Animal movement is a complex phenomenon where individual movement patterns can be influenced by a variety of factors including the animal’s current activity, available terrain and habitat, and locations of other animals. Motivated by modeling grizzly bear movement in the Greater Yellowstone Ecosystem, this article presents an agent-based model represented in a state-space framework for collective animal movement. The novel contribution of this work is a collective animal movement model that captures interactions between animals that can trigger changes in movement patterns, such as when a dominant grizzly bear may cause another subordinate bear to temporarily leave an area. The modeling framework enables learning different movement patterns through a state-space representation with particle-MCMC methods for fully Bayesian model fitting and the prediction of future animal movement behaviors.
Researchers most often focus on individual toxicants when identifying effective chemical control agents for aquatic invasive species; however, toxicant mixtures may elicit synergistic effects. Synergistic effects may decrease required concentrations and shorten exposure durations for treatments. We investigated four toxicants (EarthTec QZ, Clam-Trol CT-2, niclosamide, and potassium chloride) that have been considered to control invasive zebra mussels (Dreissena polymorpha Pallas, 1771). We determined the toxicity of binary mixtures for five different mixture ratios to adult mussels. We compared our observations to predictions made with concentration addition and independent action paradigms, as based on the dose-response relationships of each individual toxicant. We calculated the model deviation ratio for each combination at the LC50 and LC90 and identified three possible interactions: synergy, antagonism, and additivity. We found that mixtures of niclosamide and Clam-Trol CT-2 were the most synergistic while mixtures that included potassium chloride were largely additive to antagonistic. The use of synergistic combinations has potential to decrease the overall volume and concentration of individual toxicants required for dreissenid mussel treatments, thereby decreasing cost.
The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.
","language":"English","publisher":"University of California Press","doi":"10.1525/elementa.2020.00076","usgsCitation":"Pershing, A., Alexander, M.A., Brady, D., Brickman, D., Curchitser, E.N., Diamond, A.W., McClenachan, L., Mills, K., Nichols, O., Pendleton, D., Record, N., Scott, J., Staudinger, M., and Wang, Y., 2021, Climate impacts on the Gulf of Maine ecosystem: A review of observed and expected changes in 2050 from rising temperatures: Elementa: Science of the Anthropocene, v. 9, no. 1, 00076, 18 p., https://doi.org/10.1525/elementa.2020.00076.","productDescription":"00076, 18 p.","ipdsId":"IP-120338","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1525/elementa.2020.00076","text":"Publisher Index 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C.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":818161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brickman, David","contributorId":260603,"corporation":false,"usgs":false,"family":"Brickman","given":"David","email":"","affiliations":[{"id":52613,"text":"DFO","active":true,"usgs":false}],"preferred":false,"id":818162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Curchitser, Enrique N.","contributorId":260604,"corporation":false,"usgs":false,"family":"Curchitser","given":"Enrique","email":"","middleInitial":"N.","affiliations":[{"id":52614,"text":"Rutgers Unv.","active":true,"usgs":false}],"preferred":false,"id":818163,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diamond, Anthony W.","contributorId":260605,"corporation":false,"usgs":false,"family":"Diamond","given":"Anthony","email":"","middleInitial":"W.","affiliations":[{"id":18889,"text":"University of New 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0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":206655,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":818171,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wang, Yanjun","contributorId":260612,"corporation":false,"usgs":false,"family":"Wang","given":"Yanjun","email":"","affiliations":[{"id":52613,"text":"DFO","active":true,"usgs":false}],"preferred":false,"id":818172,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70222522,"text":"sir20215055 - 2021 - Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","interactions":[],"lastModifiedDate":"2021-08-05T09:52:41.030548","indexId":"sir20215055","displayToPublicDate":"2021-08-04T08:23:21","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5055","displayTitle":"Groundwater Quality and Age of Secondary Bedrock Aquifers in the Glaciated Portion of Eastern Nebraska, 2016–18","title":"Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18","docAbstract":"The Eastern Nebraska Water Resources Assessment (ENWRA) project was initiated in 2006 to assist water managers by developing a hydrogeologic framework and water budget for the glaciated portion of eastern Nebraska. Within the ENWRA area, the primary groundwater sources for municipal, domestic, and irrigation water needs are provided by withdrawals from alluvial, buried paleovalley, and the High Plains aquifer (where present). Generally, other bedrock aquifers are considered a secondary water source. However, in some areas, such as parts of Sarpy and Nemaha Counties, these secondary bedrock aquifers are the only source of water within glaciated upland areas. To improve the understanding of the quality, geochemistry, and age of groundwater from bedrock aquifers, the U.S. Geological Survey (USGS), in cooperation with the ENWRA group, which includes the Lewis and Clark, Lower Elkhorn, Lower Platte North, Lower Platte South, Nemaha, and Papio-Missouri River Natural Resources Districts, designed a study to sample 31 wells completed in the secondary bedrock aquifers and analyze samples for major ions, physical properties, nutrients, stable isotopes, and selected age tracers. Of the 31 samples collected for this report, 22 samples were collected from the Dakota aquifer contained in the Dakota Sandstone, 3 from the Niobrara aquifer contained in the Niobrara Formation of Colorado Group, and 6 from Paleozoic aquifers contained in undifferentiated Paleozoic-age units.
The results of this study indicate that major ion data collected from the Dakota aquifer can be used for assessing the quality, recharge source, and age of groundwater. Calcium bicarbonate dominant samples were characterized as modern or mixed, indicating that, in these areas, groundwater is unconfined and is recharged by precipitation and (or) surface water. If groundwater extraction rates exceed recharge rates, total dissolved solid concentrations may increase as a result of upwelling of groundwater from deeper units or formations, which can adversely affect groundwater quality. Sampling results presented in this report indicate water quality is good, but that groundwater in the Dakota aquifer with calcium bicarbonate water type may be vulnerable to surface contamination. In contrast, groundwater sampled from the Dakota aquifer, having a dominant water type other than calcium bicarbonate, generally has low dissolved oxygen and nitrate concentrations, and higher concentrations of total dissolved solids and trace elements, including iron and strontium. The geochemical characteristics of noncalcium bicarbonate samples from the Dakota aquifer indicated confining conditions and limited groundwater recharge from local precipitation. Apparent groundwater ages estimated from radiocarbon (carbon-14) sampling of noncalcium bicarbonate samples from the Dakota aquifer indicated that the time of groundwater recharge to the Dakota aquifer occurred during Pleistocene time. Depleted stable isotopes results indicate recharge during a colder climate. Groundwater under confined conditions is not easily recharged from precipitation or surface water. Future groundwater-level monitoring in locations where the Dakota aquifer appears to be confined could provide information to evaluate whether groundwater supplies remain sufficient to meet future municipal, domestic, and irrigation needs.
For the Niobrara aquifer and Paleozoic aquifers, the dominant water type was not a diagnostic indicator of recharge source, age, and groundwater quality as with the Dakota aquifer. Most likely this is because the host formation was dominated by calcium-carbonate-rich rocks; however, few samples were collected from these aquifers to be able to confirm this interpretation. Samples collected from wells completed in the Niobrara aquifer and Paleozoic aquifers and characterized as calcium sulfate water type have statistically significantly higher concentrations of total dissolved solids compared to other samples from the Niobrara aquifer and Paleozoic aquifers characterized as calcium bicarbonate. Given that six of the nine of samples collected from the Niobrara and Paleozoic aquifers indicated modern recharge, these secondary bedrock aquifers are reliant on precipitation to sustain groundwater levels and may be vulnerable to a multiyear drought. Well yields of the Niobrara and Paleozoic aquifers are dependent on the presence of secondary porosity and these units offer little storage. Samples collected from wells completed in Paleozoic aquifers were the most isotopically enriched and similar to modern precipitation and had the highest concentrations of nitrate, indicating that groundwater is affected by agricultural activities. Future groundwater sampling would be beneficial to characterize groundwater-quality changes within the Niobrara and Paleozoic aquifers over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215055","collaboration":"Prepared in cooperation with the Eastern Nebraska Water Resources Assessment","usgsCitation":"Hobza, C.M., and Flynn, A.T., 2021, Groundwater quality and age of secondary bedrock aquifers in the glaciated portion of eastern Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5055, 42 p., https://doi.org/10.3133/sir20215055.","productDescription":"Report: viii, 42 p.; Dataset","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-122775","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":387641,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":387643,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.XML","linkFileType":{"id":8,"text":"xml"}},{"id":387642,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5055/images"},{"id":387640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5055/sir20215055.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5055"},{"id":387639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5055/coverthb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.9541015625,\n 42.779275360241904\n ],\n [\n -97.7783203125,\n 42.261049162113856\n ],\n [\n -97.58056640625,\n 41.52502957323801\n ],\n [\n -97.05322265625,\n 41.07935114946899\n ],\n [\n -96.87744140625,\n 40.64730356252251\n ],\n [\n -96.591796875,\n 39.99395569397331\n ],\n [\n -95.2734375,\n 39.977120098439634\n ],\n [\n -95.49316406249999,\n 40.3130432088809\n ],\n [\n -95.80078125,\n 40.713955826286046\n ],\n [\n -95.91064453125,\n 41.31082388091818\n ],\n [\n -96.13037109375,\n 42.00032514831621\n ],\n [\n -96.5478515625,\n 42.65012181368022\n ],\n [\n -97.0751953125,\n 42.87596410238256\n ],\n [\n -97.822265625,\n 42.89206418807337\n ],\n [\n -97.9541015625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Inland aquatic systems, such as reservoirs, contribute substantially to global methane (CH4) emissions; yet are among the most uncertain components of the total CH4 budget. Reservoirs have received recent attention as they may generate high CH4 fluxes. Improved quantification of these CH4 fluxes, particularly their spatiotemporal distribution, is key to realistically incorporating them in CH4 modeling and budget studies. Here we report on a new global, gridded (0.25° lat × 0.25° lon) study of reservoir CH4 emissions, accounting for new knowledge regarding reservoir areal extent and distribution, and spatiotemporal emission patterns influenced by diurnal variability, temperature-dependent seasonality, satellite-derived freeze-thaw dynamics, and eco-climatic zone. The results of this new data set comprise daily CH4 emissions throughout the full annual cycle and show that reservoirs cover 297 × 103 km2 globally and emit 10.1 Tg CH4 yr−1 (1σ uncertainty range of 7.2–12.9 Tg CH4 yr−1) from diffusive (1.2 Tg CH4 yr−1) and ebullitive (8.9 Tg CH4 yr−1) emission pathways. This analysis of reservoir CH4 emission addresses multiple gaps and uncertainties in previous studies and represents an important contribution to studies of the global CH4 budget. The new data sets and methodologies from this study provide a framework to better understand and model the current and future role of reservoirs in the global CH4 budget and to guide efforts to mitigate reservoir-related CH4 emissions.
Eastern Bangladesh sits on the seismically active Chittagong-Myanmar fold and thrust belt (CMFB), a north-trending accretionary wedge on the eastern side of the India-Eurasia collision. Earthquakes on the basal décollement and associated thrusts within the CMFB present a hazard to this densely populated region. In this study, we interpret 28 seismic reflection profiles from both published and unpublished sources to constrain the depth of the basal décollement. To convert profiles from the time domain to the depth domain, we integrate sonic log and seismic stacking velocity data to generate time-velocity relationships for different parts of the CMFB. Our analysis reveals that the décollement is ∼9 km deep in northeast and southeast Bangladesh, but shallows to ∼5 km in east-central Bangladesh. The décollement has an area of 7.25 × 104 km2 (∼150 × 450 km), making it capable of an Mw 8.5 earthquake. However, the warped geometry of this fault might act as a rupture barrier were a large earthquake to occur on the décollement. Our combined velocity and fault model lay the groundwork for future studies to address seismic segmentation, ground shaking, and rupture modeling in the CMFB. Finally, we use our compiled data set to analyze the evolution of fold kinematics in the CMFB. We observe that folding style and failure mode varies, from mainly ductile deformation in the foreland to mainly brittle in the hinterland. The dual-failure modes within the CMFB support the hypothesis that a region with ductile deformation may still be capable of seismic behavior.
Moose (Alces alces) have experienced considerable declines along the periphery of their range in the northeastern United States. In Vermont, the population declined 45% from 2010 to 2017 despite minimal hunter harvest and adequate habitat. Similarly, nearby populations recently experienced epizootics characterized by >50% mortality. Declines have largely been associated with the effects of winter ticks (Dermacentor albipictus), but uncertainty exists about the effects of environmental and other parasite-related conditions on moose survival. We examined patterns of moose survival among a radio-collared population (n = 127) in Vermont from 2017 to 2019. Our objectives were to estimate causes of mortality and model survival probability as a function of individual and landscape variables for calves (<1 yr) and adults (≥1 yr). Observed adult survival was 90% in 2017, 84% in 2018, and 86% in 2019, and winter calf survival was 60% in 2017, 50% in 2018, and 37% in 2019. Winter tick infestation was the primary cause of mortality (91% of calves, 25% of adults), and 32% of all mortalities had evidence of meningeal worm (Parelaphostrongylus tenuis). Other sources of mortality such as vehicles, harvest, predation, deep snow, and other parasitic infections were negligible. The best supported calf model included sex differences and negative effects of tick engorgement (%/week) and parasite level (roundworm and lungworm). The best supported adult model included the effect of cumulative tick engorgement (cumulative %/week), which negatively affected survival. Our results indicate that winter tick engorgement strongly affects survival, and is probably compounded by the presence of meningeal worm and other parasites. Reduced tick effects may be achieved by decreasing moose density through harvest and managing late winter habitat to minimize tick density. Management of white-tailed deer (Odocoileus virginianus) density may also affect the transmission of meningeal worm.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22101","usgsCitation":"Debow, J., Blouin, J., Rosenblatt, E., Alexander, C., Gieder, K.D., Cottrell, W., Murdoch, J., and Donovan, T.M., 2021, Effects of winter ticks and internal parasites on moose survival in Vermont, USA: Journal of Wildlife Management, v. 85, no. 7, p. 1423-1439, https://doi.org/10.1002/jwmg.22101.","productDescription":"17 p.","startPage":"1423","endPage":"1439","ipdsId":"IP-117645","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451293,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22101","text":"Publisher Index Page"},{"id":395899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Vermont","active":true,"usgs":false}],"preferred":false,"id":834756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Cedric","contributorId":278589,"corporation":false,"usgs":false,"family":"Alexander","given":"Cedric","affiliations":[],"preferred":false,"id":834819,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gieder, Katherina D.","contributorId":34426,"corporation":false,"usgs":true,"family":"Gieder","given":"Katherina","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":834755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cottrell, Walter","contributorId":276326,"corporation":false,"usgs":false,"family":"Cottrell","given":"Walter","email":"","affiliations":[{"id":56957,"text":"Northeast Diseach Cooperative","active":true,"usgs":false}],"preferred":false,"id":834758,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murdoch, James","contributorId":276325,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":834757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834752,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222930,"text":"70222930 - 2021 - Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","interactions":[],"lastModifiedDate":"2021-10-18T14:23:58.805746","indexId":"70222930","displayToPublicDate":"2021-08-02T09:52:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","title":"Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics","docAbstract":"Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) is a long-lived, anadromous species that is broadly distributed along the Atlantic coast of North America. Historic overharvest and habitat degradation resulted in significant declines to Atlantic sturgeon populations and, following decades of limited recovery, the species was listed under the Endangered Species Act of the United States in 2012. Given continued threats to recovery and limited information about population demography, there is a need for new tools to assist in Atlantic sturgeon conservation. Here, we present a range-wide microsatellite genetic baseline for North American Atlantic sturgeon that is comprised of 2510 individuals from 18 genetically distinct groups collected in 13 rivers and one estuary. Analysis of this baseline suggested that populations from the northern range of Atlantic sturgeon were more highly differentiated than those from the southern extent, where patterns of differentiation were complicated by rivers with genetically distinct spring and fall spawning runs and less geographic distance separating populations. Despite significant demographic bottleneck events, all populations showed at least moderate levels of genetic diversity across a suite of metrics. Additionally, individual-based assignment tests had over 80% accuracy for assigning individuals to their river of origin, highlighting the utility of this baseline for characterizing the composition of mixed-stock aggregations and understanding stock-specific vulnerability and recovery. The expanded spatial coverage of this baseline dataset enabled novel inferences about patterns of genetic differentiation and spawning phenology in Atlantic sturgeon which can be used to support conservation and management efforts.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-021-01390-x","usgsCitation":"White, S.L., Kazyak, D., Darden, T.L., Farrae, D.J., Lubinski, B.A., Johnson, R.L., Eackles, M.S., Balazik, M., Brundage, H., Fox, A.G., Fox, D.A., Hager, C.H., Kahn, J.E., and Wirgin, I.I., 2021, Establishment of a microsatellite genetic baseline for North American Atlantic sturgeon (Acipenser o. oxyrhinchus) and range-wide analysis of population genetics: Conservation Genetics, v. 22, p. 977-992, https://doi.org/10.1007/s10592-021-01390-x.","productDescription":"16 p.","startPage":"977","endPage":"992","ipdsId":"IP-124759","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darden, Tanya L.","contributorId":263425,"corporation":false,"usgs":false,"family":"Darden","given":"Tanya","email":"","middleInitial":"L.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":820835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farrae, Daniel J.","contributorId":263426,"corporation":false,"usgs":false,"family":"Farrae","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":53977,"text":"SC DNR","active":true,"usgs":false}],"preferred":false,"id":820836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820837,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820838,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":820839,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Balazik, M","contributorId":263427,"corporation":false,"usgs":false,"family":"Balazik","given":"M","email":"","affiliations":[{"id":53978,"text":"VCU","active":true,"usgs":false}],"preferred":false,"id":820840,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brundage, Hal","contributorId":197215,"corporation":false,"usgs":false,"family":"Brundage","given":"Hal","email":"","affiliations":[],"preferred":false,"id":820841,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fox, Adam G","contributorId":263428,"corporation":false,"usgs":false,"family":"Fox","given":"Adam","email":"","middleInitial":"G","affiliations":[{"id":24699,"text":"UGA","active":true,"usgs":false}],"preferred":false,"id":820842,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":820843,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hager, Chris H","contributorId":263429,"corporation":false,"usgs":false,"family":"Hager","given":"Chris","email":"","middleInitial":"H","affiliations":[{"id":53979,"text":"Chesapeake Scientific","active":true,"usgs":false}],"preferred":false,"id":820844,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kahn, Jason E","contributorId":263430,"corporation":false,"usgs":false,"family":"Kahn","given":"Jason","email":"","middleInitial":"E","affiliations":[{"id":53980,"text":"NMFS","active":true,"usgs":false}],"preferred":false,"id":820845,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wirgin, Isaac I","contributorId":263431,"corporation":false,"usgs":false,"family":"Wirgin","given":"Isaac","email":"","middleInitial":"I","affiliations":[{"id":53981,"text":"NYU","active":true,"usgs":false}],"preferred":false,"id":820846,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70223134,"text":"70223134 - 2021 - Pore pressure threshold and fault slip potential for induced earthquakes in the Dallas-Fort Worth area of north central Texas","interactions":[],"lastModifiedDate":"2021-08-12T13:08:08.386264","indexId":"70223134","displayToPublicDate":"2021-08-02T08:06:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Pore pressure threshold and fault slip potential for induced earthquakes in the Dallas-Fort Worth area of north central Texas","docAbstract":"Earthquakes were induced in the Fort Worth Basin from 2008 through 2020 by increase in pore pressure from injection of oilfield wastewater (SWD). In this region and elsewhere, a missing link in understanding the mechanics of causation has been a lack of comprehensive models of pore pressure evolution (ΔPp) from SWD. We integrate detailed earthquake catalogs, ΔPp, and probabilistic fault slip potential (FSP) and find that faults near large-scale SWD operations became unstable early, when ΔPp reached ∼0.31 MPa and FSP reached 0.24. Faults farther from SWD became unstable later, when FSP reached 0.17 and at much smaller ΔPp. Earthquake sequences reactivated with mean ΔPp of ∼0.05 MPa. The response of faults shows strong variability, with many remaining stable at higher ΔPp and few that became seismogenic at smaller changes. As ΔPp spread regionally, an ever-increasing number of faults were impacted and the most sensitive became unstable.
Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatially explicit, processed-based hydrologic modeling framework implemented over the lake-rich landscape of the Northern Highlands Lake District within the United States with an ensemble of climate change scenarios for the 2050s (2041–2070) and 2080s (2071–2100). Model results quantify the effects of climate change on water budgets and lake stage elevations for 3692 lakes and highlight the importance of landscape and hydrologic setting for the response of specific lake types to climate change. All future climate projections resulted in loss of ice cover and snowpack as well as increased evaporation, but variability in climate projections (warmer conditions, wet winters combined with wet or dry summers) interacted with lake characteristics and landscape position to produce variable lake hydrologic changes. Water levels for drainage lakes (lakes with substantial surface water inflows and outflows) showed nearly no change, whereas minimum water levels for seepage lakes (minimal surface water fluxes) decreased by an average of up to 2.64 m by the end of the 21st century. Our physically based modeling approach is parsimonious and computationally efficient and can be applied to other lake-rich regions to investigate interregional variability in lake hydrologic response to future climate scenarios.
The portion of the Snake River fall Chinook salmon Oncorhynchus tshawytscha evolutionary significant unit (ESU) that spawns upstream of Lower Granite Dam transitioned from low to high abundance during 1992–2020 in response to U.S. Endangered Species Act recovery efforts and other federally mandated actions. This annual report focuses on changes in population abundance and habitat use by natural- and hatchery-origin spawners. Typically, we also report on population attributes of natural-origin juveniles, but data on juveniles were not collected in 2020 due to Covid-19. Spawners have located and used most of the available spawning habitat and that habitat is gradually approaching the point that no more redds can be supported. Timing of spawning and fry emergence have been relatively stable, but effects of density dependence are evident in juvenile life stages. Apparent abundance of juvenile fall Chinook salmon has increased and we noted the following changes: parr dispersal from riverine rearing habitat into Lower Granite Reservoir has become earlier; growth rate (g/d) and dispersal size of parr has declined; and passage timing of smolts from the two Snake River reaches has become earlier and downstream movement rate has increased. These findings coupled with stock-recruitment analyses presented in this report provide evidence for density-dependence in the Snake River reaches and in Lower Granite Reservoir resulting from the expansion of the recovery program. The long-term goal is to use this information in a comprehensive modeling effort to conduct action-effectiveness and uncertainty research and to inform Fish Population, Hydrosystem, Harvest, Hatchery, and Predation and Invasive Species Management Research, Monitoring, and Evaluation (RM&E) programs.
In 2020, the U.S. Geological Survey (USGS) focused survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use a boat-mounted underwater video camera to count 170 deepwater redds at 19 of the 28 sites surveyed. Redd depths averaged 4.2 m. We collected genetic samples from 297 live fall Chinook salmon and 16 carcasses at 44 unique geographic locations that spanned 89 river kilometers. Seventy-two fish were recovered at Eureka Bar (rkm 307.1) and Corral Creek (rkm 349.7), which accounted for 23% of all collected fish in 2020. Most (238 fish) post-spawned salmon were collected from early to mid-November just after peak spawning. A summary of 2019 PBT results can be found in Appendix A.1.
In 2020, we PIT tagged subyearling fall Chinook salmon in the Clearwater River to obtain population and growth data. In the Clearwater River, we tagged 2,192 subyearlings and recaptured 79 (3.6%) fish in the river and 187 fish (78 tagged by the U.S. Geological Survey, 109 tagged by the Nez Perce Tribe) at Lower Granite Dam during October which provided information for growth estimation. Within riverine habitats, growth in both length and mass were higher for fish tagged with 8-mm tags than with 9- and 12-mm tags. Estimated growth in length and mass of subyearlings was generally lower in Lower Granite Reservoir than in riverine habitats.
Information on prey resources and juvenile fall Chinook salmon prey consumption was collected to better understand the growth opportunity of late-migrating fish in Lower Granite Reservoir. Zooplankton and surface drifting prey were collected from three reservoir locations from July through October during 2019 and 2020. Fall Chinook salmon diet data were collected from angled fish using gastric lavage. Cladocera and Copepoda were the most abundant zooplankton taxa collected while Diptera was the most common invertebrate taxon collected in surface drift samples. Totals of 49 and 94 juvenile fall Chinook salmon were captured in 2019 and 2020, respectively, and most fish were caught in the lower reach of the reservoir in October of each year. Juvenile fall Chinook salmon consumed mainly dipterans in July and October 2019 and mainly Daphnia during August–October in 2020. Fish showed selection mainly for dipterans in 2019 but strong selection for Daphnia during October 2020. Stomach fullness values were relatively low (<1.1%) during both years. Results show that prey resources are adequate in Lower Granite Reservoir to support positive fish growth during late summer and early autumn.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Tiffan, K., Barry, P.H., Hance, D., Plumb, J., Bickford, B., Rhodes, T., King, K.G., Lebeda, D.D., Hemingway, R.J., and Hargrove, J., 2021, Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook salmon ESU, 89 p.","productDescription":"89 p.","ipdsId":"IP-132164","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":426905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396070,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Project.mvc/Display/1991-029-00"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": 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The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.","language":"English","publisher":"ACM","doi":"10.1145/3447814","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2021, Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles: ACM/IMS Transactions on Data Science, v. 2, no. 3, 20, 26 p., https://doi.org/10.1145/3447814.","productDescription":"20, 26 p.","ipdsId":"IP-114876","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451310,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1145/3447814","text":"Publisher Index Page"},{"id":408166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854279,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854280,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228321,"text":"70228321 - 2021 - Modeling at-sea density of marine birds to support renewable energy planning on the Pacific outer continental shelf of the contiguous United States","interactions":[],"lastModifiedDate":"2022-02-08T16:51:22.708275","indexId":"70228321","displayToPublicDate":"2021-08-01T10:43:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2021-014","title":"Modeling at-sea density of marine birds to support renewable energy planning on the Pacific outer continental shelf of the contiguous United States","docAbstract":"This report describes the at-sea spatial distributions of marine birds in Pacific OCS waters off the contiguous U.S. (Figure 1.1) to inform marine spatial planning in the region. The goal was to estimate long-term average spatial distributions for marine bird species using all available science-quality transect survey data and numerous bathymetric, oceanographic, and atmospheric predictor variables. We developed seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the study region. A statistical modeling framework was used to estimate numerical relationships between bird sighting data (i.e., standardized counts) and a range of temporal (e.g., Pacific Decadal Oscillation [PDO] index), spatially static (e.g., depth), and spatially dynamic (e.g., sea surface chlorophyll-a concentration) environmental variables. The estimated relationships were then used to predict spatially explicit long-term average density (individuals per km2) throughout the study area for each species/group in each of four seasons. Bird sighting data came from multiple scientific survey programs and consisted of at-sea counts of birds collected between 1980 and 2017 using boat-based and fixed-wing aerial transect survey methods. Spatial environmental variables were derived from remote sensing satellite data and an ocean dynamics model.
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We conducted simulations to compare and evaluate various passive acoustic sampling designs effectiveness for monitoring spotted owl (Strix occidentalis caurina) population trends. We found that each design was effective for detecting a decline (or stability) in spotted own populations within 10 years with even a moderate amount of sampling. There are however, important considerations and tradeoffs among the various design options. Often, estimated changes in use of the landscape were biased with a consistently lower magnitude of change compared to simulated changes in the population. Although this method has challenges, passive acoustic monitoring can be used to effectively monitor northern spotted owls in the Pacific Northwest.
","language":"English","publisher":"U.S. Forest Service","usgsCitation":"Lesmeister, D.B., Appel, C., Davis, R.J., Yackulic, C., and Ruff, Z., 2021, Simulating the effort necessary to detect changes in northern spotted owl (Strix occidentalis caurina) populations using passive acoustic monitoring: Research Paper PNW-RP-618, 55 p.","productDescription":"55 p.","ipdsId":"IP-119741","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388793,"type":{"id":15,"text":"Index Page"},"url":"https://www.fs.usda.gov/pnw/publications/simulating-effort-necessary-detect-changes-northern-spotted-owl-strix-occidentalis"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.76880048488168\n ],\n [\n -117.99316406249999,\n 36.38591277287651\n ],\n [\n -120.7177734375,\n 41.60722821271717\n ],\n [\n -118.740234375,\n 47.96050238891509\n ],\n [\n -119.267578125,\n 48.86471476180277\n ],\n [\n -123.00292968749999,\n 49.009050809382046\n ],\n [\n -123.53027343749999,\n 48.16608541901253\n ],\n [\n -124.67285156250001,\n 48.3416461723746\n ],\n [\n -124.1455078125,\n 45.30580259943578\n ],\n [\n -124.27734374999999,\n 42.391008609205045\n ],\n [\n -123.70605468750001,\n 39.232253141714885\n ],\n [\n -121.33300781249999,\n 35.817813158696616\n ],\n [\n -120.498046875,\n 34.45221847282654\n ],\n [\n -117.158203125,\n 32.76880048488168\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lesmeister, Damon B. 0000-0003-1102-0122","orcid":"https://orcid.org/0000-0003-1102-0122","contributorId":205006,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon","email":"","middleInitial":"B.","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":822477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Appel, Cara L.","contributorId":265255,"corporation":false,"usgs":false,"family":"Appel","given":"Cara L.","affiliations":[{"id":54636,"text":"Graduate Research Assistant, USDA Forest Service, Pacific Northwest Research Station and Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR.","active":true,"usgs":false}],"preferred":false,"id":822478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Raymond J.","contributorId":150574,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruff, Zachary J.","contributorId":265260,"corporation":false,"usgs":false,"family":"Ruff","given":"Zachary J.","affiliations":[],"preferred":false,"id":822481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227960,"text":"70227960 - 2021 - Using growth rates to estimate the minimum age and size at sexual maturity in a captive population of the critically endangered Central American river turtle Dermatemys mawii","interactions":[],"lastModifiedDate":"2022-02-02T15:03:31.212647","indexId":"70227960","displayToPublicDate":"2021-07-31T08:42:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10077,"text":"Journal of Zoo and Aquarium Research","onlineIssn":"2214-7594","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Using growth rates to estimate the minimum age and size at sexual maturity in a captive population of the critically endangered Central American river turtle Dermatemys mawii","title":"Using growth rates to estimate the minimum age and size at sexual maturity in a captive population of the critically endangered Central American river turtle Dermatemys mawii","docAbstract":"The Central American river turtle Dermatemys mawii is a critically endangered species that has incurred substantial losses over the last several decades due to overhunting. This species is now being considered for head-starting programs (i.e. captive breeding of turtles for wild release). However, relatively little is known about their life history characteristics, especially with respect to growth and sexual maturation. A robust knowledge of D. mawii life history traits is important in developing conservation management plans. Our research is the first known study to maintain hatchlings, juveniles, and adults in captivity with regular morphometric data collection. We quantified growth rates (cm yr-1) and calculated growth parameters (e.g. growth coefficients) to estimate body size and age at onset of sexual maturity in a group of wild-caught but captive-held and captive-bred D. mawii in Belize. Sizes at the onset of sexual maturity were inferred by segmented linear regressions that identified changes in growth rate by body size. Asymptotic sizes and growth coefficients were calculated using the Fabens method and the Wang method. Parameters from these models were then applied to a modified von Bertalanffy growth equation to estimate age at the onset of sexual maturity. Male and female D. mawii begin sexual maturation at ca. 38.0 cm and 40.0 cm straight-line carapace length, respectively. We estimated ages associated with these sizes at 13.5-16.9 yrs (males) and 13.6-17.3 yrs (females). No previous literature on growth rates or age at maturation for wild or captive D. mawii has been reported, so our results serve as a starting point in conservation management. Given the life history trait of delayed sexual maturity (>10 years), D. mawii may be more sensitive to losses of the adult population. Therefore, the importance of captive breeding and head-starting programs may be concomitant with protecting wild, adult populations.
","language":"English","publisher":"European Association of Zoos and Aquaria","doi":"10.19227/jzar.v9i3.432","usgsCitation":"Bishop, N.D., Hudson, R., Marlin, J., Pop, T., Rainwater, T.R., Boylan, S.M., Atkinson, B.K., and Carthy, R., 2021, Using growth rates to estimate the minimum age and size at sexual maturity in a captive population of the critically endangered Central American river turtle Dermatemys mawii: Journal of Zoo and Aquarium Research, v. 9, no. 3, p. 150-156, https://doi.org/10.19227/jzar.v9i3.432.","productDescription":"7 p.","startPage":"150","endPage":"156","ipdsId":"IP-094451","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Belize","county":"Toledo","otherGeospatial":"Belize Foundation for Research and Environmental Education, Hicatee Conservation Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.74309539794922,\n 16.444975888014174\n ],\n [\n -88.57486724853516,\n 16.444975888014174\n ],\n [\n -88.57486724853516,\n 16.59506848241128\n ],\n [\n -88.74309539794922,\n 16.59506848241128\n ],\n [\n -88.74309539794922,\n 16.444975888014174\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bishop, Nichole D.","contributorId":273246,"corporation":false,"usgs":false,"family":"Bishop","given":"Nichole","email":"","middleInitial":"D.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":832713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson, Rick","contributorId":212739,"corporation":false,"usgs":false,"family":"Hudson","given":"Rick","affiliations":[],"preferred":false,"id":832714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marlin, Jacob","contributorId":273247,"corporation":false,"usgs":false,"family":"Marlin","given":"Jacob","email":"","affiliations":[{"id":56441,"text":"Belize Foundation for Research and Environmental Education","active":true,"usgs":false}],"preferred":false,"id":832715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pop, Thomas","contributorId":273248,"corporation":false,"usgs":false,"family":"Pop","given":"Thomas","email":"","affiliations":[{"id":56441,"text":"Belize Foundation for Research and Environmental Education","active":true,"usgs":false}],"preferred":false,"id":832716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rainwater, Thomas R.","contributorId":93791,"corporation":false,"usgs":true,"family":"Rainwater","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":832717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boylan, Shane M.","contributorId":220012,"corporation":false,"usgs":false,"family":"Boylan","given":"Shane","email":"","middleInitial":"M.","affiliations":[{"id":40118,"text":"Clearwater Marine Aquarium, Clearwater, FL, USA","active":true,"usgs":false}],"preferred":false,"id":832718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Atkinson, Benjamin K.","contributorId":273250,"corporation":false,"usgs":false,"family":"Atkinson","given":"Benjamin","email":"","middleInitial":"K.","affiliations":[{"id":56443,"text":"Flagler College","active":true,"usgs":false}],"preferred":false,"id":832719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832720,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224636,"text":"70224636 - 2021 - Physiological consequences of consuming low-energy foods: Herbivory coincides with a stress response in Yellowstone bears.","interactions":[],"lastModifiedDate":"2021-10-01T13:11:41.766995","indexId":"70224636","displayToPublicDate":"2021-07-31T08:06:51","publicationYear":"2021","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":"Physiological consequences of consuming low-energy foods: Herbivory coincides with a stress response in Yellowstone bears.","docAbstract":"Meat, fruit, seeds and other high-energy bear foods are often highly localized and briefly available and understanding which factors influence bear consumption of these foods is a common focus of bear conservation and ecology. However, the most common bear foods, graminoids and forbs, are more widespread but of lower quality. We poorly understand how herbage consumption impacts bear physiology, such as endocrine system function that regulates homeostasis and stress responses. Here, we described bear diets with a novel approach, measuring the concentration of chlorophyll in bear scats (faecal chlorophyll) to index the proportion of the recent diet that was composed of leaves from graminoids and forbs. We measured faecal chlorophyll and faecal cortisol in 351 grizzly (Ursus arctos, n = 255) and black bear (Ursus americanus, n = 96) scats from Yellowstone National Park in 2008–2009. We compared models of faecal chlorophyll and faecal cortisol concentrations considering the effects of spatial, dietary, scat and bear-specific factors including species. Faecal chlorophyll levels were the strongest predictor of faecal cortisol in a manner that suggested an endocrine response to a low-energy diet. Both compounds were highest during the spring and early summer months, overlapping the breeding season when higher energy foods were less available. Effects of scat composition, scat weathering, bear age, bear sex, species and other factors that have previously been shown to influence faecal cortisol in bears were not important unless faecal chlorophyll was excluded from models. The top models of faecal chlorophyll suggested grazing was primarily influenced by spatial attributes, with greater grazing closer to recreational trails, implying that elevated cortisol with grazing could be a response to anthropogenic activity. Our results confirm that higher stress hormone concentrations correspond with lower quality diets in bears, particularly grazing, and that faecal chlorophyll shows promise as a metric for studying grazing behaviour and its consequences.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coab029","usgsCitation":"Christianson, D.A., Coleman, T., Doan, Q., and Haroldson, M.A., 2021, Physiological consequences of consuming low-energy foods: Herbivory coincides with a stress response in Yellowstone bears.: Conservation Physiology, v. 9, no. 1, coab029, 12 p., https://doi.org/10.1093/conphys/coab029.","productDescription":"coab029, 12 p.","ipdsId":"IP-126197","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":451319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab029","text":"Publisher Index Page"},{"id":390108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.005859375,\n 42.79540065303723\n ],\n [\n -108.74267578125,\n 42.79540065303723\n ],\n [\n -108.74267578125,\n 44.99588261816546\n ],\n [\n -111.005859375,\n 44.99588261816546\n ],\n [\n -111.005859375,\n 42.79540065303723\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Christianson, David A","contributorId":145749,"corporation":false,"usgs":false,"family":"Christianson","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16223,"text":"Institute of the Environment, University of Arizona, 325 Biological Sciences East, Tucson, AZ 85721 USA","active":true,"usgs":false}],"preferred":false,"id":824469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coleman, Tyler H","contributorId":266163,"corporation":false,"usgs":false,"family":"Coleman","given":"Tyler H","affiliations":[{"id":54935,"text":"Sequoia-Kings National Park, National Park Service","active":true,"usgs":false}],"preferred":false,"id":824470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doan, Quint","contributorId":266164,"corporation":false,"usgs":false,"family":"Doan","given":"Quint","email":"","affiliations":[{"id":54936,"text":"School of Forestry and Environmental Studies, Yale University","active":true,"usgs":false}],"preferred":false,"id":824471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":824472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223187,"text":"70223187 - 2021 - Multiple in-stream stressors degrade biological assemblages in five U.S. regions","interactions":[],"lastModifiedDate":"2022-04-28T14:19:10.572434","indexId":"70223187","displayToPublicDate":"2021-07-31T07:46:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Multiple in-stream stressors degrade biological assemblages in five U.S. regions","docAbstract":"Biological assemblages in streams are affected by a wide variety of physical and chemical stressors associated with land-use development, yet the importance of combinations of different types of stressors is not well known. From 2013 to 2017, the U.S. Geological Survey completed multi-stressor/multi-assemblage stream ecological assessments in five regions of the United States (434 streams total). Diatom, invertebrate, and fish communities were enumerated, and five types of potential stressors were quantified: habitat disturbance, excess nutrients, high flows, basic water quality, and contaminants in water and sediment. Boosted regression tree (BRT) models for each biological assemblage and region generally included variables from all five stressor types and multiple stressors types in each model was the norm. Classification and regression tree (CART) models then were used to determine thresholds for each BRT model variable above which there appeared to be adverse effects (multi-metric index (MMI) models only). In every region and assemblage there was a significant inverse relation between the MMI and the number of stressors exerting potentially adverse effects. The number of elevated instream stressors often varied substantially for a given level of land-use development and the number of elevated stressors was a better predictor of biological condition than was development. Using the adverse effects-levels that were developed based on the BRT model results, 68% of the streams had two or more stressors with potentially adverse effects and 35% had four or more. Our results indicate that relatively small increases in the number of stressors of different types can have a large effect on a stream ecosystem.
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tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825589,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825590,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":825591,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825592,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825593,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70230579,"text":"70230579 - 2021 - Establishing conservation units to promote recovery of two threatened freshwater mussel species (Bivalvia: Unionida: Potamilus)","interactions":[],"lastModifiedDate":"2022-04-18T11:51:52.930376","indexId":"70230579","displayToPublicDate":"2021-07-31T06:50:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Establishing conservation units to promote recovery of two threatened freshwater mussel species (Bivalvia: Unionida: Potamilus)","docAbstract":"Population genomics has significantly increased our ability to make inferences about microevolutionary processes and demographic histories, which have the potential to improve protection and recovery of imperiled species. Freshwater mussels (Bivalvia: Unionida) represent one of the most imperiled groups of organisms globally. Despite systemic decline of mussel abundance and diversity, studies evaluating spatiotemporal changes in distribution, demographic histories, and ecological factors that threaten long-term persistence of imperiled species remain lacking. In this study, we use genotype-by-sequencing (GBS) and mitochondrial sequence data (mtDNA) to define conservation units (CUs) for two highly imperiled freshwater mussel species, Potamilus amphichaenus and Potamilus streckersoni. We then synthesize our molecular findings with details from field collections spanning from 1901 to 2019 to further elucidate distributional trends, contemporary status, and other factors that may be contributing to population declines for our focal species. We collected GBS and mtDNA data for individuals of P. amphichaenus and P. streckersoni from freshwater mussel collections in the Brazos, Neches, Sabine, and Trinity drainages ranging from 2012 to 2019. Molecular analyses resolved disputing number of genetic clusters within P. amphichaenus and P. streckersoni; however, we find defensible support for four CUs, each corresponding to an independent river basin. Evaluations of historical and recent occurrence data illuminated a generally increasing trend of occurrence in each of the four CUs, which were correlated with recent increases in sampling effort. Taken together, these findings suggest that P. amphichaenus and P. streckersoni are likely rare throughout their respective ranges. Because of this, the establishment of CUs will facilitate evidence-based recovery planning and ensure potential captive propagation and translocation efforts are beneficial. Our synthesis represents a case study for conservation genomic assessments in freshwater mussels and provides a model for future studies aimed at recovery planning for these highly imperiled organisms.
Environmental conditions on Earth are repeated in non-random patterns that often coincide with species from different regions and time periods having consistent combinations of morphological, physiological and behavioral traits. Observation of repeated trait combinations among species confronting similar environmental conditions suggest that adaptive trait combinations are constrained by functional tradeoffs within or across niche dimensions. In an earlier study, we assembled a high-resolution database of functional traits for 134 lizard species to explore ecological diversification in relation to five fundamental niche dimensions. Here we expand and further examine multivariate relationships in that dataset to assess the relative influence of niche dimensions on the distribution of species in 6-dimensional niche space and how these may deviate from distributions generated from null models. We then analyzed a dataset with lower functional-trait resolution for 1023 lizard species that was compiled from our dataset and a published database, representing most of the extant families and environmental conditions occupied by lizards globally. Ordinations from multivariate analysis were compared with null models to assess how ecological and historical factors have resulted in the conservation, divergence or convergence of lizard niches.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s12862-021-01877-8","usgsCitation":"Pelegrin, N., Winemiller, K.O., Vitt, L.J., Fitzgerald, D.B., and Pianka, E.R., 2021, How do lizard niches conserve, diverge or converge? Further exploration of saurian evolutionary ecology: BMC Ecology and Evolution, v. 21, 149, 13 p., https://doi.org/10.1186/s12862-021-01877-8.","productDescription":"149, 13 p.","ipdsId":"IP-118097","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12862-021-01877-8","text":"Publisher Index Page"},{"id":388723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","noUsgsAuthors":false,"publicationDate":"2021-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Pelegrin, Nicolas","contributorId":265133,"corporation":false,"usgs":false,"family":"Pelegrin","given":"Nicolas","email":"","affiliations":[{"id":36900,"text":"Universidad Nacional de Córdoba","active":true,"usgs":false}],"preferred":false,"id":822291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winemiller, Kirk O.","contributorId":265134,"corporation":false,"usgs":false,"family":"Winemiller","given":"Kirk","email":"","middleInitial":"O.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":822292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vitt, Laurie J.","contributorId":147516,"corporation":false,"usgs":false,"family":"Vitt","given":"Laurie","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fitzgerald, Daniel Bruce 0000-0002-3254-7428","orcid":"https://orcid.org/0000-0002-3254-7428","contributorId":245718,"corporation":false,"usgs":true,"family":"Fitzgerald","given":"Daniel","email":"","middleInitial":"Bruce","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":822294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pianka, Eric R.","contributorId":265135,"corporation":false,"usgs":false,"family":"Pianka","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":822295,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223685,"text":"70223685 - 2021 - Multiple climate change-driven tipping points for coastal systems","interactions":[],"lastModifiedDate":"2021-09-01T12:43:11.933949","indexId":"70223685","displayToPublicDate":"2021-07-30T07:40:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8955,"text":"Nature--Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Multiple climate change-driven tipping points for coastal systems","docAbstract":"As the climate evolves over the next century, the interaction of accelerating sea level rise (SLR) and storms, combined with confining development and infrastructure, will place greater stresses on physical, ecological, and human systems along the ocean-land margin. Many of these valued coastal systems could reach “tipping points,” at which hazard exposure substantially increases and threatens the present-day form, function, and viability of communities, infrastructure, and ecosystems. Determining the timing and nature of these tipping points is essential for effective climate adaptation planning. Here we present a multidisciplinary case study from Santa Barbara, California (USA), to identify potential climate change-related tipping points for various coastal systems. This study integrates numerical and statistical models of the climate, ocean water levels, beach and cliff evolution, and two soft sediment ecosystems, sandy beaches and tidal wetlands. We find that tipping points for beaches and wetlands could be reached with just 0.25 m or less of SLR (~ 2050), with > 50% subsequent habitat loss that would degrade overall biodiversity and ecosystem function. In contrast, the largest projected changes in socioeconomic exposure to flooding for five communities in this region are not anticipated until SLR exceeds 0.75 m for daily flooding and 1.5 m for storm-driven flooding (~ 2100 or later). These changes are less acute relative to community totals and do not qualify as tipping points given the adaptive capacity of communities. Nonetheless, the natural and human built systems are interconnected such that the loss of natural system function could negatively impact the quality of life of residents and disrupt the local economy, resulting in indirect socioeconomic impacts long before built infrastructure is directly impacted by flooding.
Light is a primary constraint on primary production and drives many ecological processes in stream ecosystems, yet light regimes have received considerably less attention than other factors of the stream environment, such as hydrology or nutrient cycling. Light received by streams can be highly heterogeneous in both space and time resulting from changes in topography, channel characteristics, and riparian vegetation. Both the structure and phenology of riparian vegetation can be important determinants of the seasonality and magnitude of light reaching the stream surface, particularly in smaller forested streams. Despite the importance of riparian phenology on temporal patterns of stream light availability, existing models do not account for the seasonal dynamics of canopies. We developed a dynamic, biophysically based model (StreamLight) that incorporates canopy structure and phenology to predict light reaching the stream surface. We compared StreamLight to an existing model at 21 sites across the USA and found that, across sites, our biophysically based model produced light estimates that were more strongly correlated to observations and reduced the magnitude of errors in comparison to the existing model, particularly for streams that were relatively narrow compared to the height of riparian vegetation. Because smaller streams represent most global stream length, we expect that, in many smaller forested streams, the inclusion of canopy structure and phenology will enhance our ability to predict light regimes. We also used model simulations to examine the importance of controls on stream light environments and found that channel width was the strongest control on light environments. StreamLight represents an important incremental step forward in developing mechanistic models of river network productivity and in linking shifts in terrestrial vegetation structure and phenology to aquatic ecosystem productivity and thermal regimes.
Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
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2021 - Highly pathogenic avian influenza virus H5N2 (Clade 2.3.4.4) challenge of mallards age appropriate to the 2015 midwestern poultry outbreak","interactions":[],"lastModifiedDate":"2021-11-01T15:43:22.406864","indexId":"70222931","displayToPublicDate":"2021-07-29T09:47:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1990,"text":"Influenza and Other Respiratory Viruses","active":true,"publicationSubtype":{"id":10}},"title":"Highly pathogenic avian influenza virus H5N2 (Clade 2.3.4.4) challenge of mallards age appropriate to the 2015 midwestern poultry outbreak","docAbstract":"The 2015 highly pathogenic avian influenza virus (HPAIV) H5N2 clade 2.3.4.4 outbreak in upper midwestern U.S. poultry operations was not detected in wild birds to any great degree during the outbreak, despite wild waterfowl being implicated in the introduction, reassortment, and movement of the virus into North America from Asia. This outbreak led to the demise of over 50 million domestic birds and occurred mainly during the northward spring migration of adult avian populations.
There have been no experimental examinations of the pathogenesis, transmission, and population impacts of this virus in adult wild waterfowl with varying exposure histories—the most relevant age class.
We captured, housed, and challenged adult wild mallards (Anas platyrhynchos) with HPAIV H5N2 clade 2.3.4.4 and measured viral infection, viral excretion, and transmission to other mallards.
All inoculated birds became infected and excreted moderate amounts of virus, primarily orally, for up to 14 days. Cohoused, uninoculated birds also all became infected. Serological status had no effect on susceptibility. There were no obvious clinical signs of disease, and all birds survived to the end of the study (14 days).
Based on these results, adult mallards are viable hosts of HPAIV H5N2 regardless of prior exposure history and are capable of transporting the virus over short and long distances. These findings have implications for surveillance efforts. The capture and sampling of wild waterfowl in the spring, when most surveillance programs are not operating, are important to consider in the design of future HPAIV surveillance programs.
","language":"English","publisher":"Wiley","doi":"10.1111/irv.12886","usgsCitation":"Hall, J.S., Grear, D.A., Krauss, S., Seiler, P., Dusek, R.J., Nashold, S., and Webster, R., 2021, Highly pathogenic avian influenza virus H5N2 (Clade 2.3.4.4) challenge of mallards age appropriate to the 2015 midwestern poultry outbreak: Influenza and Other Respiratory Viruses, v. 15, no. 6, p. 767-777, https://doi.org/10.1111/irv.12886.","productDescription":"11 p.","startPage":"767","endPage":"777","ipdsId":"IP-126988","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":451359,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/irv.12886","text":"External Repository"},{"id":387814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Horicon 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 -88.70361328125,\n 43.52465500687185\n ],\n [\n -88.5779571533203,\n 43.526148603236294\n ],\n [\n -88.56834411621094,\n 43.542077996722796\n ],\n [\n -88.6102294921875,\n 43.59133291164543\n ],\n [\n -88.60061645507812,\n 43.628620426937886\n ],\n [\n -88.62876892089844,\n 43.65197548731187\n ],\n [\n -88.66584777832031,\n 43.64949133795468\n ],\n [\n -88.69194030761719,\n 43.625141236940564\n ],\n [\n -88.70361328125,\n 43.58635949637695\n ],\n [\n -88.69194030761719,\n 43.557007981627656\n ],\n [\n -88.68438720703125,\n 43.531624806844356\n ],\n [\n -88.70361328125,\n 43.52465500687185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Jeffrey S. 0000-0001-5599-2826 jshall@usgs.gov","orcid":"https://orcid.org/0000-0001-5599-2826","contributorId":2254,"corporation":false,"usgs":true,"family":"Hall","given":"Jeffrey","email":"jshall@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":820847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":820848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Scott","contributorId":190854,"corporation":false,"usgs":false,"family":"Krauss","given":"Scott","email":"","affiliations":[],"preferred":false,"id":820849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seiler, Patrick","contributorId":263433,"corporation":false,"usgs":false,"family":"Seiler","given":"Patrick","email":"","affiliations":[{"id":53983,"text":"St. Jude Children’s Research Hospital, Memphis, Tennessee","active":true,"usgs":false}],"preferred":false,"id":820850,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":820851,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nashold, Sean 0000-0002-8869-6633","orcid":"https://orcid.org/0000-0002-8869-6633","contributorId":214978,"corporation":false,"usgs":true,"family":"Nashold","given":"Sean","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":820852,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Webster, Robert G.","contributorId":263434,"corporation":false,"usgs":false,"family":"Webster","given":"Robert G.","affiliations":[{"id":53983,"text":"St. Jude Children’s Research Hospital, Memphis, Tennessee","active":true,"usgs":false}],"preferred":false,"id":820853,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236722,"text":"70236722 - 2021 - Late Holocene slip rate of the Mojave section of the San Andreas Fault near Palmdale, California","interactions":[],"lastModifiedDate":"2022-09-16T12:28:22.314471","indexId":"70236722","displayToPublicDate":"2021-07-29T07:25:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Late Holocene slip rate of the Mojave section of the San Andreas Fault near Palmdale, California","docAbstract":"The geologic slip rate on the Mojave section of the San Andreas fault is poorly constrained, despite its importance for understanding earthquake hazard, apparent discrepancies between geologic and geodetic slip rates along this fault section, and long‐term fault interactions in southern California. Here, we use surficial geologic mapping, excavations, and radiocarbon and luminescence dating to quantify the displacements and ages of late Holocene landforms offset by the fault at three sites. At the Ranch Center site, the slip rate is determined using the base of a fan marking incision and deflection of an ephemeral channel. At the adjacent Key Slide site, the margin of a landslide deposited on indigenous fire hearths provides a minimum rate. At the X‐12 site, the slip rate is determined from a channel that incised into a broad fan surface, and is deflected and beheaded by the fault. We use maximum–minimum bounds on both the displacement and age of each offset feature to calculate slip rate for each site independently. Overlap of the three independent rate ranges yields a rate of 33–39 mm/yr over the last 3 ka, under the assumption that the sites share a common history, given their proximity. Considered in sequence, site‐level epistemic uncertainties in the data permit but do not require a rate increase since ∼1200 cal B.P. Modest rate changes can be explained by aleatory variability in earthquake timing and magnitude; larger changes could suggest a shared regional variation with the Garlock and other faults. The new late Holocene slip rates are consistent with geodetic model estimates that include a viscoelastic crust and earthquake cycle effects. The geologic slip rates also provide average slip over dozens of earthquake cycles—a key constraint for long‐term earthquake rupture forecasts.
The Everglades vulnerability analysis (EVA) is a project led by the U.S. Geological Survey in cooperation with the National Park Service and U.S. Army Corps of Engineers to accomplish one of the science goals of Restoration Coordination & Verification (RECOVER), a multiagency group responsible for providing scientific and technical evaluations and assessments for improving the ability of the Comprehensive Everglades Restoration Plan to restore, preserve, and protect the south Florida ecosystem while providing for the region’s other water-related needs. In 2016, RECOVER acknowledged the need for a tool that could synthesize the decades of Everglades ecosystem science and identify areas vulnerable to changing conditions on the landscape. The EVA tool answers this need through a landscape-scale modeling framework that provides annual responses and relative vulnerability for a suite of indicators of Everglades ecosystem health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213033","collaboration":"Prepared in cooperation with the National Park Service and U.S. Army Corps of Engineers","usgsCitation":"D’Acunto, L.E., Romañach, S.S., Haider, S.M., Hackett, C.E., Nestler, J.H., Shinde, D., and Pearlstine, L.G., 2021, The Everglades vulnerability analysis—Integrating ecological models and addressing uncertainty: U.S. Geological Survey Fact Sheet 2021–3033, 4 p., https://doi.org/10.3133/fs20213033.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-127682","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":387501,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3033/coverthb.jpg"},{"id":387502,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3033/fs20213033.pdf","text":"Report","size":"1.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3033"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.859130859375,\n 25.90864446329127\n ],\n [\n -81.49658203125,\n 25.224820176765036\n ],\n [\n -80.88134765625,\n 24.956180020055925\n ],\n [\n -80.2880859375,\n 25.005972656239187\n ],\n [\n -79.815673828125,\n 26.578702269100557\n ],\n [\n -81.968994140625,\n 26.578702269100557\n ],\n [\n -81.859130859375,\n 25.90864446329127\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.694580078125,\n 25.839449402063185\n ],\n [\n -80.68359375,\n 25.839449402063185\n ],\n [\n -80.68359375,\n 25.859223554761407\n ],\n [\n -80.694580078125,\n 25.859223554761407\n ],\n [\n -80.694580078125,\n 25.839449402063185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st St.
Gainesville, FL 32653