Eelgrass (Zostera marina) meadows are expansive along the lower Alaska Peninsula, supporting a rich diversity of marine life, yet little is known about their status and trends in the region. We tested techniques to inventory and monitor trends in the spatial extent and abundance of eelgrass in lagoons of the Izembek National Wildlife Refuge. We determined if Landsat imagery could be used to assess eelgrass spatial extent in shallow (less than 4 meter water depth) coastal waters of the refuge. We determined that this seagrass could be differentiated using Landsat imagery from other cover types (that is, channels and unvegetated tidal flats) with a high degree of accuracy (greater than 80 percent) in Izembek and Kinzarof Lagoons. Eelgrass meadows represented the largest cover type in Izembek (about 16,000 hectares) and Kinzarof (about 900 hectares) Lagoons, comprising between 45 and 50 percent of the spatial extent of these lagoons, respectively. When compared to estimates of spatial extent of eelgrass from previous studies, our results suggest little change in the spatial extent of eelgrass in Izembek Lagoon during the 28-year period 1978 through 2006. Preliminary mapping of eelgrass in other embayments indicated that this seagrass was also expansive in Big Lagoon (about 900 hectares; or 34 percent of the lagoon area) and Hook Bay (about 900 hectares; or 36 percent of the bay area) but not in Cold Bay (about 100 hectares; less than 5 percent of the bay area). We conducted an embayment-wide point sampling technique to assess aboveground biomass and distribution of eelgrass and seaweeds and presence of six macro-invertebrates during a 4-year period (2007–10). We determined that, when present, mean aboveground biomass of eelgrass was greater in Kinzarof Lagoon (182.5 plus or minus 12.1 grams dry weight per square meter) than in Izembek Lagoon (152.1 plus or minus 7.1 grams dry weight per square meter) in 2008–10, possibly reflecting the warmer sea temperatures and higher salinities found on the Gulf of Alaska side of the Alaska Peninsula. Seaweeds were more abundant in Kinzarof Lagoon than in Izembek Lagoon, surpassing aboveground biomass of eelgrass in both lagoons in 2008. Gastropods (4 percent of all points) and Caprella shrimp (25 percent) were the most common of the six macro-invertebrates surveyed in Izembek Lagoon, and Telmessus crab was the most common macro-invertebrate in Kinzarof Lagoon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201035","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly,T.F., Fairchild, L.L., Sowl, K.M., and Lindstrom, S.C., 2022, Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10: U.S. Geological Survey Open-File Report 2020–1035, 30 p., https://doi.org/10.3133/ofr20201035.","productDescription":"Report: vi, 30 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-112900","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUDIOH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling for eelgrass (Zostera marina) and seaweeds in embayments adjacent to the Izembek National Wildlife Refuge, Alaska"},{"id":384515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":435682,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XNSWES","text":"USGS data release","linkHelpText":"Sampling Data for Eelgrass (Zostera marina) in Norma Bay, Izembek Lagoon, Alaska, 1987"},{"id":405752,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405751,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":379325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1035/ofr20201035.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1035"},{"id":405754,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405753,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1035/coverthb.jpg"},{"id":405750,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.37081909179688,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.00755132274014\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered resource means of 1.8 billion barrels of oil and 129.5 trillion cubic feet of gas within New Guinea, Papua Barat, Seram, and Timor-Leste.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223046","usgsCitation":"Schenk, C.J., Mercier, T.J., Tennyson, M.E., Ellis, G.S., Woodall, C.A., Le, P.A., Leathers-Miller, H.M., and Drake, R.M., II, 2022, Assessment of undiscovered conventional oil and gas resources of New Guinea, Papua Barat, Seram, and Timor-Leste, 2020: U.S. Geological Survey Fact Sheet 2022–3046, 4 p., https://doi.org/10.3133/fs20223046.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128166","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":407171,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8ZIMP","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Papua New Guinea, Papua Barat, Seram, and Timor-Leste: Assessment Unit Boundaries, Assessment Input Forms, and Fact Sheet Data Tables"},{"id":407170,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3046/fs20223046.pdf","text":"Report","size":"916 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3046"},{"id":407169,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3046/coverthb.jpg"}],"country":"New Guinea, Papua Barat, Seram, and Timor-Leste","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 122,\n -16\n ],\n [\n 152,\n -16\n ],\n [\n 152,\n 4\n ],\n [\n 122,\n 4\n ],\n [\n 122,\n -16\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Seasonal precipitation affects carbon turnover and methane accumulation in karst subterranean estuaries, the region of coastal carbonate aquifers where hydrologic and biogeochemical processes regulate material exchange between the land and ocean. However, the impact that tropical cyclones exert on subsurface carbon cycling within karst landscapes is poorly understood. Here, we present 5-month-long hydrologic and chemical records from 1 and 2 km inland from the coastline within the Ox Bel Ha Cave System in the northeastern Yucatan Peninsula. The record encompasses wet and dry seasons and includes the impact of rainfall during the development of Tropical Storm Hanna in October 2014. Methane accumulated in highest concentrations at the inland site, especially during the wet season preceding the storm. Intense rainfall led to episodic increases in water level and salinity shifts at both sites, indicating a spatially widespread hydrologic response. The most profound storm effect was a ~ 0.8 mg L−1 pulse of dissolved oxygen that declined to zero within 2 weeks and corresponded with a reduction of methane. A positive shift in methane's stable carbon isotope content from −62.6‰ ± 0.6‰ before the storm to −44.0‰ ± 2.4‰ after the storm indicates microbial methane oxidation was a mechanism for the loss of groundwater methane. Post-storm methane concentrations did not recover to pre-storm levels during the observation period, suggesting tropical cyclones have long-lasting (months) effects on the carbon cycle. Compared to seasonal effects, mixing and oxygen inputs during storm-induced hydrologic forcing have an outsized biogeochemical influence within stratified coastal aquifers.
A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time.
First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term.
A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221081","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the U.S. Fish and Wildlife Service","usgsCitation":"Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.","productDescription":"Report: viii, 38 p.; Data Release; 3 Figures: 7.99 × 6.10 inches or smaller","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138940","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":407103,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig10.pdf","text":"Figure 10, full size","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Conifer 2020"},{"id":407104,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig11.pdf","text":"Figure 11, full size","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Human Modification 2020"},{"id":407025,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig09.pdf","text":"Figure 9, full size","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Invasive Annual Grass 2020"},{"id":407023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081.pdf","text":"Report","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":407022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1081/coverthb.jpg"},{"id":407024,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y5CDV","text":"USGS data release","linkHelpText":"Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 33\n ],\n [\n -101.25,\n 33\n ],\n [\n -101.25,\n 49\n ],\n [\n -121.6845703125,\n 49\n ],\n [\n -121.6845703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sagebrush Ecosystem Specialist
Land Management Research Program
Ecosystems Mission Area
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526
Arctic marine ecosystems are undergoing rapid physical and biological change associated with climate warming and loss of sea ice. Sea ice loss will impact many species through altered spatial and temporal availability of resources. In the Bering and Chukchi Seas, the Pacific walrus Odobenus rosmarus divergens is one species that could be impacted by rapid environmental change, and thus, population assessments are needed to monitor changes in the status of this ecologically and culturally important marine mammal. We conducted a 5 yr genetic mark-recapture study to estimate demographic parameters for the Pacific walrus. We developed a Bayesian multievent mark-recapture model to estimate walrus survival and abundance while accounting for age misclassification. We estimated the probability of juvenile annual survival as 0.63 (95% credible interval [CrI]: 0.39-0.87) and adult female annual survival as 0.90 (95% CrI: 0.74-1.00). We estimated total abundance as 257 193 (95% CrI: 171 138-366 366). We provide the first estimate of total Pacific walrus abundance since an aerial survey in 2006, which generated a substantially less precise total population size estimate (129 000; 95% CI: 55 000-507 000). The emerging ecosystem state in the northern Bering and Chukchi Seas will likely result in a decline in Pacific walrus abundance, but there is substantial uncertainty regarding the magnitude of the anticipated decline. Our demographic estimates provide critical information to evaluate future population trends of this subsistence resource vital to communities that border the Bering and Chukchi Seas in the USA and Russia.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps14131","usgsCitation":"Beatty, W., Lemons, P., Everett, J.P., Lewis, C.J., Taylor, R.L., Lynn, R.J., Sethi, S.A., Quakenbush, L.T., Citta, J.J., Kissling, M., Kryukova, N., and Wennburg, J.K., 2022, Estimating Pacific walrus abundance and survival with multievent mark-recapture models: Marine Ecology Progress Series, v. 697, p. 167-182, https://doi.org/10.3354/meps14131.","productDescription":"16 p.","startPage":"167","endPage":"182","ipdsId":"IP-137159","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":473,"text":"New York Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":446361,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14131","text":"Publisher Index Page"},{"id":407409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","otherGeospatial":"Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.9,\n 60.413852350464914\n ],\n [\n -159.2578125,\n 60.413852350464914\n ],\n [\n -159.2578125,\n 74\n ],\n [\n -179.9,\n 74\n ],\n [\n -179.9,\n 60.413852350464914\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"697","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lemons, Patrick R.","contributorId":288791,"corporation":false,"usgs":false,"family":"Lemons","given":"Patrick R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Everett, Jason P.","contributorId":296986,"corporation":false,"usgs":false,"family":"Everett","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":64270,"text":"U.S. Fish and Wildlife Service, Conservation Genetics Laboratory, Anchorage, Alaska 99503","active":true,"usgs":false}],"preferred":false,"id":853041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Cara J.","contributorId":288794,"corporation":false,"usgs":false,"family":"Lewis","given":"Cara","email":"","middleInitial":"J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":853043,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lynn, Robert J.","contributorId":288795,"corporation":false,"usgs":false,"family":"Lynn","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853044,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sethi, Suresh A. 0000-0002-0053-1827","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":296987,"corporation":false,"usgs":false,"family":"Sethi","given":"Suresh","email":"","middleInitial":"A.","affiliations":[{"id":64271,"text":"U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Ithaca, New York 14853","active":true,"usgs":false}],"preferred":false,"id":853045,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quakenbush, Lori T.","contributorId":192737,"corporation":false,"usgs":false,"family":"Quakenbush","given":"Lori","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":853046,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Citta, John J.","contributorId":175350,"corporation":false,"usgs":false,"family":"Citta","given":"John","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":853047,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kissling, Michelle","contributorId":222160,"corporation":false,"usgs":false,"family":"Kissling","given":"Michelle","affiliations":[{"id":40501,"text":"U.S. Fish and Wildlife Service, Marine Mammals Management, 3000 Vintage Blvd., Suite 201, Juneau, AK 99801","active":true,"usgs":false}],"preferred":false,"id":853048,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kryukova, Natalia","contributorId":296988,"corporation":false,"usgs":false,"family":"Kryukova","given":"Natalia","email":"","affiliations":[{"id":64272,"text":"Kamchatka Branch of the Pacific Geographical Institute of Far Eastern Branch of Russian Academy of Sciences, Petropavlovsk-Kamchatsky, Russia, 683000","active":true,"usgs":false}],"preferred":false,"id":853049,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wennburg, John K.","contributorId":296989,"corporation":false,"usgs":false,"family":"Wennburg","given":"John","email":"","middleInitial":"K.","affiliations":[{"id":64270,"text":"U.S. Fish and Wildlife Service, Conservation Genetics Laboratory, Anchorage, Alaska 99503","active":true,"usgs":false}],"preferred":false,"id":853050,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70248069,"text":"70248069 - 2022 - Multi-objective modeling as a decision-support tool for free-roaming horse management","interactions":[],"lastModifiedDate":"2023-09-05T15:08:53.279712","indexId":"70248069","displayToPublicDate":"2022-09-22T10:04:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Multi-objective modeling as a decision-support tool for free-roaming horse management","docAbstract":"Decisions related to controversial problems in natural resource management receive the greatest support when they account for multiple objectives of stakeholders in a structured and transparent fashion. In the United States, management of free-roaming horses (Equus caballus; horses) is a controversial multiple-objective problem because disparate stakeholder groups have varying objectives and opinions about how to manage fast-growing horse populations in ways that sustain both natural ecosystems and healthy horses. Despite much decision-support research on management alternatives that prevent excessive population size or cost, horse management decisions still receive resistance from a variety of stakeholder groups, potentially because decisions fail to explicitly or transparently account for multiple objectives of diverse stakeholders. Here, we used a predictive model for horse populations to evaluate the degree to which alternative management strategies involving removals and fertility control treatment with the immunocontraceptive vaccine PZP-22 maximize 4 objectives in horse management: maximize ecosystem health, maximize horse health, minimize effects on horse behavior, and minimize management cost. We simulated scenarios varying in management action, frequency, magnitude, and starting population size over a 10-year interval and evaluated scenario performance with a weighted multiple-objective utility reward function. Management involving high-magnitude removals along with PZP-22 treatment generally outperformed other alternatives by achieving higher reward relative to alternatives in 2 scenario analyses. Simulation of 1,372 scenarios at 5 starting population sizes generally found that management with biannual removals and 2 doses of PZP-22 treatment for half of eligible females during years 1 and 5 generated the most rewarding outcomes. However, a removal scenario with more frequent PZP-22 application generated the greatest reward when starting population size was already within target population size range. Our paper demonstrates how values and objectives of diverse stakeholders can be used to support management decisions in ways that might lead to greater acceptance of decisions by a broad array of stakeholder groups.
","language":"English","publisher":"Utah State University","doi":"10.26077/a6e8-0759","usgsCitation":"Folt, B.P., Schoenecker, K., and Ekernas, L.S., 2022, Multi-objective modeling as a decision-support tool for free-roaming horse management: Human-Wildlife Interactions, v. 16, no. 2, p. 233-250, https://doi.org/10.26077/a6e8-0759.","productDescription":"18 p.","startPage":"233","endPage":"250","ipdsId":"IP-134038","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435684,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRF1H9","text":"USGS data release","linkHelpText":"Multi-objective Modeling as a Decision-support Tool for Feral Horse Management"},{"id":420482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Folt, Brian Patrick 0000-0003-2278-2018","orcid":"https://orcid.org/0000-0003-2278-2018","contributorId":328937,"corporation":false,"usgs":true,"family":"Folt","given":"Brian","email":"","middleInitial":"Patrick","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ekernas, L. Stefan 0000-0002-9205-1985","orcid":"https://orcid.org/0000-0002-9205-1985","contributorId":223034,"corporation":false,"usgs":true,"family":"Ekernas","given":"L.","email":"","middleInitial":"Stefan","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881745,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256652,"text":"70256652 - 2022 - Golden eagle nesting territory distribution in wind energy landscapes of the southern Great Plains","interactions":[],"lastModifiedDate":"2024-08-29T14:53:18.784467","indexId":"70256652","displayToPublicDate":"2022-09-22T09:46:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Golden eagle nesting territory distribution in wind energy landscapes of the southern Great Plains","docAbstract":"Deaths of four Golden Eagles (Aquila chrysaetos) due to collision trauma at a new wind energy facility in east-central New Mexico during 2004–2005 prompted concerns about the species' population status in the encompassing Southern Great Plains region, primarily because its breeding distribution there was poorly documented and wind energy development was expanding rapidly. Therefore, we conducted aerial searches for Golden Eagle nests across northeastern New Mexico, northwestern Texas, western Oklahoma, and adjacent portions of Colorado and Kansas during 2006–2009 and 2015–2020. We delineated five Golden Eagle Nest Search Areas (NSAs) with unique physiographic/geological origins. Individual NSAs were searched partially or entirely for up to 8 yr. Collectively, we identified 123 nesting territories (NTs) occupied by Golden Eagles ≥ 1 yr, of which 94 (76%) were in northeastern New Mexico. The most NTs (40) were in the 11,720-km2 Highlands NSA. Greatest NT density (126.6 km2/NT) and shortest NT nearest neighbor distance (7.4 km) were in the 3533-km2 Northern Caprock NSA. Wind turbines existed near (within 3.2 km) eight nests distributed among five of 28 NTs in the Northern Caprock and were planned for sites in two occupied NTs. Elsewhere, only potential turbines were near nests and only within six NTs. The number of nesting territories we found underscores the importance of the Southern Great Plains to Golden Eagles, even though this region lies at the eastern margin of the species' western North American breeding range. Our data provide strong support for protecting breeding habitat from potential threats, particularly those posed by wind energy development, and are also a foundation for long-term population monitoring.
","language":"English","publisher":"The Raptor Research Foundation, Inc.","doi":"10.3356/JRR-21-68","usgsCitation":"Stahlecker, D., Wallace, Z., Mikesic, D., Boal, C.W., Murphy, R., Howe, W., and Ruehmann, M., 2022, Golden eagle nesting territory distribution in wind energy landscapes of the southern Great Plains: Journal of Raptor Research, v. 56, no. 4, p. 387-397, https://doi.org/10.3356/JRR-21-68.","productDescription":"11 p.","startPage":"387","endPage":"397","ipdsId":"IP-131460","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106,\n 38.1\n ],\n [\n -106,\n 34\n ],\n [\n -100,\n 34\n ],\n [\n -100,\n 38.1\n ],\n [\n -106,\n 38.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stahlecker, D.W.","contributorId":341492,"corporation":false,"usgs":false,"family":"Stahlecker","given":"D.W.","affiliations":[{"id":81689,"text":"Eagle Environmental, Inc.","active":true,"usgs":false}],"preferred":false,"id":908500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wallace, Z.P.","contributorId":341493,"corporation":false,"usgs":false,"family":"Wallace","given":"Z.P.","email":"","affiliations":[{"id":81689,"text":"Eagle Environmental, Inc.","active":true,"usgs":false}],"preferred":false,"id":908501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mikesic, D.G.","contributorId":341494,"corporation":false,"usgs":false,"family":"Mikesic","given":"D.G.","email":"","affiliations":[{"id":81745,"text":"Navajo Nation Zoo","active":true,"usgs":false}],"preferred":false,"id":908502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphy, R.K.","contributorId":271015,"corporation":false,"usgs":false,"family":"Murphy","given":"R.K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howe, W.H.","contributorId":341495,"corporation":false,"usgs":false,"family":"Howe","given":"W.H.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ruehmann, M.B.","contributorId":341496,"corporation":false,"usgs":false,"family":"Ruehmann","given":"M.B.","email":"","affiliations":[{"id":81746,"text":"Eagle Environmental Inc.","active":true,"usgs":false}],"preferred":false,"id":908506,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237241,"text":"70237241 - 2022 - Status of landbirds in the National Park of American Samoa","interactions":[],"lastModifiedDate":"2022-10-05T14:11:34.056505","indexId":"70237241","displayToPublicDate":"2022-09-22T08:59:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2990,"text":"Pacific Science","active":true,"publicationSubtype":{"id":10}},"title":"Status of landbirds in the National Park of American Samoa","docAbstract":"The National Park of American Samoa (NPSA) was surveyed in 2011 and 2018 using point-transect distance sampling to estimate trends in landbird distribution, composition, population density, and abundance. Surveys were conducted within the Ta‘ū Unit and Tutuila Unit, each on separate islands of American Samoa. We detected a total of 14 species during surveys and there were sufficient detections of seven species to allow for density estimation and abundance within each unit. We assessed differences in density between surveys with a two-sample z-test and found significant declines of Blue-crowned Lorikeets (Vini australis) in the Ta‘ū Unit, and of Samoan Starlings (Aplonis atrifusca) in the Tutuila Unit. Density estimates of the Crimson-crowned Fruit Dove (Ptilinopus porphyraceus), Pacific Kingfisher (Todiramphus sacer), Polynesian Wattled Honeyeater (Foulehaio carunculatus), and Samoan Starling (in the Ta‘ū Unit) were also lower in 2018 than 2011, but differences were inconclusive because of relatively large variance estimates. Densities of the Polynesian Starling (Aplonis tabuensis) and Pacific Imperial Pigeon (Ducula pacifica) in the Ta‘ū Unit were higher in 2018 than 2011, but differences were similarly inconclusive. Lower 2018 densities could be due to Tropical Cyclone Gita that struck the islands just four months before the surveys. We provide indices of relative occurrence and abundance for the remaining seven species detected, which include the Many-colored Fruit Dove (Ptilinopus perousii) and the rarely detected Spotless Crake (Zapornia tabuensis)—both of which are species of concern in American Samoa.
","language":"English","publisher":"BioOne","doi":"10.2984/76.2.4","usgsCitation":"Judge, S., Camp, R.J., Vaivai, V., and Hart, P.J., 2022, Status of landbirds in the National Park of American Samoa: Pacific Science, v. 76, no. 2, p. 139-156, https://doi.org/10.2984/76.2.4.","productDescription":"18 p.","startPage":"139","endPage":"156","ipdsId":"IP-131689","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2984/76.2.4","text":"Publisher Index Page"},{"id":407958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"American Samoa","otherGeospatial":"National Park of American Samoa, Ofu-Olosega, Ta'u, Tutuila","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.7392120361328,\n -14.317282180862385\n ],\n [\n -170.6403350830078,\n -14.317282180862385\n ],\n [\n -170.6403350830078,\n -14.231439639624147\n ],\n [\n -170.7392120361328,\n -14.231439639624147\n ],\n [\n -170.7392120361328,\n -14.317282180862385\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.6831512451172,\n -14.191834591858717\n ],\n [\n -169.60693359375,\n -14.191834591858717\n ],\n [\n -169.60693359375,\n -14.15055809981021\n ],\n [\n -169.6831512451172,\n -14.15055809981021\n ],\n [\n -169.6831512451172,\n -14.191834591858717\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.49363708496094,\n -14.275695888737538\n ],\n [\n -169.4194793701172,\n -14.275695888737538\n ],\n [\n -169.4194793701172,\n -14.207810571387945\n ],\n [\n -169.49363708496094,\n -14.207810571387945\n ],\n [\n -169.49363708496094,\n -14.275695888737538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":853715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","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":853716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaivai, Visa","contributorId":254982,"corporation":false,"usgs":false,"family":"Vaivai","given":"Visa","affiliations":[{"id":51382,"text":"National Park Service, I&M","active":true,"usgs":false}],"preferred":false,"id":853717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":853718,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238505,"text":"70238505 - 2022 - Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean","interactions":[],"lastModifiedDate":"2022-11-28T13:39:00.114514","indexId":"70238505","displayToPublicDate":"2022-09-22T07:34:30","publicationYear":"2022","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":"Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean","docAbstract":"Animals that regularly traverse habitat extremes between the subtropics and subarctic are expected to exhibit foraging behaviors that respond to changes in dynamic ocean habitats, and these behaviors may facilitate adaptations to novel and changing climates. During the chick-provisioning stage, Laysan albatross Phoebastria immutabilis parents regularly undertake short- and long-distance foraging trips throughout the vast central North Pacific Ocean. We examined GPS tracking data among chick-provisioning albatrosses in Hawai‘i to characterize habitats during short- and long-distance trips. The study period encompassed a marine heatwave (2014) and the cooling period after an extreme El Niño event (2016), enabling us to examine foraging habitats under novel and changing climates. First passage time and generalized additive mixed models indicated that during 183 short and 110 long trips (n = 32 birds), wind-assisted flight efficiency, proximity to productive areas, and moonlit-searching were important in both subtropical and subarctic habitats. Laysan albatross took foraging trips that had similar lengths and durations in 2014 and 2016 and visited similar areas, indicating that their foraging range did not expand in response to climatic variability. A strategy that uses similar foraging areas across years combined with reliance on environmental processes that enhance flight efficiency (wind) and that enable searching behaviors (moonlight) indicate that Laysan albatross exhibit complex behavioral plasticity that allows them to utilize subtropical and subarctic habitats affected by dynamic climate variability. This strategy may benefit their ability to respond to oceanographic and climatic change, including expanding warm water regions and changing atmospheric conditions influenced by global warming.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps14148","usgsCitation":"Gilmour, M.E., Felis, J.J., Hester, M.M., Young, L.C., and Adams, J., 2022, Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean: Marine Ecology Progress Series, v. 697, p. 125-147, https://doi.org/10.3354/meps14148.","productDescription":"23 p.","startPage":"125","endPage":"147","ipdsId":"IP-137932","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14148","text":"Publisher Index Page"},{"id":409685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 35.095301697524135\n ],\n [\n -177.86550682042343,\n 28.860574564487507\n ],\n [\n -155.53062317940885,\n 20.809565675106214\n ],\n [\n -147.22958348721264,\n 19.95935891124141\n ],\n [\n -134.00475959018274,\n 35.360858467785576\n ],\n [\n -136.90695297728425,\n 50.99805127330757\n ],\n [\n -142.66136789996617,\n 59.0913410074362\n ],\n [\n -147.22441346291703,\n 60.36962329343831\n ],\n [\n -154.7675208704121,\n 56.641133852727194\n ],\n [\n -176.45903611138576,\n 54.11395665374428\n ],\n [\n -178,\n 52.825004330008824\n ],\n [\n -179.9,\n 35.095301697524135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"697","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hester, Michelle M. 0000-0002-0769-5904","orcid":"https://orcid.org/0000-0002-0769-5904","contributorId":197785,"corporation":false,"usgs":false,"family":"Hester","given":"Michelle","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":857664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Lindsay C.","contributorId":149044,"corporation":false,"usgs":false,"family":"Young","given":"Lindsay","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":857665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857666,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259620,"text":"70259620 - 2022 - The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil","interactions":[],"lastModifiedDate":"2024-10-17T12:03:37.308351","indexId":"70259620","displayToPublicDate":"2022-09-22T06:55:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil","docAbstract":"Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the exchange of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of soil fungal and bacterial relative abundance could drive striking differences in carbon decomposition and soil organic matter formation across different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial 45 ecosystems has prevented the inclusion of soil fungal and bacterial relative abundance and the associated processes into global biogeochemical models. Here, we used a global scale dataset in the top soil surface (>3000 distinct observations of soil fungal and bacterial abundance) to generate the first quantitative and spatially high resolution (1km) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high latitude environments with large soil carbon stocks. There was strong non-linear response of fungal 50 dominance to environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi and bacteria dominated in regions with low and high MAT and NPP, respectively, thus representing slow vs. fast soil energy channels, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios","language":"English","publisher":"Earth System Science Data","doi":"10.5194/essd-14-4339-2022","usgsCitation":"Yu, K., Hoogen, J.V., Wang, Z., Averill, C., Routh, D., Smith, G.R., Drenovsky, R.E., Scow, K., Mo, F., Waldrop, M., Yang, Y., Tang, W., De Vries, F., Bardgett, R., Manning, P., Bastida, F., Baer, S.G., Bach, E., Garcia, C.J., Wang, Q., Ma, L., Chen, B., He, X., Teurlinex, S., Heijboer, A., Bradley, J.A., and Crowther, T.W., 2022, The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil: Earth System Science Data, v. 14, p. 4339-4350, https://doi.org/10.5194/essd-14-4339-2022.","productDescription":"12 p,","startPage":"4339","endPage":"4350","ipdsId":"IP-116714","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-4339-2022","text":"Publisher Index Page"},{"id":462936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2022-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Yu, Kailiang","contributorId":221398,"corporation":false,"usgs":false,"family":"Yu","given":"Kailiang","email":"","affiliations":[{"id":40362,"text":"Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA","active":true,"usgs":false}],"preferred":false,"id":915995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoogen, Johan van den","contributorId":345210,"corporation":false,"usgs":false,"family":"Hoogen","given":"Johan","email":"","middleInitial":"van den","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":915996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Zhiqiang","contributorId":345211,"corporation":false,"usgs":false,"family":"Wang","given":"Zhiqiang","email":"","affiliations":[{"id":82525,"text":"Chengdu University","active":true,"usgs":false}],"preferred":false,"id":915997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":915998,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Routh, Devin","contributorId":345212,"corporation":false,"usgs":false,"family":"Routh","given":"Devin","email":"","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":915999,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Gabriel Reuben","contributorId":345213,"corporation":false,"usgs":false,"family":"Smith","given":"Gabriel","email":"","middleInitial":"Reuben","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":916000,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drenovsky, Rebecca E.","contributorId":345214,"corporation":false,"usgs":false,"family":"Drenovsky","given":"Rebecca","email":"","middleInitial":"E.","affiliations":[{"id":27555,"text":"John Carroll University","active":true,"usgs":false}],"preferred":false,"id":916001,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Scow, Kate M.","contributorId":345215,"corporation":false,"usgs":false,"family":"Scow","given":"Kate M.","affiliations":[{"id":82527,"text":"U. California Davis","active":true,"usgs":false}],"preferred":false,"id":916002,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mo, Fei","contributorId":344978,"corporation":false,"usgs":false,"family":"Mo","given":"Fei","email":"","affiliations":[{"id":82451,"text":"College of Agronomy, Northwest A&F University, Yangling, Xianyang, Shaanxi, 712100, China;","active":true,"usgs":false}],"preferred":false,"id":916003,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216769,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916004,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yang, Yuanhe","contributorId":247646,"corporation":false,"usgs":false,"family":"Yang","given":"Yuanhe","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":916005,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tang, Weize","contributorId":345216,"corporation":false,"usgs":false,"family":"Tang","given":"Weize","email":"","affiliations":[],"preferred":false,"id":916006,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"De Vries, Franciska","contributorId":345217,"corporation":false,"usgs":false,"family":"De Vries","given":"Franciska","affiliations":[{"id":37958,"text":"University of Amsterdam","active":true,"usgs":false}],"preferred":false,"id":916007,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bardgett, Richard D.","contributorId":266148,"corporation":false,"usgs":false,"family":"Bardgett","given":"Richard D.","affiliations":[{"id":54928,"text":"School of Earth and Environmental Sciences, Michael Smith Building, The University of Manchester, Oxford Road, Manchester M13 9PT, UK","active":true,"usgs":false}],"preferred":false,"id":916008,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Manning, Peter","contributorId":345218,"corporation":false,"usgs":false,"family":"Manning","given":"Peter","email":"","affiliations":[],"preferred":false,"id":916009,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Bastida, Felipe","contributorId":240755,"corporation":false,"usgs":false,"family":"Bastida","given":"Felipe","email":"","affiliations":[],"preferred":false,"id":916010,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Baer, Sara G.","contributorId":189135,"corporation":false,"usgs":false,"family":"Baer","given":"Sara","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":916011,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Bach, Elizabeth","contributorId":345219,"corporation":false,"usgs":false,"family":"Bach","given":"Elizabeth","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":916012,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Garcia, Carlos J.","contributorId":342669,"corporation":false,"usgs":false,"family":"Garcia","given":"Carlos","email":"","middleInitial":"J.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":916013,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Wang, Qingkui","contributorId":345220,"corporation":false,"usgs":false,"family":"Wang","given":"Qingkui","email":"","affiliations":[{"id":82528,"text":"CAS Key Laboratory of Forest Ecology and Management, China","active":true,"usgs":false}],"preferred":false,"id":916014,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Ma, Linna","contributorId":345221,"corporation":false,"usgs":false,"family":"Ma","given":"Linna","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":916015,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Chen, Baodong","contributorId":345222,"corporation":false,"usgs":false,"family":"Chen","given":"Baodong","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":916016,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"He, Xianjing","contributorId":345223,"corporation":false,"usgs":false,"family":"He","given":"Xianjing","email":"","affiliations":[{"id":34946,"text":"Lanzhou University, China","active":true,"usgs":false}],"preferred":false,"id":916017,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Teurlinex, Sven","contributorId":345224,"corporation":false,"usgs":false,"family":"Teurlinex","given":"Sven","email":"","affiliations":[{"id":35358,"text":"Netherlands Institute of Ecology","active":true,"usgs":false}],"preferred":false,"id":916018,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Heijboer, Amber","contributorId":345225,"corporation":false,"usgs":false,"family":"Heijboer","given":"Amber","email":"","affiliations":[{"id":36528,"text":"Wageningen University & Research","active":true,"usgs":false}],"preferred":false,"id":916019,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Bradley, James A.","contributorId":345226,"corporation":false,"usgs":false,"family":"Bradley","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":35299,"text":"Queen Mary University of London","active":true,"usgs":false}],"preferred":false,"id":916020,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Crowther, Thomas W.","contributorId":177398,"corporation":false,"usgs":false,"family":"Crowther","given":"Thomas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":916021,"contributorType":{"id":1,"text":"Authors"},"rank":27}]}} ,{"id":70237240,"text":"70237240 - 2022 - Status of forest birds on Tinian Island, Commonwealth of the Northern Mariana Islands, with an emphasis on the Tinian monarch (Monarcha takatsukasae) (Passeriformes; Monarchidae)","interactions":[],"lastModifiedDate":"2022-10-05T11:51:45.655434","indexId":"70237240","displayToPublicDate":"2022-09-22T06:46:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2990,"text":"Pacific Science","active":true,"publicationSubtype":{"id":10}},"title":"Status of forest birds on Tinian Island, Commonwealth of the Northern Mariana Islands, with an emphasis on the Tinian monarch (Monarcha takatsukasae) (Passeriformes; Monarchidae)","docAbstract":"Landbird populations on Tinian Island have been periodically surveyed since 1982 to evaluate the status of non-native and native landbirds. We report the results of surveys in 2013 and the observed changes during 31 years in species population trends based on surveys since 1982. A total of 11 native and 3 non-native species were detected during the 2013 survey. Population sizes were estimated using point-transect distance sampling methods, and population trends were assessed using repeated measures analysis of variance for nine forest bird species. In all years, the Rufous Fantail (Rhipidura rufifrons) and Bridled White-eye (Zosterops conspicillatus) were the most abundant species, whereas the White-throated Ground Dove (Pampusana xanthonura) was the least abundant species in 1982, 1996, and 2008, and the Mariana Kingfisher (Todiramphus albicilla) was the least abundant in 2013. The less common species numbered in the low thousands included the Mariana Fruit Dove (Ptilinopus roseicapilla), White-throated Ground Dove, introduced Philippine Collared Dove (Streptopelia dusumieri), Mariana Kingfisher (Todiramphus albicilla), and Micronesian Myzomela (Myzomela rubratra). The Micronesian Starling (Aplonis opaca) and Tinian Monarch (Monarcha takatsukasae) were estimated to number in the tens of thousands. The most abundant species were the Rufous Fantail, numbering more than 100,000, and the Bridled White-eye, numbering more than 400,000. The overall trends in abundance between 1982 and 2013 showed an increase in the Mariana Kingfisher, Micronesian Starling, Rufous Fantail, White-throated Ground Dove, and Philippine Collared Dove, while populations were stable for the Bridled White-eye and Tinian Monarch. Declines were seen for the Mariana Fruit Dove and Micronesian Myzomela. These trends matched previous analyses with the exception that Tinian Monarch abundance showed an increase in the 2013 survey.
Around the globe, fish and wildlife managers are facing increasingly complex management issues because of multiscale ecological effects like climate change, species invasion, and land-use change. Managers seeking to prevent extinctions or preserve ecosystems are increasingly considering more interventionist techniques to overcome the resulting changes. Among those techniques, translocation methods that intentionally move species into new, less impacted habitats are being considered. These types of translocations are known by a range of terms, including “managed relocation” and “assisted migration,” but the International Union for the Conservation of Nature’s Species Survival Commission (IUCN SSC, 2013) has proposed “conservation introduction” (CI) as a standard term.
As defined by the IUCN SSC, CI is the intentional movement of a species or population outside its indigenous range for conservation purposes. CI can be divided into two forms: assisted colonization and ecological replacement. Assisted colonization is moving species outside its indigenous range to prevent extinction or extirpation of a population. Ecological replacement is moving species to fulfill an important niche that is necessary within an ecosystem. Proponents suggest these methods are necessary to address the ecological challenges managers are trying to overcome. Opponents point out the potential for species to become invasive, introduce disease or parasites, and cause other cascading impacts throughout the ecosystem. The result is controversy and disagreement. As such, it will be imperative to develop clear guidelines and best practices to be followed within wildlife management agencies to prevent potential
To this end, the U.S. Fish and Wildlife Service (USFWS) partnered with the U.S. Geological Survey (USGS) to develop the current project. The intent was to describe the perceptions of USFWS personnel across many aspects of CI so that the USFWS could use this information in the planning and development of their own internal decision-support framework for CI.
This report is presented in five sections. Section 1 introduces the project and provides an in-depth overview of background literature related to CI. Section 2 describes the study design, methods, and study participant characteristics. Section 3 describes key results and recommendations related to the development of a USFWS decision framework. Section 4 investigates a range of perceptions held by participants and establishes baseline information for how USFWS personnel may view CI and its application. Types of viewpoints surveyed include preferred terms and definitions, perceived barriers, perceived risks and tradeoffs, and aspects of success. Perceived barriers refers to factors that may prevent successful implementation of CI and perceived risks refers to potential negative outcomes that may occur as a result of implementing CI. Section 5 provides an overview of our conclusions for this project.
Overall, we found that CI is likely to be viewed positively within the USFWS, but employees offered cautions and caveats. Most participants we interviewed feel that it is a necessary tool that will be indispensable in certain situations but also feel that there is more risk associated than with more traditional methods. For this reason, many participants are concerned about the assessment and planning that should be conducted prior to any CI effort. Our results indicate that many USFWS personnel will be open to CI being adopted more regularly but will be looking for clear guidance on how it should be implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225092","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Cole, N., Goolsby, J.B., and Cravens, A.E., 2022, Perceptions of conservation introduction to inform decision support among U.S. Fish and Wildlife Service employees: U.S. Geological Survey Scientific Investigations Report 2022–5092, 22 p., https://doi.org/10.3133/sir20225092.","productDescription":"v, 22 p.","onlineOnly":"Y","ipdsId":"IP-133493","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":407145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5092/coverthb.jpg"},{"id":407146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5092/sir20225092.pdf","text":"Report","size":"880 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5092"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Urban forests have largely been overlooked for the role they play in reducing stormwater runoff volume by using hydrologic processes such as interception (rainfall intercepted by tree canopy), evapotranspiration (the transfer of water from vegetation into the atmosphere) and infiltration (percolation of rainwater into the Earth’s soil). Early research into the effects of trees on urban stormwater runoff used simple estimates based on assumptions of canopy coverage and design storm criteria. In a review of available literature on how capable urban trees are at reducing runoff, the Center for Watershed Protection (2017) found only six studies; three of them used measured data from a single plot, and the other three used models. When identifying gaps in research on the role of trees in stormwater management, Kuehler and others (2017) highlighted the need for studies that scale the local effects of urban trees to the larger sewershed catchment area, allowing a more holistic understanding of the urban tree canopy effects on hydrology.
For these reasons, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, U.S. Forest Service, and the University of Wisconsin, quantified the effect of removing urban street trees and their canopy on stormwater generation in a medium-density residential area. Using a paired-catchment experimental design, rainfall-runoff relations were characterized in two medium-density residential catchments in Fond du Lac, Wisconsin, during May through September in 2018–20. Results of the study are detailed in Selbig and others (2022).
During the calibration phase, hydrograph metrics from paired runoff events were used to develop the relation between the control and test catchments with street trees in place. The ability to measure changes to the rainfall-runoff response after removal of tree canopy was made possible by an aggressive tree removal program by the city as a response to rapid infestation from the Agrilus planipennis (emerald ash borer). In March 2020, a total of 31 street trees were removed at the onset of the treatment period, resulting in a loss of 2,990 square meters of canopy over streets, driveways, sidewalks, and grassed areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223074","usgsCitation":"Selbig, W.R., Loheide, S.P., II, Shuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R., and Nowak, D., 2022, Loss of street tree canopy increases stormwater runoff: U.S. Geological Survey Fact Sheet 2022–3074, 4 p., https://doi.org/10.3133/fs20223074.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141242","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407081,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.XML"},{"id":407082,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3074/images"},{"id":407157,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3074/coverthb.jpg"},{"id":407080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.pdf","text":"Report","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3074"},{"id":501510,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113526.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.494873046875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.72148995228582\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Barrier islands and their associated backbarrier environments protect mainland population centers and infrastructure from storm impacts, support biodiversity, and provide long-term carbon storage, among other ecosystem services. Despite their socio-economic and ecological importance, the response of coupled barrier-marsh-lagoon environments to sea-level rise is poorly understood. Undeveloped barrier-marsh-lagoon systems typically respond to sea-level rise through the process of landward migration, driven by storm overwash and landward mainland marsh expansion. Such response, however, can be affected by human development and engineering activities such as lagoon dredging and shoreline stabilization. To better understand the difference in the response between developed and undeveloped barrier-marsh-lagoon environments to sea-level rise, we perform a local morphologic analysis that describes the evolution of Long Beach Island (LBI), New Jersey, over the last 182 years. We find that between 1840 and 1934 the LBI system experienced landward migration of all five boundaries, including 171 meters of shoreline retreat. Between the 1920s and 1950s, however, there was a significant shift in system behavior that coincided with the onset of groin construction, which was enhanced by beach nourishment and lagoon dredging practices. From 1934 to 2022 the LBI system experienced ~22 meters of shoreline progradation and a rapid decline in marsh platform extent. Additionally, we extend a morphodynamic model to describe the evolution of the system in terms of five geomorphic boundaries: the ocean shoreline and backbarrier-marsh interface, the seaward and landward lagoon-marsh boundaries, and the landward limit of the inland marsh. We couple this numerical modeling effort with the map analysis during the undeveloped phase of LBI evolution, between 1840 and 1934. Despite its simplicity, the modeling framework can describe the average cross-shore evolution of the barrier-marsh-lagoon system during this period without accounting for human landscape modifications, supporting the premise that natural processes were the key drivers of morphological change. Overall, these results suggest that anthropogenic effects have played a major role in the evolution of LBI over the past century by altering overwash fluxes and marsh-lagoon geometry; this is likely the case for other barrier-marsh-lagoon environments around the world.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.958573","usgsCitation":"Tenebruso, C., Nichols-O’Neill, S., Lorenzo-Trueba, J., Ciarletta, D.J., and Miselis, J.L., 2022, Undeveloped and developed phases in the centennial evolution of a barrier-marsh-lagoon system: The case of Long Beach Island, New Jersey: Frontiers in Marine Science, v. 9, 958573, 15 p., https://doi.org/10.3389/fmars.2022.958573.","productDescription":"958573, 15 p.","ipdsId":"IP-141531","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.958573","text":"Publisher Index Page"},{"id":409230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Long Beach island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.07802920592502,\n 39.74949430937235\n ],\n [\n -74.11619642132992,\n 39.76972910720298\n ],\n [\n -74.32809027237006,\n 39.48488546642025\n ],\n [\n -74.27149750470127,\n 39.46863133845025\n ],\n [\n -74.07802920592502,\n 39.74949430937235\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Tenebruso, Christopher","contributorId":297141,"corporation":false,"usgs":false,"family":"Tenebruso","given":"Christopher","email":"","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":853509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nichols-O’Neill, Shane","contributorId":297142,"corporation":false,"usgs":false,"family":"Nichols-O’Neill","given":"Shane","email":"","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":853510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenzo-Trueba, Jorge","contributorId":297143,"corporation":false,"usgs":false,"family":"Lorenzo-Trueba","given":"Jorge","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":853511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853513,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237705,"text":"70237705 - 2022 - Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","interactions":[],"lastModifiedDate":"2022-10-31T14:56:21.921244","indexId":"70237705","displayToPublicDate":"2022-09-21T08:23:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","docAbstract":"Wildlife avoid human disturbances, including roads and development. Avoidance and displacement of wildlife into less suitable habitat due to human development can affect their energy expenditures and fitness. The heart rate and oxygen uptake of large mammals varies with both natural aspects of their habitat (terrain, climate, predators, etc.) and anthropogenic influence (noise, light, fragmentation, etc.). Although incorporating physiological analyses of energetics can inform the impacts of both development and conservation, management decisions rarely incorporate individuals’ energetic requirements when deciding on locations for potential development.
We aimed to estimate the change in expected energy expenditure, numerically and spatially, for mule deer to traverse a landscape with varying levels of oil and gas development through time.
Using calculations of energy expenditure of mule deer (Odocoileus hemionus) by weight, in relation to physical terrain components, plus avoidance factors for anthropogenic disturbance, we developed a spatiotemporal model of the minimum energy required for mule deer to traverse a landscape. We compared expected energy expenditure across 12 study sites with increasing levels of oil and gas development and over time in our study area, on the northern Colorado Plateau of Utah.
We found that energy expenditure can be increased by development, regardless of terrain, through increased travel distance associated with avoidance behavior. Maximum median energy expenditure to traverse a 1400 ha sample area rose from 1135 to 1935 kilocalories, a 70% increase in energy required of a mule deer. There was a significant relationship between energy expenditure and the size of oil and gas development (p < 0.001), its compactness (p < 0.05), and its ‘thinness’ (p < 0.001), but not terrain ruggedness (p = 0.25).
As the energy costs of movement correlate across multiple species of large mammals, our analysis of the energetic cost, for mule deer, associated with development can serve as a quantitative representative of the impacts of oil and gas development for multiple mammals—including threatened or endangered species. Our bioenergetic cost-distance model provides a means of delineating impediments to efficient movement and can be used to quantify the expected energetic costs of proposed future developments. As wildlife are exposed to increasing anthropogenic stressors which reduce fitness, it is important to make strategic siting decisions to reduce energetic costs imposed by human activities.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01521-w","usgsCitation":"Chambers, S.N., Villarreal, M.L., Duane, O.J., Munson, S.M., Stuber, E.F., Tyree, G., Waller, E.K., and Duniway, M.C., 2022, Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development: Landscape Ecology, v. 37, p. 2947-2961, https://doi.org/10.1007/s10980-022-01521-w.","productDescription":"15 p.","startPage":"2947","endPage":"2961","ipdsId":"IP-138879","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":435685,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99JGAYG","text":"USGS data release","linkHelpText":"Maps of mule deer avoidance areas based on density of oil and gas developments, Book Cliffs, Utah"},{"id":408538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"northern Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.753173828125,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 38.77978137804918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Samuel Norton 0000-0002-9840-7989","orcid":"https://orcid.org/0000-0002-9840-7989","contributorId":297110,"corporation":false,"usgs":true,"family":"Chambers","given":"Samuel","email":"","middleInitial":"Norton","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duane, Olivia Jane Marie","contributorId":298083,"corporation":false,"usgs":true,"family":"Duane","given":"Olivia","email":"","middleInitial":"Jane Marie","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":855078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stuber, Erica Francis 0000-0002-2687-6874","orcid":"https://orcid.org/0000-0002-2687-6874","contributorId":298084,"corporation":false,"usgs":true,"family":"Stuber","given":"Erica","email":"","middleInitial":"Francis","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tyree, Gayle L","contributorId":298085,"corporation":false,"usgs":false,"family":"Tyree","given":"Gayle L","affiliations":[{"id":64492,"text":"Plant and Environmental Sciences Department, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":855080,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waller, Eric K","contributorId":298087,"corporation":false,"usgs":false,"family":"Waller","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":64493,"text":"Independent USGS contractor","active":true,"usgs":false}],"preferred":false,"id":855081,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":855082,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238504,"text":"70238504 - 2022 - Postbreeding movements and molting ecology of female gadwalls and mallards","interactions":[],"lastModifiedDate":"2022-11-28T13:27:12.130738","indexId":"70238504","displayToPublicDate":"2022-09-21T07:23:27","publicationYear":"2022","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":"Postbreeding movements and molting ecology of female gadwalls and mallards","docAbstract":"The wing molt is an important annual life-history event that occurs in waterfowl and molt site selection can play an important role in determining survival. We tracked postbreeding movements of gadwall (Mareca strepera) and mallard (Anas platyrhynchos) females that bred in the Suisun Marsh (Suisun) of California, USA, to determine molt site selection and wing molt chronology. We attached backpack transmitters with global positioning system and global system for mobile communications (GPS-GSM) technology to female gadwalls and mallards within Suisun and tracked the birds following the breeding season during 2015–2018. We determined molt locations for 52 female gadwalls and 112 female mallards. Thirty of the marked gadwall females selected 2 regions within southern Oregon-northeastern California (SONEC) to undergo molt; 16 molted in the Upper Klamath Basin (southern OR) and 14 in the Lower Klamath Basin (northeastern CA). A large portion of female mallards molted in Suisun (n = 34) and the Sacramento Valley in California (n = 31) but also used the Upper Klamath Basin (n = 13), Lower Klamath Basin (n = 12), and the Yolo–Delta region in California (n = 12). On average, gadwalls departed Suisun on 30 July (±17.82 days [SD]), and mallards departed on 24 July (±22.69 days). Mean start date of molt for each species was similar: 27 August (±16.09 days) for gadwalls and 26 August (±21.03 days) for mallards. Molt end date was analogous for each species as well. Molt ended on average 1 October (±15.52 days) for gadwalls and on 5 October (±18.34 days) for mallards. Gadwalls and mallards showed intraspecific differences in average molt start and end date within the 3 main geographical zones: Suisun, Central Valley of California (Central Valley), and SONEC. Mean duration of wing molt for gadwalls was 34.72± 8.62 days and 41.09 ± 12.54 days for mallards. Both species primarily selected permanent marsh to undergo wing molt (gadwalls = 90.4%, mallards = 63.4%). Conservation and active management of these high-use molting areas used by California's primary breeding waterfowl species could enhance postbreeding survival, leading to increased breeding waterfowl populations.
A pipeline carrying unconventional oil and gas (OG) wastewater spilled approximately 11 million liters of wastewater into Blacktail Creek, North Dakota, USA. Flow of the mix of stream water and wastewater down the channel resulted in storage of contaminants in the hyporheic zone and along the banks, providing a long-term source of wastewater constituents to the stream. A multi-level investigation was used to assess the potential effects of oil and brine spills on aquatic life. In this study, we used a combination of experiments using a native fish species, Fathead Minnow (Pimephales promelas), field sampling of the microbial community structure, and measures of estrogenicity. The fish investigation included in situ experiments and experiments with collected site water. Estrogenicity was measured in collected site water samples, and microbial community analyses were conducted on collected sediments. During the initial post-spill investigation, February 2015, performing in situ fish bioassays was impossible because of ice conditions. However, microbial community (e.g., the presence of members of the Halomonadaceae, a family that is indicative of elevated salinity) and estrogenicity differences were compared to reference sites and point to early biological effects of the spill. We noted water column effects on in situ fish survival 6 months post-spill during June 2015. At that time, total dissolved ammonium (sum of ammonium and ammonia, TAN) was 4.41 mg NH4/L with an associated NH3 of 1.09 mg/L, a concentration greater than the water quality criteria established to protect aquatic life. Biological measurements in the sediment defined early and long-lasting effects of the spill on aquatic resources. The microbial community structure was affected during all sampling events. Therefore, sediment may act as a sink for constituents spilled and as such provide an indication of continued and cumulative effects post-spill. However, lack of later water column effects may reflect pulse hyporheic flow of ammonia from shallow ground water. Combining fish toxicological, microbial community structure and estrogenicity information provides a complete ecological investigation that defines potential influences of contaminants at organismal, population, and community levels. In general, in situ bioassays have implications for the individual survival and changes at the population level, microbial community structure defines potential changes at the community level, and estrogenicity measurements define changes at the individual and molecular level. By understanding effects at these various levels of biological organization, natural resource managers can interpret how a course of action, especially for remediation/restoration, might affect a larger group of organisms in the system. The current work also reviews potential effects of additional constituents defined during chemistry investigations on aquatic resources.
We develop an empirical, spatially continuous model for the single-station within-event (ϕSS) component of earthquake ground motion variability in the Los Angeles area. ϕSS represents event-to-event variability in site response or remaining variability due to path effects not captured by ground motion models. Site-specific values of ϕSS at permanent seismic network stations were estimated during our previous work [1]. Here we first fit a model to ϕSS conditioned on the time-averaged shear wave velocity in the upper 30 m (VS30). We observe that stations on soft soil have larger average variability in site response than those on rock, especially at short periods. We use regression kriging to spatially interpolate ϕSS to an even grid spacing, using the VS30-scaling as the background model [2]. This improves on previous work that used ordinary kriging for interpolation [1]. We find that ϕSS ranges from about 0.1 to 0.4 natural log units in our study area, representing variations in site response at single locations of a factor of 1.1 up to a factor of 1.5. There is both greater variability and more coherency in the variability for short-period site response than for long periods. We recommend using these ϕSS models with the site response models of [1] for applications where quantification of variability is needed, such as probabilistic seismic hazard analyses.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"Jun 27 - Jul 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Parker, G.A., Baltay Sundstrom, A.S., and Thompson, E.M., 2022, Spatially continuous models of aleatory variability in seismic site response for southern California, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, Jun 27 - Jul 1, 2022, 5 p.","productDescription":"5 p.","ipdsId":"IP-134370","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":411890,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411887,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://12ncee.org/program/proceedings"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117,\n 35\n ],\n [\n -119,\n 35\n ],\n [\n -119,\n 33\n ],\n [\n -117,\n 33\n ],\n [\n -117,\n 35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":848817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256634,"text":"70256634 - 2022 - Blood biochemistry and hematology of adult and chick brown pelicans in the northern Gulf of Mexico: Baseline health values and ecological relationships","interactions":[],"lastModifiedDate":"2024-08-27T16:47:11.717581","indexId":"70256634","displayToPublicDate":"2022-09-20T11:36:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Blood biochemistry and hematology of adult and chick brown pelicans in the northern Gulf of Mexico: Baseline health values and ecological relationships","docAbstract":"The northern Gulf of Mexico supports a diverse community of nearshore seabirds during both breeding and nonbreeding periods of the annual cycle and is also a highly industrialized marine ecosystem with substantial levels of oil and gas development particularly in the west and central regions. Stakeholders in the region often assess risk to species of interest based on these differing levels of development. We collected blood samples from 81 adult and 35 chick eastern brown pelicans (Pelecanus occidentalis carolinensis) from 10 colonies across the northern Gulf of Mexico and used these to establish baseline values for hematology and blood biochemistry. We assessed the potential influence of body condition, sex and home range size on hematology and blood biochemistry. We also assessed potential influences of oil and gas activity by considering differing levels of oil and gas development that occur regionally throughout the study area. Although blood analyte concentrations of adults and chicks were often associated with these regional differences, the pattern we observed was not entirely consistent with the differing levels of oil and gas activity across the Gulf, suggesting that regional levels of oil and gas activity around breeding sites may not be the primary drivers of hematology and blood biochemistry. We note that baseline values or reference intervals are not available for other nearshore seabirds that breed in the northern Gulf. Given that exposure and risk may differ among this suite of species based on diet, foraging strategies and life history strategies, similar assessments and monitoring may be warranted.
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Using the NGA-East dataset, we apply mixed-effects residual analysis and identify that period-dependent bias in 5% damped response spectral acceleration is present across a wide frequency range, but is strongest (i.e., overestimating by a factor of 2) at short oscillator periods <0.2 sec. Ongoing work to remedy this bias consists of expanding the NGA-East dataset with more recent recordings and enhanced metadata, particularly regarding site conditions.
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