 dataset. Geometric improvements in Collection-2 include improvements to the geometric accuracy and interoperability of all Landsat products by implementing the Sentinel 2 Global Reference Image (GRI), improved elevation dataset using NASADEM and other sources of Digital Elevation Model (DEM) for terrain correction, improvements to the precision correction process for Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) sensors, changes to the Thermal Infrared Sensor (TIRS) to Operational Land Imager (OLI) alignment estimates, thermal band detector alignments and focal plane adjustments for ETM+, and improvements to the ETM+ sensor to attitude control system (ACS).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: Earth observing systems XXV","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Earth Observing Systems XXV","conferenceDate":"Aug 24-Sep 4, 2020","language":"English","publisher":"SPIE","doi":"10.1117/12.2570429","usgsCitation":"Rengarajan, R., Choate, M.J., Storey, J.C., Franks, S., and Micijevic, E., 2020, Landsat Collection 2 geometric calibration updates, in Proceedings: Earth observing systems XXV, v. 11501, Aug 24-Sep 4, 2020, 115010N, 11 p., https://doi.org/10.1117/12.2570429.","productDescription":"115010N, 11 p.","ipdsId":"IP-122667","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":378968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11501","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":800336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":800337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":800436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":93362,"corporation":false,"usgs":true,"family":"Franks","given":"Shannon","affiliations":[],"preferred":false,"id":800437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":800438,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215257,"text":"70215257 - 2020 - Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS","interactions":[],"lastModifiedDate":"2020-10-15T13:14:16.860035","indexId":"70215257","displayToPublicDate":"2020-09-17T07:15:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2958,"text":"Organic Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS","docAbstract":"The Norwegian Geochemical Standard North Sea Oil-1 was analyzed by gas chromatography triple quadrupole mass spectrometry (GC-QQQ-MS) on two instruments using independently developed analytical methods. Biomarker ratios determined by GC-QQQ-MS were compared to each other and to previously reported values determined by gas chromatography single quadrupole mass spectrometry (GC-Q-MS) or flame ionization detection (GC-FID). Hopane, sterane, and tricyclic ratio values determined by GC-QQQ-MS in multiple reaction monitoring (MRM) mode are comparable to each other, but their comparability to reported values measured by GC-Q-MS in selected ion monitoring (SIM) mode depends in part on whether the compounds in the ratio have similar or dissimilar mass spectral responses. For example, sterane and hopane stereoisomer thermal maturity ratios measured by GC-QQQ-MS in MRM mode agree with previously reported GC-Q-MS SIM values, but an offset is observed for ratios of rearranged hopanes or steranes to their non-rearranged counterparts. Triaromatic steroid, monoaromatic steroid, phenanthrene, and methylphenanthrene ratios measured by GC-QQQ-MS are comparable to each other and to reported GC-Q-MS SIM values. The carbon preference index is comparable across GC-QQQ-MS measurements and reported GC-FID values, while comparability is more variable for other ratios based on pristane, phytane, and/or n-alkanes. Comparability of variably acquired biomarker data could be enhanced in the future by developing instrument- and ratio-specific correction factors or by modifying GC-QQQ-MS parameters and MRM transitions to more closely reproduce previously reported values.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2020.104124","usgsCitation":"French, K.L., Leider, A., and Hallmann, C., 2020, Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS: Organic Geochemistry, v. 150, 104124, 4 p., https://doi.org/10.1016/j.orggeochem.2020.104124.","productDescription":"104124, 4 p.","ipdsId":"IP-119234","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455295,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2020.104124","text":"Publisher Index Page"},{"id":436788,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99ZMFJ5","text":"USGS data release","linkHelpText":"Data Release for "Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS""},{"id":379343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":801281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leider, Arne","contributorId":242996,"corporation":false,"usgs":false,"family":"Leider","given":"Arne","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hallmann, Christian","contributorId":242997,"corporation":false,"usgs":false,"family":"Hallmann","given":"Christian","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801283,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213232,"text":"70213232 - 2020 - How and why is the timing and occurrence of seasonal migrants in the Gulf of Maine changing due to climate?","interactions":[],"lastModifiedDate":"2020-12-14T17:38:21.907848","indexId":"70213232","displayToPublicDate":"2020-09-16T11:33:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":7468,"text":"Final Report","active":true,"publicationSubtype":{"id":9}},"title":"How and why is the timing and occurrence of seasonal migrants in the Gulf of Maine changing due to climate?","docAbstract":"Plants and animals undergo certain recurring life-cycle events, such as migrations between summer and winter habitats or the annual blooming of plants. Known as phenology, the timing of these events is very sensitive to changes in climate (and changes in one species’ phenology can impact entire food webs and ecosystems). Shifts in phenology have been described as a “fingerprint” of the temporal and spatial responses of wildlife to climate change impacts. Thus, phenology provides one of the strongest indicators of the adaptive capacity of organisms (or the ability of organisms to cope with future environmental conditions).
In this study, researchers are exploring how the timing and occurrence of a number of highly migratory marine animals is changing due to a series of climatic and ecological shifts. First, using existing long-term historical data series, they will determine the direction and magnitude of how migration, abundance, or other phenological factors have changed for marine mammals, sea turtles, and fishes that migrate into the Gulf of Maine on a seasonal basis. Because marine animals are inherently difficult to detect, the team will apply dynamic occupancy models to evaluate seasonal migration patterns and habitat use across multiple habitats in the Gulf of Maine region. The project team will also synthesize regional information on a key, ecologically-important prey fish, sandlance, whose timing and abundance is a strong predictor of the occurrence and behavior of predator species targeted in this study as well as a range of other regional fish and wildlife of conservation and management concern. Results from this component of the project will identify coastal fish and wildlife species that are relatively more or less able to adapt and thus potentially vulnerable to climate change; determine the likely primary drivers of those changes; and identify data gaps and future monitoring needs. Ultimately, this information will be available and useful for regional coastal management and adaptation decisions that will allow managers to effectively plan for the future.
In a second component of the project, researchers will focus specifically on changes in migration patterns of the endangered North Atlantic right whale. While shifts in the distribution and time of recurring life events are adaptive responses that may help species cope with climate impacts, they can also lead to changes in how species interact with humans. The North Atlantic right whale is one of the most endangered whale species on the planet. In the North Atlantic Ocean, ship strikes and entanglements with commercial fishing gear represent fatal threats to right whales. Recent reports suggest that North Atlantic right whale migration patterns have changed. Many researchers posit that shifts in migration are responsible for recent increases in the overlap between right whales and human activities, especially fishing. To help understand how changes in right whale movements and behaviors may overlap with ship traffic, and thus put the animals at risk of encountering vessels, we will combine right whale habitat models with ship traffic maps. The end result will be a set of maps identifying risk levels.
Mercury (Hg) is a naturally occurring element that bonds with organic matter and, when converted to methylmercury, is a potent neurotoxicant. Here we estimate potential future releases of Hg from thawing permafrost for low and high greenhouse gas emissions scenarios using a mechanistic model. By 2200, the high emissions scenario shows annual permafrost Hg emissions to the atmosphere comparable to current global anthropogenic emissions. By 2100, simulated Hg concentrations in the Yukon River increase by 14% for the low emissions scenario, but double for the high emissions scenario. Fish Hg concentrations do not exceed United States Environmental Protection Agency guidelines for the low emissions scenario by 2300, but for the high emissions scenario, fish in the Yukon River exceed EPA guidelines by 2050. Our results indicate minimal impacts to Hg concentrations in water and fish for the low emissions scenario and high impacts for the high emissions scenario.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-18398-5","usgsCitation":"Schaefer, K., Elshorbany, Y., Jafarov, E., Schuster, P.F., Striegl, R.G., Wickland, K.P., and Sunderland, E.M., 2020, Potential impacts of mercury released from thawing permafrost: Nature-Communications, v. 11, 4650, 6 p., https://doi.org/10.1038/s41467-020-18398-5.","productDescription":"4650, 6 p.","ipdsId":"IP-115389","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-18398-5","text":"Publisher Index Page"},{"id":378509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.564453125,\n 60.19615576604439\n ],\n [\n -158.466796875,\n 61.52269494598361\n ],\n [\n -153.544921875,\n 63.89873081524394\n ],\n [\n -142.294921875,\n 61.56457388515458\n ],\n [\n -139.658203125,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 60.1524422143808\n ],\n [\n -136.7578125,\n 61.52269494598361\n ],\n [\n -136.0986328125,\n 64.47279382008166\n ],\n [\n -145.3271484375,\n 67.90861918215302\n ],\n [\n -147.744140625,\n 67.97463396204759\n ],\n [\n -153.72070312499997,\n 67.45808150845772\n ],\n [\n -156.88476562499997,\n 66.7745857647255\n ],\n [\n -160.400390625,\n 64.92354174306496\n ],\n [\n -160.83984375,\n 63.64625919492172\n ],\n [\n -163.16894531249997,\n 62.85514553774182\n ],\n [\n -163.916015625,\n 63.37183226679281\n ],\n [\n -165.6298828125,\n 62.28836509824845\n ],\n [\n -163.564453125,\n 60.19615576604439\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Kevin 0000-0002-5444-9917","orcid":"https://orcid.org/0000-0002-5444-9917","contributorId":202096,"corporation":false,"usgs":false,"family":"Schaefer","given":"Kevin","email":"","affiliations":[{"id":36340,"text":"National Snow and National Snow and Ice Data Center, Cooperative Institute for Research, Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":799032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elshorbany, Yasin","contributorId":240870,"corporation":false,"usgs":false,"family":"Elshorbany","given":"Yasin","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":799033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jafarov, Elchin","contributorId":195182,"corporation":false,"usgs":false,"family":"Jafarov","given":"Elchin","affiliations":[],"preferred":false,"id":799034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schuster, Paul F. 0000-0002-8314-1372 pschuste@usgs.gov","orcid":"https://orcid.org/0000-0002-8314-1372","contributorId":1360,"corporation":false,"usgs":true,"family":"Schuster","given":"Paul","email":"pschuste@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":799036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":799037,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":65376,"corporation":false,"usgs":true,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799090,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215541,"text":"70215541 - 2020 - Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","interactions":[],"lastModifiedDate":"2020-10-22T14:41:08.677038","indexId":"70215541","displayToPublicDate":"2020-09-16T09:36:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","docAbstract":"Intramolecular isotope distributions, including isotope clumping and position specific fractionation, can provide proxies for the formation temperature and formation and destruction pathways of molecules. In this study, we explore the position-specific hydrogen isotope distribution in propane. We analyzed propane samples from 10 different petroleum systems with high-resolution molecular mass spectrometry. Our results show that the hydrogen isotope fractionation between central and terminal positions of natural propanes ranges from −102‰ to +205‰, a much larger range than that expected for thermodynamic equilibrium at their source and reservoir temperatures (36–63‰). Based on these findings, we propose that the hydrogen isotope structure of catagenic propane is largely controlled by irreversible processes, expressing kinetic isotope effects (KIEs). Kinetic control on hydrogen isotope composition of the products of thermal cracking is supported by a hydrous pyrolysis experiment using the Woodford Shale as substrate, in which we observed isotopic disequilibrium in the early stage of pyrolysis. We make a more general prediction of KIE signatures associated with kerogen cracking by simulating this chemistry in a kinetic Monte Carlo model for different types of kerogens. In contrast, unconventional shale fluids or hot conventional reservoirs contain propane with an isotopic structure close to equilibrium, presumably reflecting internal and/or heterogeneous exchange during high temperature storage (ca. 100–150 °C). In relatively cold (<100 °C) conventional gas accumulations, propane can discharge from its source to a colder reservoir, rapidly enough to preserve disequilibrium signatures even if the source rock thermal maturity is high. These findings imply that long times at elevated temperatures are required to equilibrate the hydrogen isotopic structure of propane in natural gas host rocks and reservoirs. We further defined the kinetics of propane equilibration through hydrogen isotope exchange experiments under hydrous conditions; these experiments show that hydrogen in propane is exchangeable over laboratory timescales when exposed to clay minerals such as kaolinite. This implies rather rapid transfer of propane from sources to cold reservoirs in some of the conventional petroleum systems. Propane is also susceptible to microbial degradation in both oxic and anoxic environments. Biodegradation of propane in the Hadrian and Diana Hoover oil fields (Gulf of Mexico) results in strong increases in central-terminal hydrogen isotope fractionation. This reflects preferential attack on the central position, consistent with previous studies.
The ecosystems along the border between the United States and Mexico are at increasing risk to wildfire due to interactions among climate, land-use, and fuel loads. A wide range of fuel treatments have been implemented to mitigate wildfire and its threats to valued resources, yet we have little information about treatment effectiveness. To fill critical knowledge gaps, we reviewed wildfire risk and fuel treatment studies that were conducted near the US-Mexico border and published in the peer-reviewed literature between 1986 and 2019. The number of studies has grown during this time in warm desert to forest ecosystems on primarily federal lands. The most common study topics included fire effects on native species, the role of invasive species and woody encroachment on wildfire risk, historical fire regimes, and remote sensing and modeling to study wildfire risk across the landscape. A majority of fuel treatment studies focused on prescribed burns, and fuel treatments collectively had mixed effects on mitigating future wildfire risk and threats to ecosystems depending on vegetation and fire characteristics. The diversity of ecosystems and land ownership along the US-Mexico border present unique challenges for understanding and managing wildfire risk, and also create opportunities for collaboration and cross-site studies to promote knowledge across broad environmental gradients.
The coastal Pacific Northwest USA hosts thousands of deep-seated landslides. Historic landslides have primarily been triggered by rainfall, but the region is also prone to large earthquakes on the 1100-km-long Cascadia Subduction Zone megathrust. Little is known about the number of landslides triggered by these earthquakes because the last magnitude 9 rupture occurred in 1700 CE. Here, we map 9938 deep-seated bedrock landslides in the Oregon Coast Range and use surface roughness dating to estimate that past earthquakes triggered fewer than half of the landslides in the past 1000 years. We find landslide frequency increases with mean annual precipitation but not with modeled peak ground acceleration or proximity to the megathrust. Our results agree with findings about other recent subduction zone earthquakes where relatively few deep-seated landslides were mapped and suggest that despite proximity to the megathrust, most deep-seated landslides in the Oregon Coast Range were triggered by rainfall.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.aba6790","usgsCitation":"LaHusen, S., Duvall, A.R., Booth, A.M., Grant, A.R., Mishkin, B.A., Montgomery, D., Struble, W., Roering, J., and Wartman, J., 2020, Rainfall triggers more deep-seated landslides than Cascadia earthquakes in the Oregon Coast Range, USA: Science Advances, v. 6, no. 38, eaba6790, 11 p., https://doi.org/10.1126/sciadv.aba6790.","productDescription":"eaba6790, 11 p.","ipdsId":"IP-114882","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aba6790","text":"Publisher Index Page"},{"id":378896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 42.05745022024682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean R 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":241904,"corporation":false,"usgs":false,"family":"LaHusen","given":"Sean R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duvall, Alison R 0000-0002-7760-7236","orcid":"https://orcid.org/0000-0002-7760-7236","contributorId":241905,"corporation":false,"usgs":false,"family":"Duvall","given":"Alison","email":"","middleInitial":"R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Adam M. 0000-0002-7339-0594","orcid":"https://orcid.org/0000-0002-7339-0594","contributorId":241907,"corporation":false,"usgs":false,"family":"Booth","given":"Adam","email":"","middleInitial":"M.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":800162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":800163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishkin, Benjamin A","contributorId":241909,"corporation":false,"usgs":false,"family":"Mishkin","given":"Benjamin","email":"","middleInitial":"A","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montgomery, David R.","contributorId":241911,"corporation":false,"usgs":false,"family":"Montgomery","given":"David R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":800166,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":800167,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wartman, Joseph 0000-0001-7659-7198","orcid":"https://orcid.org/0000-0001-7659-7198","contributorId":241918,"corporation":false,"usgs":false,"family":"Wartman","given":"Joseph","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800168,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216424,"text":"70216424 - 2020 - Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","interactions":[],"lastModifiedDate":"2020-11-17T13:53:10.508922","indexId":"70216424","displayToPublicDate":"2020-09-16T07:46:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","docAbstract":"Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.
Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to account for the effects of streamflow variability on travel time and compare our estimates to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15 min interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60–90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because of heterogeneous surficial geology, hydro-climatology, and vegetation and for applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.
Tsunami risk management requires strategies that can address multiple sources with different recurrence intervals, wave-arrival times, and inundation extents. Probabilistic tsunami hazard analysis (PTHA) provides a structured way to integrate multiple sources, including the uncertainties due to the natural variability and limited knowledge of sources. PTHA-based products relate to specific average return periods (ARP) and while there has been considerable attention paid to ARP choice for building codes, guidance on ARP choice to support evacuation planning and related land use is lacking. We use the State of California (USA) coastal communities as a case study to explore the use of geospatial analysis and pedestrian-evacuation modeling for comparing the societal implications of tsunamis based on evacuation areas that reflect inundation from 475-year, 975-year, and 2475-year ARPs. Results demonstrate that changes in PTHA ARP had a substantial effect on the number of tax-lot parcels in PTHA evacuation areas, but not on the primary land use of these parcels or which communities had the largest number of exposed parcels. Composite PTHA maps provided high-level insights on hazard exposure and identified dominant sources; however, disaggregated PTHA outputs that reflect single source parameters (e.g., wave-arrival time) were necessary to quantify evacuation potential from local and distant tsunamis. Framing changes in ARP assumption based on changes in the number, land-use type, and potential evacuation challenges of parcels in evacuation areas can provide valuable insight on the real-world implications of which ARP to use in land use or evacuation planning.
Ponds are common features on salt marshes, yet it is unclear how they affect large-scale marsh evolution. We developed a spatially explicit model that combines cellular automata for pond formation, expansion, and drainage, and partial differential equations for elevation dynamics. We use the mesotidal Barnstable marsh (MA, USA) as a case study, for which we measured pond expansion rate by remote sensing analysis over a 41-year time span. We estimated pond formation rate by comparing observed and modeled pond size distribution, and predicted pond deepening by comparing modeled and measured pond depth. The Barnstable marsh is currently in the pond recovery regime, i.e.,every pond revegetates and recovers the necessary elevation to support plant growth after re-connecting to the channel network. This pond dynamic creates an equivalent (i.e.,spatially and temporally averaged over the whole marsh) 0.5–2 mm/yr elevation loss that needs to be supplemented by excess vertical accretion. We explore how the pond regime would change with decreased sediment supply and increased relative sea-level rise (RSLR) rate, focusing on the case in which the vegetated marsh keeps pace with RSLR. When the RSLR rate remains below the minimum unvegetated deposition rate, the pond dynamics is nearly unaltered and ponds always occupy ~10% of the marsh area. However, when RSLR rate exceeds this threshold, the ponds in the marsh interior – which receive the least amount of suspended sediment – do not recover after drainage. These ponds transition to mudflats and permanently occupy up to 30% of the marsh area depending on RSLR rate. For marshes with a small tidal range, such as the microtidal Sage Lot Pond marsh on the opposite side of the peninsula from Barnstable marsh, high RSLR rates could bring every portion of the marsh into the pond runaway regime, with the whole marsh eventually converting into mudflats. In this regime, the existing marsh would disappear within centuries to millennia depending on the RSLR rate. Because of the spatial and temporal components of marsh evolution, a single RSLR threshold value applied across the entire marsh landscape provides a limited description of the marsh vulnerability to RSLR.
Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historical land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001–2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~222.7 Mm3 by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm3 by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping to better inform land and resource management decisions.
","language":"English","publisher":"MDPI","doi":"10.3390/land9090322","usgsCitation":"Wilson, T., Van Schmidt, N.D., and Langridge, R., 2020, Land-use change and future water demand in California’s central coast: Land, v. 9, no. 322, p. 322-343, https://doi.org/10.3390/land9090322.","productDescription":"21 p.","startPage":"322","endPage":"343","ipdsId":"IP-112033","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land9090322","text":"Publisher Index Page"},{"id":378385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Cruz, San Benito, Monterey, San Luis Obispo, & Santa Barbara","otherGeospatial":"central California coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.838623046875,\n 35.585851593232356\n ],\n [\n -121.06109619140625,\n 35.505400093441324\n ],\n [\n -120.89904785156251,\n 35.22094130403182\n ],\n [\n -120.66558837890626,\n 34.89043681762452\n ],\n [\n -120.63812255859375,\n 34.76417891445512\n ],\n [\n -120.58319091796874,\n 34.646766246519114\n ],\n [\n -120.22613525390624,\n 34.80252766591687\n ],\n [\n -120.0640869140625,\n 34.872411827691025\n ],\n [\n -120.838623046875,\n 35.585851593232356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"322","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":798599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":240648,"corporation":false,"usgs":false,"family":"Van Schmidt","given":"Nathan","middleInitial":"D.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langridge, Ruth 0000-0001-5320-8882","orcid":"https://orcid.org/0000-0001-5320-8882","contributorId":240649,"corporation":false,"usgs":false,"family":"Langridge","given":"Ruth","email":"","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212635,"text":"70212635 - 2020 - Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","interactions":[],"lastModifiedDate":"2023-11-08T16:13:16.263836","indexId":"70212635","displayToPublicDate":"2020-09-13T13:38:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","docAbstract":"High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was calculated from a correlation with SC. Explanatory variables for BRT models included well location and construction, surficial variables (e.g., soils and land use), and variables extracted from a groundwater‐flow model, including simulated groundwater ages. BRT model fits (r2) were 0.74 (SC and Cl) and 0.62 (TDS). BRT models provided spatially continuous salinity predictions across surficial and deeper aquifers where discrete water‐quality samples were missing. Uncertainty was smaller where salinity was lower, and models tended to underpredict in areas of highest salinity. Despite this, BRT models were able to capture areas of documented high salinity that exceed the TDS secondary maximum contaminant level for drinking water of 500 mg/L. Variables that served as surrogates for position along groundwater flowpaths were the most important predictors, indicating that much of the control on dissolved solids is related to rock‐water interaction as residence time increases. BRT models additionally support hypotheses of both surficial and deep sources of salinity.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12879","usgsCitation":"Knierim, K.J., Kingsbury, J.A., Haugh, C., and Ransom, K.M., 2020, Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States: Journal of American Water Resources Association, v. 56, no. 6, https://doi.org/10.1111/1752-1688.12879.","productDescription":"20 p.","startPage":"1029","ipdsId":"IP-111775","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":455333,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12879","text":"Publisher Index Page"},{"id":436791,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBFR1T","text":"USGS data release","linkHelpText":"Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment"},{"id":382516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.3408203125,\n 36.98500309285596\n ],\n [\n -90.52734374999999,\n 36.73888412439431\n ],\n [\n -92.3291015625,\n 34.66935854524543\n ],\n [\n -93.779296875,\n 32.32427558887655\n ],\n [\n -92.548828125,\n 31.240985378021307\n ],\n [\n -90.52734374999999,\n 32.509761735919426\n ],\n [\n -88.857421875,\n 32.10118973232094\n ],\n [\n -87.2314453125,\n 30.789036751261136\n ],\n [\n -86.923828125,\n 31.690781806136822\n ],\n [\n -87.275390625,\n 32.879587173066305\n ],\n [\n -88.9453125,\n 33.87041555094183\n ],\n [\n -89.2529296875,\n 35.17380831799959\n ],\n [\n -88.6376953125,\n 36.59788913307022\n ],\n [\n -88.76953125,\n 36.914764288955936\n ],\n [\n -89.3408203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"6","edition":"1010","noUsgsAuthors":false,"publicationDate":"2020-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haugh, Connor J. 0000-0002-5204-8271","orcid":"https://orcid.org/0000-0002-5204-8271","contributorId":219945,"corporation":false,"usgs":true,"family":"Haugh","given":"Connor J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797185,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214081,"text":"70214081 - 2020 - The roles of storminess and sea level rise in decadal barrier island evolution","interactions":[],"lastModifiedDate":"2020-09-22T15:15:12.947787","indexId":"70214081","displayToPublicDate":"2020-09-13T10:09:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The roles of storminess and sea level rise in decadal barrier island evolution","docAbstract":"Models of alongshore sediment transport during quiescent conditions, storm‐driven barrier island morphology, and poststorm dune recovery are integrated to assess decadal barrier island evolution under scenarios of increased sea levels and variability in storminess (intensity and frequency). Model results indicate barrier island response regimes of keeping pace, narrowing, flattening, deflation (narrowing and flattening), and aggradation. Under lower storminess scenarios, more areas of the island experienced narrowing due to collision. Under higher storminess scenarios, more areas experienced flattening due to overwash and inundation. Both increased sea levels and increased storminess resulted in breaching when the majority of the island was not keeping pace and deflation was the dominant regime due to increased overtopping. Under the highest storminess scenario, the island was unable to recover elevation after storms and drowned in just 10 years.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089370","usgsCitation":"Passeri, D., Dalyander, P., Long, J.W., Mickey, R.C., Jenkins, R., Thompson, D.M., Plant, N.G., Godsey, E., and Gonzalez, V., 2020, The roles of storminess and sea level rise in decadal barrier island evolution: Geophysical Research Letters, v. 47, no. 18, e2020GL089370, 8 p., https://doi.org/10.1029/2020GL089370.","productDescription":"e2020GL089370, 8 p.","ipdsId":"IP-121601","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":378665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35273742675781,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.207454473209072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"18","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":799390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":799391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":799395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Godsey, Elizabeth","contributorId":177095,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","affiliations":[],"preferred":false,"id":799396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gonzalez, Victor","contributorId":173702,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Victor","affiliations":[],"preferred":false,"id":799397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70218454,"text":"70218454 - 2020 - Hydro-climatic drought in the Delaware River Basin","interactions":[],"lastModifiedDate":"2021-02-26T13:54:09.110536","indexId":"70218454","displayToPublicDate":"2020-09-13T07:50:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydro-climatic drought in the Delaware River Basin","docAbstract":"The Delaware River Basin (DRB) supplies water to approximately 15 million people and is essential to agriculture and industry. In this study, a monthly water balance model is used to compute monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [R]) for the DRB for the 1901 through 2015 period. Water‐year R is used to identify drought periods in the basin and seven drought periods were identified. All but one of the drought periods occurred before about 1970; after this date, precipitation increased in the DRB and droughts were infrequent. The seven droughts were largely driven by precipitation deficits, rather than by unusually warm temperatures. For six of the seven droughts, the precipitation deficits were associated with atmospheric pressure patterns that resulted in northerly wind anomalies (i.e., conditions that deviate from the long‐term mean) over the basin that indicate an anomalous flow of dry air from the North American continent into the DRB. An examination of drought events estimated from a tree ring–based reconstruction of the Palmer Drought Severity Index for the 490 through 2005 time period indicates that although there were some DRB droughts that were longer and more severe during previous centuries, the DRB droughts during 1901 through 2015 were comparable in duration and severity to most drought events during previous centuries.
The Geomagnetism Program of the U.S. Geological Survey (USGS) monitors geomagnetic field variation through operation of a network of observatories across the United States and its territories, and it pursues scientific research needed to estimate and assess geomagnetic and geoelectric hazards. Over the next five years (2020–2024 inclusive) and in support of national and agency priorities, Geomagnetism Program research scientists plan to pursue an integrated set of research projects broadly encompassing empirical estimation and mapping of geomagnetic disturbance, modeling of solid-Earth conductivity structure and surface impedance, and mapping of magnetic-storm-induced geoelectric fields. Analyses are empirically based, relying on measured time series as well as statistical and numerical modeling of geomagnetic-monitoring data from ground-based observatories and surface-impedance tensors acquired during magnetotelluric surveys. The plan describes augmentation and development of the Geomagnetism Program's existing research portfolio, assuming present funding levels and staffing numbers. Because the projects are interdependent, they cannot be straightforwardly prioritized. They will all be pursued as resources and time permit; additional funding and staffing would enable the projects to be broadened and more rapidly completed. Where appropriate and subject to budgetary constraints and staffing numbers, research on specific projects might be accelerated or even judiciously expanded—some opportunities for expansion are discussed in this plan. Results will provide realistic illumination of the nature of the ground-level expression of space-weather disturbance, a subject of particular importance for projects focused on evaluating the vulnerability of electric-power-grid systems. This plan does not cover Geomagnetism Program operations, which are primarily concerned with the operation of magnetic observatories and, now, magnetotelluric surveys, although the context of such observatories and surveys is discussed. The research element of the program provides guidance for the expansion of program operations and research projects. In addition to the research projects summarized here, program scientists continue to provide leadership to the national and international geomagnetic, magnetotelluric, and space-weather communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1469","issn":"2330-5703","usgsCitation":"Love, J.J., Kelbert, A., Murphy, B.S., Rigler, E.J., and Lewis, K.A., 2020, Geomagnetism Program research plan, 2020–2024: U.S. Geological Survey Circular 1469, 19 p., https://doi.org/10.3133/cir1469.","productDescription":"viii, 19 p.","onlineOnly":"Y","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":378321,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1469/coverthb.jpg"},{"id":378322,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1469/circ1469.pdf","text":"Report","size":"14.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1469"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225
The ability to effectively manage game species for specific conservation objectives is often limited by the scientific understanding of their distribution and abundance. This is especially true in Hawai‘i where introduced game mammals are poorly studied and have low value relative to native species in other states. We modeled the habitat suitability and ecological associations of European mouflon sheep (“mouflon”; Ovis musimon) and axis deer (Axis axis) on the island of Lāna‘i using intensive aerial survey and environmental data that included climate, vegetation, and topographic variables. We conducted diagnostic tests on a suite of primarily categorical predictors and determined most were highly correlated. We therefore developed a suite of other spatial predictor layers with continuous variables. We tested several modeling approaches but settled on generalized linear models (GLM) and random GLMs because they could account for group size of animals and were based on curvilinear responses of each species to environmental variability. Both mammal species were habitat generalists showing little affinity to particular plant species or communities. Continuous predictors associated with plant productivity such as mean annual precipitation, normalized difference vegetation index (NDVI), and cloud cover were important explanatory factors in a GLM of axis deer and a random GLM of mouflon habitat suitability. The presence of axis deer was also an important explanatory predictor for mouflon distribution, but deer were not influenced by mouflon distribution, indicating asymmetrical competition. Consequently, mouflon were restricted to lower elevation arid and very dry slopes, whereas axis deer were more broadly distributed throughout other upland environments of the island, but avoided steep terrain. Findings indicate that removal of a substantial portion of the more abundant axis deer population may lead to an increase in abundance and distribution of mouflon without containment. Resulting spatial models of game mammal habitat suitability will be employed to inform land use prioritization analyses and to help resolve long-standing conflicts between native species conservation and sustained-yield hunting.
","language":"English","publisher":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i","usgsCitation":"Hess, S.C., Fortini, L., Leopold, C., Muise, J., and Sprague, J., 2020, Habitat suitability and ecological associations of two non-native ungulate species on the Hawaiian island of Lanai: Hawaii Cooperative Studies Unit Technical Report Series 91, iv, 30 p.","productDescription":"iv, 30 p.","ipdsId":"IP-113538","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":378620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378602,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5383"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Lāna‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.79962158203125,\n 20.824159066298787\n ],\n [\n -156.8909454345703,\n 20.917189979347988\n ],\n [\n -156.9891357421875,\n 20.931941310423674\n ],\n [\n -157.060546875,\n 20.913982976117605\n ],\n [\n -157.06329345703125,\n 20.88383379386135\n ],\n [\n -157.0323944091797,\n 20.85624519604873\n ],\n [\n -157.0001220703125,\n 20.834427371957577\n ],\n [\n -156.9891357421875,\n 20.812606385754087\n ],\n [\n -156.99462890624997,\n 20.78564668820214\n ],\n [\n -156.9843292236328,\n 20.756113874762082\n ],\n [\n -156.9609832763672,\n 20.72400644605942\n ],\n [\n -156.88201904296875,\n 20.73877670943921\n ],\n [\n -156.8305206298828,\n 20.75868217465891\n ],\n [\n -156.80374145507812,\n 20.804904106750566\n ],\n [\n -156.79962158203125,\n 20.821591880501483\n ],\n [\n -156.79962158203125,\n 20.824159066298787\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hess, Steve C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":150366,"corporation":false,"usgs":true,"family":"Hess","given":"Steve","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":799277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":799278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Christina 0000-0003-0499-3196","orcid":"https://orcid.org/0000-0003-0499-3196","contributorId":178961,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","affiliations":[],"preferred":false,"id":799279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muise, Jacob","contributorId":240997,"corporation":false,"usgs":false,"family":"Muise","given":"Jacob","email":"","affiliations":[{"id":48185,"text":"KIA Hawaii","active":true,"usgs":false}],"preferred":false,"id":799280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprague, Jonathan","contributorId":240998,"corporation":false,"usgs":false,"family":"Sprague","given":"Jonathan","email":"","affiliations":[{"id":48186,"text":"Pulama Lana‘i","active":true,"usgs":false}],"preferred":false,"id":799281,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220321,"text":"70220321 - 2020 - Lead speciation, bioaccessibility and source attribution in Missouri's Big River watershed","interactions":[],"lastModifiedDate":"2021-05-06T11:47:35.150072","indexId":"70220321","displayToPublicDate":"2020-09-11T06:43:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Lead speciation, bioaccessibility and source attribution in Missouri's Big River watershed","docAbstract":"The Southeast Missouri Lead District is among the most productive lead deposits exploited in modern times. Intensive mining conducted prior to regulations resulted in a legacy of lead contaminated soil, large piles of mine tailings and elevated childhood blood lead levels. This study seeks to identify the source of the lead contamination in the Big River and inform risk to the public. Isotopic analysis indicated the mine tailing piles at the head of the Big River are the primary source of the lead contamination. The isotopic signature of the lead in these mine tailings matched the lead over 100 km downstream. All of the other potential lead sources investigated had different isotopic signatures. Lead concentrations in soils and sediments decrease with distance downstream of the mine tailings piles. Additionally, the speciation of the lead changes from predominantly mineralized forms, such as galena, to adsorbed lead. This is reflected in the in-vitro bioaccessibility assay (IVBA) analysis which shows higher bioaccessibility further downstream, demonstrating the importance of speciation in risk evaluation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104757","usgsCitation":"Noerpel, M., Pribil, M., Rutherford, D., Law, P., Bradham, K., Nelson, C., Weber, R., Gunn, G., and Scheckel, K.G., 2020, Lead speciation, bioaccessibility and source attribution in Missouri's Big River watershed: Applied Geochemistry, v. 123, 104757, 11 p., https://doi.org/10.1016/j.apgeochem.2020.104757.","productDescription":"104757, 11 p.","ipdsId":"IP-115151","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455349,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7787989","text":"Publisher Index Page"},{"id":385438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River 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Agency, Region 7","active":true,"usgs":false}],"preferred":false,"id":815159,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scheckel, Kirk G.","contributorId":167121,"corporation":false,"usgs":false,"family":"Scheckel","given":"Kirk","email":"","middleInitial":"G.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815160,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215761,"text":"70215761 - 2020 - Habitat use by tiger prey in Thailand’s Western Forest Complex: What will it take to fill a half-full tiger landscape?","interactions":[],"lastModifiedDate":"2020-10-29T12:56:30.154835","indexId":"70215761","displayToPublicDate":"2020-09-10T07:53:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2142,"text":"Journal for Nature Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use by tiger prey in Thailand’s Western Forest Complex: What will it take to fill a half-full tiger landscape?","docAbstract":"Tiger populations are declining globally, and depletion of major ungulate prey is an important contributing factor. To better understand factors affecting prey distribution in Thailand’s Western Forest Complex (WEFCOM), we conducted sign surveys for gaur (Bos gaurus), banteng (Bos javanicus), and sambar (Rusa unicolor) along 3517 1-km transects and used occupancy models to identify important covariates associated with habitat use by each species. Habitat use by both gaur and sambar was lowest in areas closest to human settlements, although sambar preferred lower slopes near streams whereas gaur preferred steeper slopes at higher elevations. Banteng were found in only one of 17 protected areas (Huai Kha Khaeng [HKK] Wildlife Sanctuary), where they used low elevations and low slopes. We used these modeled relationships to predict occurrence of gaur, sambar, and banteng across each square km of the 19,000 km2 WEFCOM landscape, using > 60 % occupancy probability to define suitable habitat use for each species. Based on this criterion, gaur and sambar occupied 28 and 50 % of suitable habitat in WEFCOM, and banteng occupied 57 % of suitable habitat in HKK. We used our models to assess the effectiveness of two hypothetical conservation initiatives. First, we modeled the impact of decreasing human activities around nine villages in the core of WEFCOM, which increased predicted suitable habitat in WEFCOM to 68 and 75 % for guar and sambar. We also modeled the extent of potential banteng habitat that still remains in the other 16 protected areas. This could result in a 4-fold increase in banteng suitable habitat in WEFCOM. This is the first study to use occupancy surveys to determine where large prey species can be restored to support management to increase the distribution of tigers, and potentially fill a half-full tiger landscape.