The field of geospatial analytics and modeling has a long history coinciding with the physical and cultural evolution of humans. This history is analyzed relative to the four scientific paradigms: (1) empirical analysis through description, (2) theoretical explorations using models and generalizations, (3) simulating complex phenomena and (4) data exploration. Correlations among developments in general science and those of the geospatial sciences are explored. Trends identify areas ripe for growth and improvement in the fourth and current paradigm that has been spawned by the big data explosion, such as exposing the ‘black box’ of GeoAI training and generating big geospatial training datasets. Future research should focus on integrating both theory- and data-driven knowledge discovery.
From the shores of Jamestown and spreading north, south, and west, the lands that became the State of Virginia were some of the first in North America top experience rapid landscape change from European settlement. Imagery and data from the USGS Landsat series of satellites offer an unparalleled resource for the study, understanding, and preservation of Virginia’s land and water resources. From monitoring the health of water bodies to managing invasive species to planning for a range of climate change effects, the USGS National Land Imaging Program’s stewardship and public delivery of Landsat data have benefitted Virginians in myriad ways.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
With the United Nations Decade on Ecosystem Restoration, restoration of damaged ecosystems is turning into a global movement. Restoration actions that are not based on science and an understanding of ecosystem function can thwart desired restoration outcomes at best and cause further damage to ecosystems at worst. Restoration often includes revegetation using seed. Where we source seed for restoration can make a difference for species establishment, restoration outcomes, and recovery of ecosystem function. However, sourcing seeds of native species, let alone genetically appropriate seed, is not currently possible for many restoration projects. The process of increasing and sourcing suitable seed for restoration includes many steps that need to be addressed typically years before a restoration project is initiated. These steps of seed collection, evaluation and development, field establishment, production, certification and procurement, storage, and finally restoration, need to be considered ideally at a scale larger than individual restoration projects and with research conducted in each step. We describe these steps as implemented in the United States, the challenges therein, and provide suggestions and examples of how groups can make efficient and effective progress toward getting the right seed in the right place at the right time.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13499","usgsCitation":"Mccormick, M.L., Carr, A.N., Massatti, R., Winkler, D.E., De Angelis, P., and Olwell, P., 2021, How to increase the supply of native seed to improve restoration success: The US native seed development process: Restoration Ecology, v. 29, no. 8, e13499, 9 p., https://doi.org/10.1111/rec.13499.","productDescription":"e13499, 9 p.","ipdsId":"IP-129073","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450919,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13499","text":"Publisher Index Page"},{"id":389146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Mccormick, Molly Lutisha 0000-0002-4361-7567","orcid":"https://orcid.org/0000-0002-4361-7567","contributorId":265148,"corporation":false,"usgs":true,"family":"Mccormick","given":"Molly","email":"","middleInitial":"Lutisha","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Amanda N.","contributorId":265604,"corporation":false,"usgs":false,"family":"Carr","given":"Amanda","email":"","middleInitial":"N.","affiliations":[{"id":54729,"text":"Chicago Botanic Garden, Negaunee Institute for Plant Conservation Science and Action, 1000 Lake Cook Rd, Glencoe, IL 60022","active":true,"usgs":false}],"preferred":false,"id":823086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Angelis, Patricia","contributorId":265605,"corporation":false,"usgs":false,"family":"De Angelis","given":"Patricia","email":"","affiliations":[{"id":54730,"text":"U.S. Fish and Wildlife Service, International Affairs, Division of Scientific Authority, 5275 Leesburg Pike, Falls Church, VA 22041-3803","active":true,"usgs":false}],"preferred":false,"id":823089,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olwell, Peggy","contributorId":213569,"corporation":false,"usgs":false,"family":"Olwell","given":"Peggy","email":"","affiliations":[{"id":38799,"text":"Bureau of Land Management, Washington DC","active":true,"usgs":false}],"preferred":false,"id":823090,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223790,"text":"70223790 - 2021 - Contemporary and historic dynamics of lake whitefish (Coregonus clupeaformis) eggs, larvae, and juveniles suggest recruitment bottleneck during first growing season","interactions":[],"lastModifiedDate":"2021-09-08T12:51:05.717868","indexId":"70223790","displayToPublicDate":"2021-09-06T07:49:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9331,"text":"Annales Zoologici Finnici","active":true,"publicationSubtype":{"id":10}},"title":"Contemporary and historic dynamics of lake whitefish (Coregonus clupeaformis) eggs, larvae, and juveniles suggest recruitment bottleneck during first growing season","docAbstract":"To determine if a survival bottleneck occurs in Lake Erie's lake whitefish (Coregonus clupeaformis) population and explore possible mechanisms responsible, we examined contemporary and historical dynamics of lake whitefish eggs, larvae and juveniles. Widespread spawning and low overwinter egg retention were observed in 2016–2018, however subsequent larval CPUE remained consistent with historical observations when regular recruitment occurred. Highest larval CPUE was consistently observed in nearshore areas 3–11 km from mid-lake spawning locations. Fall age-1 juvenile presence was predicted by fall age-0 catches, indicating the bottleneck occurs during the first growing season. Our results suggest the following: (1) factors limiting recruitment affect survival during or after the pelagic larval stage to fall age-0, and (2) physical and biological processes underlying connectivity between spawning and nearshore nursery habitats may be limiting recruitment. Future research focusing on larval nursery habitat characterization and lake whitefish growth and survival may reveal mechanisms affecting recruitment.
About 14.5 months after the 2018 eruption and summit collapse of Kīlauea Volcano, Hawaiʻi, liquid water started accumulating in the deepened summit crater, forming a lake that attained 51 m depth before rapidly boiling off on December 20, 2020, when an eruption from the crater wall poured lava into the lake. Modeling the growth of the crater lake at Kīlauea summit is important for assessing the potential for explosive volcanism. Our current understanding of the past 2500 years of eruptive activity at Kīlauea suggests a slight dominance of explosive behavior over effusive. The deepened summit crater and presence of the crater lake in 2019 raised renewed concerns about explosive activity. Groundwater models using hydraulic-property data from a nearby drillhole successfully forecast the timing and rate of lake filling. Here we compare the groundwater-model predictions with observational data through the demise of the crater lake, examine the implications for local water-table configuration, consider the potential role of evaporation and recharge (neglected in previous models), and briefly discuss the energetics of the rapid boil-off. This post audit of groundwater-flow models of Kīlauea summit shows that simple models can sometimes be used effectively to simulate complex settings such as volcanoes.
Across portions of the western Great Plains in North America, natural fire has been removed from grassland ecosystems, decreasing vegetation heterogeneity and allowing woody encroachment. The loss of fire has implications for grassland species requiring diverse vegetation patches and structure or patches that have limited occurrence in the absence of fire. The lesser prairie-chicken (Tympanuchus pallidicinctus) is a declining species of prairie-grouse that requires heterogeneous grasslands throughout its life history and fire has been removed from much of its occupied range. Patch-burn grazing is a management strategy that re-establishes the fire-grazing interaction to a grassland system, increasing heterogeneity in vegetation structure and composition. We evaluated the effects of patch-burn grazing on lesser prairie-chicken space use, habitat features, and vegetation selection during a 4-year field study from 2014–2017. Female lesser prairie-chickens selected 1- and 2-year post-fire patches during the lekking season, ≥4-year post-fire patches during the nesting season, and year-of-fire and 1-year post-fire patches during post-nesting and nonbreeding seasons. Vegetation selection during the lekking season was not similar to available vegetation in selected patches, suggesting that lesser prairie-chickens cue in on other factors during the lekking season. During the nesting season, females selected nest sites with greater visual obstruction, which was available in ≥4-year post-fire patches; during the post-nesting season, females selected sites with 15–25% bare ground, which was available in the year-of-fire, 1-year post-fire, and 2-year post-fire patches; and during the nonbreeding season they selected sites with lower visual obstruction, available in the year-of-fire and 1-year post-fire patches. Because lesser prairie-chickens selected all available time-since-fire patches during their life history, patch-burn grazing may be a viable management tool to restore and maintain lesser prairie-chicken habitat on the landscape. © 2021 The Wildlife Society.
Functional connectivity (i.e., the movement of individuals across a landscape) is essential for the maintenance of genetic variation and persistence of rare species. However, illuminating the processes influencing functional connectivity and ultimately translating this knowledge into management practice remains a fundamental challenge. Here, we combine various population structure analyses with pairwise, population-specific demographic modeling to investigate historical functional connectivity in Graham’s beardtongue (Penstemon grahamii), a rare plant narrowly distributed across a dryland region of the western US. While principal component and population structure analyses indicated an isolation-by-distance pattern of differentiation across the species’ range, spatial inferences of effective migration exposed an abrupt shift in population ancestry near the range center. To understand these seemingly conflicting patterns, we tested various models of historical gene flow and found evidence for recent admixture (~ 3400 generations ago) between populations near the range center. This historical perspective reconciles population structure patterns and suggests management efforts should focus on maintaining connectivity between these previously isolated lineages to promote the ongoing transfer of genetic variation. Beyond providing species-specific knowledge to inform management options, our study highlights how understanding demographic history may be critical to guide conservation efforts when interpreting population genetic patterns and inferring functional connectivity.
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (Odocoileus virginianus), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22120","usgsCitation":"Bauder, J., Anderson, C.S., Gibbs, H., Tonkovich, M., and Walter, W., 2021, Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA: Journal of Wildlife Management, v. 85, no. 8, p. 1669-1684, https://doi.org/10.1002/jwmg.22120.","productDescription":"16 p.","startPage":"1669","endPage":"1684","ipdsId":"IP-128673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan M.","contributorId":288641,"corporation":false,"usgs":false,"family":"Bauder","given":"Javan M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":838159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christine S.","contributorId":288642,"corporation":false,"usgs":false,"family":"Anderson","given":"Christine","email":"","middleInitial":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, H. Lisle","contributorId":288643,"corporation":false,"usgs":false,"family":"Gibbs","given":"H. Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tonkovich, Michael J.","contributorId":288644,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Michael J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":838162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838158,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223767,"text":"70223767 - 2021 - Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales","interactions":[],"lastModifiedDate":"2021-10-18T14:28:57.67922","indexId":"70223767","displayToPublicDate":"2021-09-03T10:15:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales","docAbstract":"The abundance of greater white-fronted geese (Anser albifrons frontalis) on the Arctic Coastal Plain (ACP) of northern Alaska, USA, has more than tripled since the late 1990s; however, recent rate of annual population growth has declined as population size increased, which may indicate white-fronted geese on the ACP are approaching carrying capacity. We examined rates of gosling growth in greater white-fronted geese at 3 sites on the ACP during 2012–2014 to assist with predictions of future population trends and assess evidence for density-dependent constraints on recruitment. We marked goslings at hatch with individually coded webtags and conducted brood drives during early August to capture, measure, and weigh goslings. Annual estimates of gosling mass at 32 days old (range = 1,190–1,685) indicate that goslings had obtained >60% of asymptotic size. This rate of growth corresponds with that of other goose species and populations with access to high-quality forage and no limitations on forage availability, and is consistent with the overall increase in abundance of white-fronted geese at the ACP scale. Contrary to most previous investigations, age-adjusted mass of goslings did not decline with hatch date. Goslings grew faster in coastal areas than at inland freshwater sites. Taken together, these findings suggest forage was not limiting gosling growth rates in either ecosystem, but forage was of greater quality in coastal areas where goose foraging habitat is expanding because of permafrost subsidence. Spatial patterns of gosling growth corresponded with local-scale patterns of population density and population change; the areas with greatest rates of gosling growth were those with the greatest population density and rates of population increase. We found little evidence to suggest forage during brood rearing was limiting population increase of white-fronted geese on the ACP. Factors responsible for the apparent slowing of ACP-wide population growth are likely those that occur in stages of the annual cycle outside of the breeding grounds.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.22115","usgsCitation":"Fondell, T.F., Meixell, B.W., and Flint, P.L., 2021, Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales: Journal of Wildlife Management, v. 85, no. 8, p. 1591-1600, https://doi.org/10.1002/jwmg.22115.","productDescription":"10 p.","startPage":"1591","endPage":"1600","ipdsId":"IP-113368","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":436212,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92OQ9A4","text":"USGS data release","linkHelpText":"Capture and Measurement Data of Greater White-Fronted Geese (Anser albifrons) from the Arctic Coastal Plain of Alaska, 2012-2014"},{"id":436211,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92OQ9A4","text":"USGS data release","linkHelpText":"Capture and Measurement Data of Greater White-Fronted Geese (Anser albifrons) from the Arctic Coastal Plain of Alaska, 2012-2014"},{"id":388878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"National Petroleum Reserve–Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.5107421875,\n 69.10777677331498\n ],\n [\n -148.16162109375,\n 69.10777677331498\n ],\n [\n -148.16162109375,\n 71.42017915498717\n ],\n [\n -158.5107421875,\n 71.42017915498717\n ],\n [\n -158.5107421875,\n 69.10777677331498\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fondell, Thomas F tfondell@usgs.gov","contributorId":219605,"corporation":false,"usgs":false,"family":"Fondell","given":"Thomas","email":"tfondell@usgs.gov","middleInitial":"F","affiliations":[{"id":40039,"text":"USGS Alaska Science Center (Deceased)","active":true,"usgs":false}],"preferred":false,"id":822587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":822588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":822589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223746,"text":"70223746 - 2021 - Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","interactions":[],"lastModifiedDate":"2021-09-07T14:51:20.460964","indexId":"70223746","displayToPublicDate":"2021-09-03T09:46:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","title":"Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","docAbstract":"Sea lamprey (SL; Petromyzon marinus), one of the oldest living vertebrates, have a complex metamorphic life cycle. Following hatching, SL transition into a microphagous, sediment burrowing larval stage, and after 2–10+ years, the larvae undergo a dramatic metamorphosis, transforming into parasitic juveniles that feed on blood and bodily fluids of fishes; adult lamprey cease feeding, spawn, and die. Since gut microbiota are critical for the overall health of all animals, we examined the microbiota associated with SLs in each life history stage. We show that there were significant differences in the gut bacterial communities associated with the larval, parasitic juvenile, and adult life stages. The transition from larval to the parasitic juvenile stage was marked with a significant shift in bacterial community structure and reduction in alpha diversity. The most abundant SL-associated phyla were Proteobacteria, Fusobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Firmicutes, with their relative abundances varying among the stages. Moreover, while larval SL were enriched with unclassified Fusobacteriaceae, unclassified Verrucomicrobiales and Cetobacterium, members of the genera with fastidious nutritional requirements, such as Streptococcus, Haemophilus, Cutibacterium, Veillonella, and Massilia, were three to four orders of magnitude greater in juveniles than in larvae. In contrast, adult SLs were enriched with Aeromonas, Iodobacter, Shewanella, and Flavobacterium. Collectively, our findings show that bacterial communities in the SL gut are dramatically different among its life stages. Understanding how these communities change over time within and among SL life stages may shed more light on the role that these gut microbes play in host growth and fitness.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2021.706683","usgsCitation":"Mathai, P., Byappanahalli, M., Johnson, N.S., and Sadowsky, M.J., 2021, Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages: Frontiers in Microbiology, v. 12, 706683, 11 p., https://doi.org/10.3389/fmicb.2021.706683.","productDescription":"706683, 11 p.","ipdsId":"IP-129553","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2021.706683","text":"Publisher Index Page"},{"id":388873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Lake Huron watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.24316406249999,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 42.85180609584705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Mathai, P 0000-0001-5261-9911","orcid":"https://orcid.org/0000-0001-5261-9911","contributorId":265312,"corporation":false,"usgs":false,"family":"Mathai","given":"P","email":"","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadowsky, Michael J.","contributorId":34003,"corporation":false,"usgs":false,"family":"Sadowsky","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822541,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224925,"text":"70224925 - 2021 - Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","interactions":[],"lastModifiedDate":"2021-10-05T12:21:37.890807","indexId":"70224925","displayToPublicDate":"2021-09-03T07:18:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","docAbstract":"Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13–29 km2) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km2 (Brandy Creek), 2,700 ± 527 t/km2 (Boulder Creek), and 305 ± 58.0 t/km2 (Whiskey Creek)—some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by chaparral pre-fire. The second post-fire year (a dry year) was characterized by fluvial reworking and delta progradation of the first-year deposits and relatively little new sediment export. First-year sedimentation of 111,000 m3 represented minor loss of storage capacity in Whiskeytown Lake but would be detrimental to smaller reservoirs; in general, increased sediment yields from western US watersheds as fire and extreme rainfall increase will likely pose risks to water quality and storage.
Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Livebearers in the family Poeciliidae are some of the most widely introduced fishes. Native poeciliid translocations within the U.S. are mostly due to deliberate stocking for mosquito control. Introductions of exotic poeciliids, those not native to the U.S., are more likely to be due to release from aquaria or escape from farms. Many of these non-natives originate from warm climate regions, contrasting with the relatively cold climates in the U.S. Thus, thermal springs may increase the possible range of these species. Our primary objective was to examine the importance of climate and thermal springs in affecting the distribution of translocated and non-native poeciliids in the U.S. This objective was addressed using a national database of poeciliid introductions. Records were dominated by a handful of states and most introductions led to established populations. While translocated mosquitofish were found across many states and climates, non-natives were found almost exclusively in warm climate states and territories (e.g., Florida, Hawaii, Puerto Rico), especially where air temperatures remained above freezing. Outside warm climate states, 46% of established non-native populations were located at thermal spring sources. These results indicate that thermal springs extend the distribution of non-natives, but were relatively unimportant for translocated poeciliids.
Nannizziopsis guarroi is an ascomycete fungus associated with a necrotizing dermatitis in captive green iguanas (Iguana iguana) and bearded dragons (Pogona vitticeps) across both Europe and North America. Clinical signs of the disease include swelling and lesion formation. Lesions develop from white raised bumps on the skin and progress into crusty, yellow, discolored scales, eventually becoming necrotic. The clinical signs are the basis of a colloquial name yellow fungal disease (YFD). However, until now, N. guarroi has not been confirmed as the primary agent of the disease in bearded dragons. In this experiment, we fulfill Koch’s postulates criteria of disease, demonstrating N. guarroi as the primary agent of YFD in bearded dragons.
Costs to producing sexual signals can create selective pressures on males to invest signaling effort in particular contexts. When the benefits of signaling vary consistently across time, males can optimize signal investment to specific temporal contexts using biological rhythms. Sea lamprey, Petromyzon marinus, have a semelparous life history, are primarily nocturnal, and rely on pheromone communication for reproduction; however, whether male investment in pheromone transport and release matches increases in spawning activity remains unknown. By measuring (1) 3keto-petromyzonol sulfate (3kPZS, a main pheromone component) and its biosynthetic precursor PZS in holding water and tissue samples at six points over the course of 24 hours and (2) 3kPZS release over the course of several days, we demonstrate that 3kPZS release exhibits a consistent diel pattern across several days with elevated pheromone release just prior to sunset and at night. Trends in hepatic concentrations and circulatory transport of PZS and 3kPZS were relatively consistent with patterns of 3kPZS release and suggest the possibility of direct upregulation in pheromone transport and release rather than observed release patterns being solely a byproduct of increased behavioral activity. Our results suggest males evolved a signaling strategy that synchronizes elevated pheromone release with nocturnal increases in sea lamprey behavior. This may be imperative to ensure that male signaling effort is not wasted in a species having a single, reproductive event.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icb/icab190","usgsCitation":"Fissette, S.D., Bussy, U., Huerta, B., Brant, C.O., Li, K., Johnson, N.S., and Li, W., 2021, Diel patterns of pheromone release by male sea lamprey: Integrative and Comparative Biology, v. 61, no. 5, p. 1795-1810, https://doi.org/10.1093/icb/icab190.","productDescription":"16 p.","startPage":"1795","endPage":"1810","ipdsId":"IP-131044","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icb/icab190","text":"Publisher Index Page"},{"id":412801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fissette, Skye D.","contributorId":150994,"corporation":false,"usgs":false,"family":"Fissette","given":"Skye","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bussy, Ugo","contributorId":150993,"corporation":false,"usgs":false,"family":"Bussy","given":"Ugo","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huerta, Belinda","contributorId":222210,"corporation":false,"usgs":false,"family":"Huerta","given":"Belinda","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brant, Cory O.","contributorId":126746,"corporation":false,"usgs":false,"family":"Brant","given":"Cory","email":"","middleInitial":"O.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Ke","contributorId":172267,"corporation":false,"usgs":false,"family":"Li","given":"Ke","email":"","affiliations":[],"preferred":false,"id":863804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weiming","contributorId":126748,"corporation":false,"usgs":false,"family":"Li","given":"Weiming","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863629,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225501,"text":"70225501 - 2021 - Individual variation in temporal dynamics of post-release habitat selection","interactions":[],"lastModifiedDate":"2021-10-18T11:28:27.245581","indexId":"70225501","displayToPublicDate":"2021-09-03T06:26:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual variation in temporal dynamics of post-release habitat selection","docAbstract":"Translocated animals undergo a phase of behavioral adjustment after being released in a novel environment, initially prioritizing exploration and gradually shifting toward resource exploitation. This transition has been termed post-release behavioral modification. Post-release behavioral modification may also manifest as changes in habitat selection through time, and these temporal dynamics may differ between individuals. We aimed to evaluate how post-release behavioral modification is reflected in temporal dynamics of habitat selection and its variability across individuals using a population of translocated female greater sage-grouse as a case study. Sage-grouse were translocated from Wyoming to North Dakota (USA) during the summers of 2018–2020. We analyzed individual habitat selection as a function of sagebrush cover, herbaceous cover, slope, and distance to roads. Herbaceous cover is a key foraging resource for sage-grouse during summer; thus, we expected a shift from exploration to exploitation to manifest as temporally-varying selection for herbaceous cover. For each individual sage-grouse (N = 26), we tested two competing models: a null model with no time-dependence and a model with time-dependent selection for herbaceous cover. We performed model selection at the individual level using an information-theoretic approach. Time-dependence was supported for five individuals, unsupported for seven, and the two models were indistinguishable based on AICc for the remaining fourteen. We found no association between the top-ranked model and individual reproductive status (brood-rearing or not). We showed that temporal dynamics of post-release habitat selection may emerge in some individuals but not in others, and that failing to account for time-dependence may hinder the detection of steady-state habitat selection patterns. These findings demonstrate the need to consider both temporal dynamics and individual variability in habitat selection when conducting post-release monitoring to inform translocation protocols.
Lead (Pb) exposure is a widespread wildlife conservation threat. Although commonly associated with Pb-based ammunition from big-game hunting, small mammals (e.g., ground squirrels) shot for recreational or pest-management purposes represent a potentially important Pb vector in agricultural regions. We measured the responses of avian scavengers to pest-shooting events and examined their Pb exposure through consumption of shot mammals. There were 3.4-fold more avian scavengers at shooting fields relative to those at fields with no recent shooting, and avian scavengers spent 1.8-fold more time feeding after recent shooting events. We isotopically labeled shot ground squirrels in the field with an enriched 15N isotope tracer; 6% of avian scavengers sampled within a 39 km radius reflected this tracer in their blood. However, 33% of the avian scavengers within the average foraging dispersal distance of nests (0.6−3.7 km) were labeled, demonstrating the importance of these shooting fields as a source of food for birds nesting in close proximity. Additionally, Pb concentrations in 48% of avian scavengers exceeded subclinical poisoning benchmarks for sensitive species (0.03−0.20 μg/g w/w), and those birds exhibited reduced δ-aminolevulinic acid dehydratase activity, indicating a biochemical effect of Pb. The use of shooting to manage small mammal pests is a common practice globally. Efforts that can reduce the use of Pb-based ammunition may lessen the negative physiological effects of Pb exposure on avian scavengers.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c01041","usgsCitation":"Herring, G., Eagles-Smith, C., Goodell, J., Buck, J.A., and Willacker, J., 2021, Small mammal shooting as a conduit for lead exposure in avian scavengers: Environmental Science and Technology, v. 55, no. 18, p. 12272-12280, https://doi.org/10.1021/acs.est.1c01041.","productDescription":"9 p.","startPage":"12272","endPage":"12280","ipdsId":"IP-126933","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":397858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","county":"Lake County, Malheur 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Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodell, John","contributorId":289382,"corporation":false,"usgs":false,"family":"Goodell","given":"John","email":"","affiliations":[{"id":62121,"text":"High Desert Museum","active":true,"usgs":false}],"preferred":false,"id":839077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buck, Jeremy A.","contributorId":195480,"corporation":false,"usgs":false,"family":"Buck","given":"Jeremy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":839078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223740,"text":"70223740 - 2021 - Unexpected diversity of Endozoicomonas in deep-sea corals","interactions":[],"lastModifiedDate":"2021-09-03T12:12:53.673111","indexId":"70223740","displayToPublicDate":"2021-09-02T07:09:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Unexpected diversity of Endozoicomonas in deep-sea corals","docAbstract":"ABSTRACT: The deep ocean hosts a large diversity of azooxanthellate cold-water corals whose associated microbiomes remain to be described. While the bacterial genus Endozoicomonas has been widely identified as a dominant associate of tropical and temperate corals, it has rarely been detected in deep-sea corals. Determining microbial baselines for these cold-water corals is a critical first step to understanding the ecosystem services their microbiomes contribute, while providing a benchmark against which to measure responses to environmental change or anthropogenic effects. Samples of Acanthogorgia aspera, A. spissa, Desmophyllum dianthus, and D. pertusum (Lophelia pertusa) were collected from western Atlantic sites off the US east coast and from the northeastern Gulf of Mexico. Microbiomes were characterized by 16S rRNA gene amplicon surveys. Although D. dianthus and D. pertusum have recently been combined into a single genus due to their genetic similarity, their microbiomes were significantly different. The Acanthogorgia spp. were collected from submarine canyons in different regions, but their microbiomes were extremely similar and dominated by Endozoicomonas. This is the first report of coral microbiomes dominated by Endozoicomonas occurring below 1000 m, at temperatures near 4°C. D. pertusum from 2 Atlantic sites were also dominated by distinct Endozoicomonas, unlike D. pertusum from other sites described in previous studies, including the Gulf of Mexico, the Mediterranean Sea and a Norwegian fjord.
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