Nutrients that have gradually accumulated in soils, groundwaters, and river sediments in the United States over the past century can remobilize and increase current downstream loading, obscuring effects of conservation practices aimed at protecting water resources. Drivers of storage accumulation and release of nutrients are poorly understood at the spatial scale of basins to watersheds. Predicting water quality outcomes in large river basins demands modeling storage lags and time varying reactivity that models of mean conditions typically cannot elucidate. We developed a seasonally dynamic approach to large-scale nutrient modeling based on a multiscale framework and nutrient storage lags were quantified for the nearly 190 000 small catchments that feed the rivers across the northeastern United States where catchment mean transit times were found to be around 4.7 (2–10) years for nitrogen and 1.3 (0.7–2) years for phosphorus. Nutrient loads carried in river flow in the current season contained a significant—and sometimes dominant—portion of mass lagged in its release from catchment storage repositories. Our approach of integrating storage releases with seasonally dynamic hydroclimatic drivers sets the stage to assess the accumulated effects of nutrient storage and lagged releases to the river interacting with seasonally varying nutrient reactivity and societal management actions throughout large river basins.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac1af4","usgsCitation":"Schmadel, N., Harvey, J., and Schwarz, G.E., 2021, Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins: Environmental Research Letters, v. 16, no. 9, 095004, 11 p., https://doi.org/10.1088/1748-9326/ac1af4.","productDescription":"095004, 11 p.","ipdsId":"IP-126236","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac1af4","text":"Publisher Index Page"},{"id":436229,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NRFWOV","text":"USGS data release","linkHelpText":"Mean seasonal SPARROW model inputs and simulated nitrogen and phosphorus loads for the Northeastern United States 2002 base year"},{"id":388586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822011,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224580,"text":"70224580 - 2021 - Nonlinear shifts in infectious rust disease due to climate change","interactions":[],"lastModifiedDate":"2021-09-29T13:39:18.489479","indexId":"70224580","displayToPublicDate":"2021-08-24T08:35:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Nonlinear shifts in infectious rust disease due to climate change","docAbstract":"Range shifts of infectious plant disease are expected under climate change. As plant diseases move, emergent abiotic-biotic interactions are predicted to modify their distributions, leading to unexpected changes in disease risk. Evidence of these complex range shifts due to climate change, however, remains largely speculative. Here, we combine a long-term study of the infectious tree disease, white pine blister rust, with a six-year field assessment of drought-disease interactions in the southern Sierra Nevada. We find that climate change between 1996 and 2016 moved the climate optimum of the disease into higher elevations. The nonlinear climate change-disease relationship contributed to an estimated 5.5 (4.4–6.6) percentage points (p.p.) decline in disease prevalence in arid regions and an estimated 6.8 (5.8–7.9) p.p. increase in colder regions. Though climate change likely expanded the suitable area for blister rust by 777.9 (1.0–1392.9) km2 into previously inhospitable regions, the combination of host-pathogen and drought-disease interactions contributed to a substantial decrease (32.79%) in mean disease prevalence between surveys. Specifically, declining alternate host abundance suppressed infection probabilities at high elevations, even as climatic conditions became more suitable. Further, drought-disease interactions varied in strength and direction across an aridity gradient—likely decreasing infection risk at low elevations while simultaneously increasing infection risk at high elevations. These results highlight the critical role of aridity in modifying host-pathogen-drought interactions. Variation in aridity across topographic gradients can strongly mediate plant disease range shifts in response to climate change.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41467-021-25182-6","usgsCitation":"Dudney, J., Willing, C., Das, A., Latimer, A.M., Nesmith, J.C., and Battles, J.J., 2021, Nonlinear shifts in infectious rust disease due to climate change: Nature Communications, v. 12, 5102, 13 p., https://doi.org/10.1038/s41467-021-25182-6.","productDescription":"5102, 13 p.","ipdsId":"IP-122388","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451079,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-25182-6","text":"Publisher Index Page"},{"id":389950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.30352783203125,\n 36.69485094156225\n ],\n [\n -118.41888427734374,\n 37.03325468997236\n ],\n [\n -118.69903564453124,\n 37.21939331752986\n ],\n [\n -118.828125,\n 37.23907530202184\n ],\n [\n -118.85559082031249,\n 37.19533058280065\n ],\n [\n -118.75946044921874,\n 37.07271048132943\n ],\n [\n -118.80615234374999,\n 36.95208671786997\n ],\n [\n -118.91601562499999,\n 36.91915611148194\n ],\n [\n -118.89953613281249,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 36.78949107451841\n ],\n [\n -118.98193359375,\n 36.756490329505176\n ],\n [\n -118.970947265625,\n 36.619936625629215\n ],\n [\n -118.91601562499999,\n 36.59127365634205\n ],\n [\n -118.89404296875,\n 36.49859745028132\n ],\n [\n -118.75946044921874,\n 36.47209813242351\n ],\n [\n -118.76495361328125,\n 36.32176422120382\n ],\n [\n -118.53424072265625,\n 36.29741818650811\n ],\n [\n -118.57818603515625,\n 36.405810193726765\n ],\n [\n -118.46282958984374,\n 36.319551259461186\n ],\n [\n -118.2403564453125,\n 36.48314061639213\n ],\n [\n -118.30352783203125,\n 36.69485094156225\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dudney, Joan","contributorId":223741,"corporation":false,"usgs":false,"family":"Dudney","given":"Joan","affiliations":[{"id":40762,"text":"University of California, Berkley","active":true,"usgs":false}],"preferred":false,"id":824155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willing, Claire","contributorId":266029,"corporation":false,"usgs":false,"family":"Willing","given":"Claire","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":824156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Latimer, Andrew M.","contributorId":229043,"corporation":false,"usgs":false,"family":"Latimer","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":41559,"text":"Department of Plant Sciences, University of California Davis, One Shields Ave., Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":824158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nesmith, Jonathan C B","contributorId":245216,"corporation":false,"usgs":false,"family":"Nesmith","given":"Jonathan","email":"","middleInitial":"C B","affiliations":[{"id":49124,"text":"National Park Service, Sierra Nevada Network Inventory & Monitoring Program","active":true,"usgs":false}],"preferred":false,"id":824159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":824160,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225709,"text":"70225709 - 2021 - The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland","interactions":[],"lastModifiedDate":"2021-11-04T13:41:17.401004","indexId":"70225709","displayToPublicDate":"2021-08-24T08:25:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland","docAbstract":"The northern sector of the Greenland Ice Sheet is considered to be particularly susceptible to ice mass loss arising from increased glacier discharge in the coming decades. However, the past extent and dynamics of outlet glaciers in this region, and hence their vulnerability to climate change, are poorly documented. In the summer of 2019, the Swedish icebreaker Oden entered the previously unchartered waters of Sherard Osborn Fjord, where Ryder Glacier drains approximately 2 % of Greenland's ice sheet into the Lincoln Sea. Here we reconstruct the Holocene dynamics of Ryder Glacier and its ice tongue by combining radiocarbon dating with sedimentary facies analyses along a 45 km transect of marine sediment cores collected between the modern ice tongue margin and the mouth of the fjord. The results illustrate that Ryder Glacier retreated from a grounded position at the fjord mouth during the Early Holocene (> 10.7±0.4 ka cal BP) and receded more than 120 km to the end of Sherard Osborn Fjord by the Middle Holocene (6.3±0.3 ka cal BP), likely becoming completely land-based. A re-advance of Ryder Glacier occurred in the Late Holocene, becoming marine-based around 3.9±0.4 ka cal BP. An ice tongue, similar in extent to its current position was established in the Late Holocene (between 3.6±0.4 and 2.9±0.4 ka cal BP) and extended to its maximum historical position near the fjord mouth around 0.9±0.3 ka cal BP. Laminated, clast-poor sediments were deposited during the entire retreat and regrowth phases, suggesting the persistence of an ice tongue that only collapsed when the glacier retreated behind a prominent topographic high at the landward end of the fjord. Sherard Osborn Fjord narrows inland, is constrained by steep-sided cliffs, contains a number of bathymetric pinning points that also shield the modern ice tongue and grounding zone from warm Atlantic waters, and has a shallowing inland sub-ice topography. These features are conducive to glacier stability and can explain the persistence of Ryder's ice tongue while the glacier remained marine-based. However, the physiography of the fjord did not halt the dramatic retreat of Ryder Glacier under the relatively mild changes in climate forcing during the Holocene. Presently, Ryder Glacier is grounded more than 40 km seaward of its inferred position during the Middle Holocene, highlighting the potential for substantial retreat in response to ongoing climate change.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-15-4073-2021","usgsCitation":"O’Regan, M., Cronin, T.M., Reilly, B., Olsen Alstrup, A.K., Gemery, L., Golub, A., Mayer, L.A., Morlighem, M., Moros, M., Munk, O.L., Nilsson, J., Pearce, C., Detlef, H., Stranne, C., Vermassen, F., West, G., and Jakobsson, M., 2021, The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland: The Cryosphere, v. 15, p. 4073-4097, https://doi.org/10.5194/tc-15-4073-2021.","productDescription":"25 p.","startPage":"4073","endPage":"4097","ipdsId":"IP-127375","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":451081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-15-4073-2021","text":"Publisher Index Page"},{"id":391381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","otherGeospatial":"Ryder Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63.6328125,\n 80.17871349622823\n ],\n [\n -32.34375,\n 80.17871349622823\n ],\n [\n -32.34375,\n 83.57940370073115\n ],\n [\n -63.6328125,\n 83.57940370073115\n ],\n [\n -63.6328125,\n 80.17871349622823\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Regan, Matt","contributorId":197135,"corporation":false,"usgs":false,"family":"O’Regan","given":"Matt","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":826358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":826359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reilly, Brendan","contributorId":258076,"corporation":false,"usgs":false,"family":"Reilly","given":"Brendan","email":"","affiliations":[],"preferred":false,"id":826360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olsen Alstrup, Aage K.","contributorId":268312,"corporation":false,"usgs":false,"family":"Olsen Alstrup","given":"Aage","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":826361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gemery, Laura 0000-0003-1966-8732","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":245413,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":826362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golub, Anna","contributorId":268313,"corporation":false,"usgs":false,"family":"Golub","given":"Anna","email":"","affiliations":[],"preferred":false,"id":826363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayer, Larry A.","contributorId":69583,"corporation":false,"usgs":true,"family":"Mayer","given":"Larry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":826364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morlighem, Mathieu","contributorId":141050,"corporation":false,"usgs":false,"family":"Morlighem","given":"Mathieu","email":"","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":826365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moros, Matthias","contributorId":268314,"corporation":false,"usgs":false,"family":"Moros","given":"Matthias","email":"","affiliations":[],"preferred":false,"id":826366,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Munk, Ole L.","contributorId":268315,"corporation":false,"usgs":false,"family":"Munk","given":"Ole","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":826367,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nilsson, Johan","contributorId":166855,"corporation":false,"usgs":false,"family":"Nilsson","given":"Johan","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826368,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":826369,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Detlef, Henrieka","contributorId":268316,"corporation":false,"usgs":false,"family":"Detlef","given":"Henrieka","email":"","affiliations":[],"preferred":false,"id":826370,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stranne, Christian","contributorId":166862,"corporation":false,"usgs":false,"family":"Stranne","given":"Christian","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826371,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Vermassen, Flor","contributorId":268317,"corporation":false,"usgs":false,"family":"Vermassen","given":"Flor","email":"","affiliations":[],"preferred":false,"id":826372,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"West, Gabriel","contributorId":258085,"corporation":false,"usgs":false,"family":"West","given":"Gabriel","email":"","affiliations":[],"preferred":false,"id":826373,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Jakobsson, Martin","contributorId":166854,"corporation":false,"usgs":false,"family":"Jakobsson","given":"Martin","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826374,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70224330,"text":"70224330 - 2021 - Thyroid disruption and oxidative stress in American kestrels following embryonic exposure to the alternative flame retardants, EHTBB and TBPH","interactions":[],"lastModifiedDate":"2021-09-23T12:50:23.708822","indexId":"70224330","displayToPublicDate":"2021-08-24T07:47:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Thyroid disruption and oxidative stress in American kestrels following embryonic exposure to the alternative flame retardants, EHTBB and TBPH","docAbstract":"Brominated flame retardant chemicals, such as 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EHTBB) (CAS #: 183658–27-7) and bis(2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH) (CAS #: 26040–51-7), have been detected in avian tissues and eggs from remote regions. Exposure to EHTBB and TBPH has been shown to cause oxidative stress and altered thyroid function in rodents and fish, yet no controlled studies have examined potential adverse effects of exposure in birds. Because flame retardants have been detected in wild raptors, we used American kestrels (Falco sparverius) as a model raptor to determine whether in ovo exposure to EHTBB or TBPH affected growth, hatching success, oxidative stress, or thyroid function. We exposed kestrel embryos to nominal concentrations (10, 50, or 100 ng g−1 egg weight) of EHTBB and TBPH via egg-injection on embryonic day 5. Embryonic exposure (~23 d) to EHTBB increased thyroid gland mass, reduced glandular colloid and total thyroxine (T4) in hatchling males and females, whereas deiodinase enzyme activity increased in males but decreased in females. Hatchlings exposed to TBPH in ovo exhibited reduced colloid and increased oxidative stress. Although exposure to EHTBB and TBPH caused several physiological effects (e.g., heart and brain mass), only exposure to 50 ng g−1 EHTBB appeared to reduce hatching success. Our results suggest these flame retardants may be hazardous for predatory birds. Future research should evaluate long-term survival and fitness consequences in birds exposed to these chemicals.
Reliable and long-term hindcast data of water levels are essential in quantifying return period and values of extreme water levels. In order to inform design decisions on a local flood control district level, process-based numerical modeling has proven an essential tool to provide the needed temporal and spatial coverage for different extreme value analysis methods. To determine the importance of different physical processes to the extreme water levels we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to simulate a large, urban, high-energy coastal estuary (the San Francisco Bay). The unstructured grid with 1D/2DH model elements, allows for efficient model simulations and therefore it was possible to simulate over 70 years between 1950 and 2019. Results show significant skill in reproducing observations for the entire modeled time period with an average root-mean-square error of 8.0 cm. A process-based modeling approach allows for the explicit in- and exclusion of different physical processes to quantify their importance to the extremes. For the 100-year still water level (SWL), tide (70%) and non-tidal residual (NTR) (25%) explain the majority of the simulated high water levels in the Bay relative to Mean Higher High Water (MHHW). However, closer to the Delta, local fluvial inflow increases in importance. For longer return periods, the importance of tide decreases and the importance of remote NTRs and fluvial inflow increases.
A random variable is a function that assigns a value in a sample space to an element of an arbitrary set (James 1992; Pawlowsky-Glahn et al. 2015). It is a model for a random experiment: the arbitrary set is an abstraction of the experimental conditions, the values taken by the random variable are in the sample space, and the function itself models the assignment of outcomes, thus also describing its frequency of appearance. In simpler terms, for the purpose of this presentation, a random variable is a function that assigns to each of the outcomes of a random experiment a value with a certain probability. A random variable also goes by stochastic variable and aleatory variable. Random variables are usually annotated as Roman capital letters, such as X or Y.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Mathematical Geosciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-26050-7_429-1","usgsCitation":"Olea, R., 2021, Random variable, chap. of Encyclopedia of Mathematical Geosciences, HTML Document, https://doi.org/10.1007/978-3-030-26050-7_429-1.","productDescription":"HTML Document","ipdsId":"IP-124122","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":391978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":224285,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":827121,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230324,"text":"70230324 - 2021 - A ground motion model for GNSS peak ground displacement","interactions":[],"lastModifiedDate":"2022-04-07T12:22:56.814703","indexId":"70230324","displayToPublicDate":"2021-08-24T07:18:34","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":"A ground motion model for GNSS peak ground displacement","docAbstract":"We present an updated ground‐motion model (GMM) for
Despite its importance for forest regeneration, food webs, and human economies, changes in tree fecundity with tree size and age remain largely unknown. The allometric increase with tree diameter assumed in ecological models would substantially overestimate seed contributions from large trees if fecundity eventually declines with size. Current estimates are dominated by overrepresentation of small trees in regression models. We combined global fecundity data, including a substantial representation of large trees. We compared size–fecundity relationships against traditional allometric scaling with diameter and two models based on crown architecture. All allometric models fail to describe the declining rate of increase in fecundity with diameter found for 80% of 597 species in our analysis. The strong evidence of declining fecundity, beyond what can be explained by crown architectural change, is consistent with physiological decline. A downward revision of projected fecundity of large trees can improve the next generation of forest dynamic models.
Conducting managed species translocations and establishing climate change refugia are adaptation strategies to cope with projected consequences of global warming, but successful implementation requires on-the-ground validation of demographic responses to transient climate conditions. Here we estimated the effect of nine abiotic and biotic factors on local occupancy and an index of abundance (few or chorus) for four amphibian species (Eleutherodactylus wightmanae, E. brittoni, E. antillensis, and E. coqui) in Puerto Rico, USA. We also assessed how the same factors influenced reproductive activity of E. coqui and how species responded to hurricane María (20 September 2017). As predicted, occupancy and abundance of E. wightmanae, E. brittoni and E. coqui were positively and strongly influenced by abiotic covariates (e.g., relative humidity) that characterize high elevation, mesic habitats. E. antillensis exhibited the opposite pattern, with highest probabilities (≥0.6) recorded at ≤300 m and with average relative humidity<75%. Biotic covariates (e.g., canopy cover) had a weak influence on both parameters, regardless of species. High probabilities (≥0.9) of detecting an E. coqui chorus and active nests occurred at sites experiencing average relative humidity of>80% and temperature of ≤26 °C. Moderate to high probabilities of detecting a chorus (0.4–0.7) were recorded at sites with average temperatures>26 °C, but no reproductive activity was detected, implying that monitoring abundance alone could misrepresent the capacity of a local population to sustain itself. The possibility underscores the importance of understanding the interplay between local demographic and environmental parameters in the advent of global warming to help guide monitoring and management decisions, especially for high elevation specialists. Hurricanes can inflict marked reductions in population numbers, but impacts vary by location and species. We found that the abundance (chorus) of E. antillensis and E. brittoni increased after the hurricane, but the abundance of the other two species did not differ between years. Lack of impacts was probably mediated by low structural damage to forest tracts (e.g., 9% canopy loss). Our findings help assess habitat suitability in terms of parameters that foster local population growth, which provides a basis for testing spatio-temporal predictions about demographic rates in potential climate refugia and for designing criteria to help guide managed translocations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01624","usgsCitation":"Rivera-Burgos, A., Collazo, J.A., Terando, A., and Pacifici, K., 2021, Linking demographic rates to local environmental conditions: Empirical data to support climate adaptation strategies for Eleutherodactylus frogs: Global Ecology and Conservation, v. 28, e01624,16 p., https://doi.org/10.1016/j.gecco.2021.e01624.","productDescription":"e01624,16 p.","ipdsId":"IP-119108","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01624","text":"Publisher Index Page"},{"id":396461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.3277,18.295843],[-65.337451,18.308308],[-65.327318,18.323666],[-65.342068,18.34529],[-65.335701,18.349535],[-65.329334,18.341955],[-65.321754,18.338316],[-65.309833,18.337973],[-65.304409,18.332054],[-65.298328,18.330529],[-65.255933,18.342117],[-65.221568,18.320959],[-65.222853,18.310464],[-65.249857,18.296691],[-65.260282,18.290823],[-65.283269,18.280214],[-65.3277,18.295843]]],[[[-67.89174,18.11397],[-67.887099,18.112574],[-67.87643,18.114157],[-67.869804,18.118851],[-67.861548,18.122144],[-67.848245,18.10832],[-67.843202,18.094858],[-67.843615,18.085099],[-67.845293,18.081938],[-67.853098,18.078195],[-67.865598,18.06544],[-67.871462,18.0578],[-67.895921,18.052342],[-67.904431,18.05913],[-67.918778,18.063116],[-67.927841,18.068572],[-67.940799,18.079716],[-67.934479,18.111306],[-67.932185,18.113221],[-67.91088,18.119668],[-67.89174,18.11397]]],[[[-65.308717,18.145172],[-65.302295,18.141089],[-65.294896,18.14283],[-65.287962,18.148097],[-65.275165,18.13443],[-65.276214,18.131936],[-65.283248,18.132999],[-65.296036,18.12799],[-65.322794,18.126589],[-65.327184,18.124106],[-65.338506,18.112439],[-65.342037,18.11138],[-65.350493,18.111914],[-65.364733,18.120377],[-65.397837,18.110873],[-65.399791,18.108832],[-65.411767,18.106211],[-65.423765,18.097764],[-65.426311,18.093749],[-65.45138,18.086096],[-65.45681,18.087778],[-65.465849,18.087715],[-65.468768,18.092643],[-65.47979,18.096352],[-65.507265,18.091646],[-65.524209,18.081977],[-65.542087,18.081177],[-65.558646,18.08566],[-65.569305,18.091616],[-65.570628,18.097325],[-65.57686,18.103224],[-65.575579,18.115669],[-65.546199,18.119329],[-65.511712,18.13284],[-65.489829,18.135912],[-65.46791,18.143767],[-65.437058,18.15766],[-65.399517,18.161935],[-65.371373,18.157517],[-65.334289,18.147761],[-65.313476,18.144296],[-65.308717,18.145172]]],[[[-66.438813,18.485713],[-66.420921,18.488639],[-66.410344,18.489886],[-66.394287,18.489748],[-66.377286,18.488044],[-66.37282,18.487726],[-66.349647,18.486335],[-66.337728,18.48562],[-66.315477,18.474724],[-66.31503,18.47468],[-66.291225,18.472347],[-66.283675,18.472203],[-66.276599,18.478129],[-66.269799,18.480281],[-66.258015,18.476906],[-66.251547,18.472464],[-66.241797,18.46874],[-66.220148,18.466],[-66.199032,18.466163],[-66.192664,18.466212],[-66.183886,18.460506],[-66.179218,18.455305],[-66.172315,18.451462],[-66.159796,18.451706],[-66.153037,18.454457],[-66.14395,18.459761],[-66.139572,18.462317],[-66.139451,18.462387],[-66.139443,18.462315],[-66.138532,18.453305],[-66.133085,18.445881],[-66.127938,18.444632],[-66.125198,18.451209],[-66.124284,18.456324],[-66.123188,18.45943],[-66.123343,18.460363],[-66.125015,18.470435],[-66.118338,18.469581],[-66.092098,18.466535],[-66.083254,18.462022],[-66.073987,18.4581],[-66.043272,18.453655],[-66.03944,18.454441],[-66.036559,18.450216],[-66.036491,18.450117],[-66.023221,18.443875],[-66.006523,18.444347],[-65.99718,18.449895],[-65.992935,18.457489],[-65.992793,18.458102],[-65.992349,18.460024],[-65.99079,18.460419],[-65.958492,18.451354],[-65.92567,18.444881],[-65.916843,18.444619],[-65.907756,18.446893],[-65.904988,18.450926],[-65.878683,18.438322],[-65.838825,18.431865],[-65.831476,18.426849],[-65.828457,18.423543],[-65.816691,18.410663],[-65.794556,18.402845],[-65.787666,18.402544],[-65.774937,18.413951],[-65.77053,18.41294],[-65.769749,18.409473],[-65.771695,18.406277],[-65.750455,18.385208],[-65.750179,18.38505],[-65.742154,18.380459],[-65.733567,18.382211],[-65.699069,18.368156],[-65.669636,18.362102],[-65.668845,18.361939],[-65.634431,18.369835],[-65.627246,18.376436],[-65.626527,18.381728],[-65.624975,18.386553],[-65.622761,18.387771],[-65.618229,18.386496],[-65.614891,18.382473],[-65.619068,18.367755],[-65.628198,18.353711],[-65.63419,18.338965],[-65.628047,18.328252],[-65.626456,18.298982],[-65.634389,18.292349],[-65.635826,18.288271],[-65.634893,18.283923],[-65.630833,18.264989],[-65.623111,18.248012],[-65.597618,18.234289],[-65.589947,18.228225],[-65.593795,18.224059],[-65.615981,18.227389],[-65.626731,18.235484],[-65.638181,18.229121],[-65.637565,18.224444],[-65.628414,18.205149],[-65.635281,18.199975],[-65.639688,18.205656],[-65.662185,18.207018],[-65.664127,18.207136],[-65.690749,18.19499],[-65.694515,18.187011],[-65.691021,18.178998],[-65.695856,18.179324],[-65.710895,18.186963],[-65.712533,18.189146],[-65.717999,18.190176],[-65.728471,18.185588],[-65.734664,18.180368],[-65.738834,18.174066],[-65.739125,18.173453],[-65.743632,18.163957],[-65.758728,18.156601],[-65.766919,18.148424],[-65.777584,18.129239],[-65.796711,18.083746],[-65.796289,18.079835],[-65.794686,18.078607],[-65.795028,18.073561],[-65.796711,18.069842],[-65.801831,18.058527],[-65.809174,18.056818],[-65.817107,18.063378],[-65.825848,18.057482],[-65.83109,18.050664],[-65.834274,18.038988],[-65.832429,18.014916],[-65.839591,18.015077],[-65.850913,18.011954],[-65.870335,18.006597],[-65.875122,18.002826],[-65.884937,17.988521],[-65.896102,17.99026],[-65.905319,17.983974],[-65.910537,17.981855],[-65.924738,17.976087],[-65.976611,17.967669],[-65.98455,17.969411],[-65.985358,17.971854],[-65.995185,17.978989],[-66.007731,17.980541],[-66.017308,17.979583],[-66.019539,17.978354],[-66.024,17.975896],[-66.046585,17.954853],[-66.049033,17.954561],[-66.058217,17.959238],[-66.068678,17.966335],[-66.069979,17.966357],[-66.08141,17.966552],[-66.116194,17.949141],[-66.127009,17.946953],[-66.140661,17.94102],[-66.147912,17.933963],[-66.155387,17.929406],[-66.159742,17.928613],[-66.161232,17.931747],[-66.175626,17.933565],[-66.186914,17.935363],[-66.189726,17.933936],[-66.200174,17.929515],[-66.206961,17.932268],[-66.213374,17.944614],[-66.202655,17.944753],[-66.185554,17.940997],[-66.179548,17.943727],[-66.174839,17.948214],[-66.176814,17.950438],[-66.206207,17.96305],[-66.206807,17.963307],[-66.215355,17.959376],[-66.218081,17.95729],[-66.231519,17.943912],[-66.229181,17.934651],[-66.232013,17.931154],[-66.252737,17.934574],[-66.260684,17.936083],[-66.270905,17.947098],[-66.275651,17.94826],[-66.290782,17.946491],[-66.297679,17.959148],[-66.31695,17.976683],[-66.323659,17.978536],[-66.338152,17.976492],[-66.33839,17.976458],[-66.362511,17.968231],[-66.365098,17.964832],[-66.368777,17.957717],[-66.371591,17.951469],[-66.385059,17.939004],[-66.391227,17.945819],[-66.398945,17.950925],[-66.412131,17.957286],[-66.445481,17.979379],[-66.450368,17.983226],[-66.454888,17.986784],[-66.461342,17.990273],[-66.491396,17.990262],[-66.510143,17.985618],[-66.540537,17.975476],[-66.583233,17.961229],[-66.589658,17.969386],[-66.594392,17.970682],[-66.605035,17.969015],[-66.623788,17.98105],[-66.631944,17.982746],[-66.645651,17.98026],[-66.657797,17.974605],[-66.664391,17.968259],[-66.672819,17.966451],[-66.699115,17.977568],[-66.709856,17.982109],[-66.713394,17.987763],[-66.716957,17.990344],[-66.731118,17.991658],[-66.746248,17.990349],[-66.750427,17.995443],[-66.753964,17.99959],[-66.755341,18.001203],[-66.764491,18.006317],[-66.770307,18.005955],[-66.799656,17.99245],[-66.806866,17.983786],[-66.807924,17.979606],[-66.806903,17.976046],[-66.805683,17.975052],[-66.795106,17.977438],[-66.789302,17.980793],[-66.784953,17.978326],[-66.787245,17.972914],[-66.80827,17.965635],[-66.8224,17.954499],[-66.838584,17.949931],[-66.852288,17.955004],[-66.856474,17.956553],[-66.859471,17.954316],[-66.862545,17.952022],[-66.871697,17.952707],[-66.88344,17.952526],[-66.899639,17.948298],[-66.904585,17.950527],[-66.906532,17.955356],[-66.906276,17.963368],[-66.924529,17.972808],[-66.928651,17.970204],[-66.930414,17.963127],[-66.916127,17.959102],[-66.909483,17.952559],[-66.909359,17.94988],[-66.912522,17.947446],[-66.930313,17.943389],[-66.932636,17.939998],[-66.931581,17.9369],[-66.919298,17.932062],[-66.923826,17.926923],[-66.927261,17.926875],[-66.959998,17.940216],[-66.980516,17.951648],[-66.98105,17.952505],[-66.982669,17.9551],[-66.982206,17.961192],[-66.987287,17.970663],[-66.996738,17.972899],[-67.003972,17.970799],[-67.014744,17.968468],[-67.024522,17.970722],[-67.062478,17.973819],[-67.076534,17.967759],[-67.089827,17.951418],[-67.101468,17.946621],[-67.109985,17.945806],[-67.109986,17.945806],[-67.128251,17.948153],[-67.133733,17.951919],[-67.167031,17.963073],[-67.178566,17.964792],[-67.183508,17.962706],[-67.188717,17.950989],[-67.187474,17.946252],[-67.183694,17.937982],[-67.183457,17.931135],[-67.194785,17.932826],[-67.196924,17.935651],[-67.197273,17.937461],[-67.197517,17.941514],[-67.197668,17.943549],[-67.198988,17.94782],[-67.200973,17.949896],[-67.210034,17.953595],[-67.212101,17.956027],[-67.21433,17.962436],[-67.215271,17.983464],[-67.211973,17.992993],[-67.207694,17.998019],[-67.177893,18.008882],[-67.174299,18.011149],[-67.172397,18.014906],[-67.172138,18.021422],[-67.173761,18.024548],[-67.193269,18.03185],[-67.209887,18.035439],[-67.196694,18.066491],[-67.190656,18.064269],[-67.184589,18.06775],[-67.183938,18.069914],[-67.186465,18.074195],[-67.192999,18.076877],[-67.198212,18.076828],[-67.199314,18.091135],[-67.19529,18.096149],[-67.183921,18.103683],[-67.182182,18.108507],[-67.176554,18.151046],[-67.178618,18.159318],[-67.180822,18.168055],[-67.180701,18.168182],[-67.155185,18.195001],[-67.152665,18.203493],[-67.158001,18.216719],[-67.173,18.230666],[-67.175429,18.248008],[-67.187843,18.266671],[-67.187873,18.266874],[-67.189971,18.281015],[-67.196056,18.290443],[-67.209963,18.294974],[-67.225403,18.296648],[-67.226081,18.296722],[-67.235137,18.299935],[-67.267484,18.353149],[-67.27135,18.362329],[-67.268259,18.366989],[-67.260671,18.370197],[-67.23909,18.375318],[-67.226744,18.378247],[-67.216998,18.382078],[-67.202167,18.389908],[-67.160144,18.415587],[-67.159608,18.415915],[-67.156599,18.418983],[-67.155245,18.424401],[-67.156619,18.439562],[-67.161746,18.453462],[-67.169011,18.466352],[-67.169016,18.478488],[-67.164144,18.487396],[-67.14283,18.505485],[-67.138249,18.507776],[-67.125655,18.511706],[-67.103468,18.514523],[-67.093752,18.515757],[-67.07929,18.513256],[-67.020276,18.510603],[-66.988958,18.497724],[-66.95954,18.489878],[-66.957733,18.489129],[-66.957517,18.489171],[-66.944636,18.491693],[-66.906872,18.483556],[-66.90143,18.484552],[-66.867386,18.490785],[-66.849673,18.490745],[-66.83694,18.487659],[-66.836635,18.487701],[-66.79932,18.492775],[-66.780311,18.491411],[-66.764893,18.484097],[-66.749301,18.476701],[-66.742067,18.474681],[-66.733986,18.473457],[-66.710743,18.472611],[-66.683719,18.481367],[-66.679876,18.484944],[-66.664364,18.487809],[-66.645839,18.488777],[-66.624618,18.494199],[-66.586778,18.484948],[-66.584074,18.484287],[-66.565241,18.485523],[-66.562916,18.48845],[-66.563485,18.490512],[-66.558503,18.489987],[-66.53484,18.481253],[-66.533487,18.481663],[-66.529476,18.482877],[-66.511609,18.476848],[-66.470292,18.46907],[-66.456486,18.46892],[-66.449184,18.470991],[-66.441852,18.479751],[-66.439961,18.485525],[-66.438813,18.485713]]]]},\"properties\":{\"name\":\"Puerto Rico\",\"nation\":\"USA \"}}]}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivera-Burgos, A.C.","contributorId":280017,"corporation":false,"usgs":false,"family":"Rivera-Burgos","given":"A.C.","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":835886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835887,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230044,"text":"70230044 - 2021 - Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2022-03-28T14:43:35.819413","indexId":"70230044","displayToPublicDate":"2021-08-23T09:38:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":10527,"text":"Final Project Report","active":true,"publicationSubtype":{"id":4}},"title":"Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico","docAbstract":"Climate change is transforming ecosystems and affecting ecosystem goods and services. Along the Gulf of Mexico and Atlantic coasts of the southeastern United States, the frequency and intensity of extreme freeze events greatly influences whether coastal wetlands are dominated by freeze-sensitive woody plants (mangrove forests) or freeze-tolerant grass-like plants (salt marshes). In response to warming winters, mangroves have been expanding and displacing salt marshes at varying degrees of severity in parts of north Florida, Louisiana, and Texas. As winter warming accelerates, mangrove range expansion is expected to increasingly modify wetland ecosystem structure and function. Because there are differences in the ecological and societal benefits that salt marshes and mangroves provide, coastal environmental managers are challenged to anticipate effects of mangrove expansion on critical wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation. This project produced information that is relevant to scientists and coastal resource managers working within the transition zone between mangrove forests and salt marshes. The two primary products are: (1) an investigation that leverages data and information from a community-curated data network called the Mangrove Migration Network to refine temperature thresholds for mangrove range expansion in a warming climate; and (2) a review article that examines current understanding of the effects of mangrove range expansion and displacement of salt marshes on wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation.","language":"English","publisher":"Southeast Climate Adaptation Science Center (SECASC)","usgsCitation":"Osland, M., 2021, Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico: Final Project Report, 26 p.","productDescription":"26 p.","ipdsId":"IP-132784","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":397706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397659,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/mangrove-migration/"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.123046875,\n 25.64152637306577\n ],\n [\n -81.123046875,\n 25.720735134412106\n ],\n [\n -81.123046875,\n 25.720735134412106\n ],\n [\n -81.123046875,\n 25.64152637306577\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.771484375,\n 25.48295117535531\n ],\n [\n -82.177734375,\n 27.137368359795584\n ],\n [\n -82.353515625,\n 28.536274512989916\n ],\n [\n -83.671875,\n 30.44867367928756\n ],\n [\n -84.990234375,\n 29.916852233070173\n ],\n [\n -86.1328125,\n 30.44867367928756\n ],\n [\n -88.59374999999999,\n 30.44867367928756\n ],\n [\n -90.52734374999999,\n 30.29701788337205\n ],\n [\n -89.82421875,\n 29.458731185355344\n ],\n [\n -90.439453125,\n 29.458731185355344\n ],\n [\n -91.7578125,\n 29.916852233070173\n ],\n [\n -92.548828125,\n 29.6880527498568\n ],\n [\n -94.658203125,\n 29.84064389983441\n ],\n [\n -96.328125,\n 28.844673680771795\n ],\n [\n -97.470703125,\n 27.761329874505233\n ],\n [\n -97.998046875,\n 26.745610382199022\n ],\n [\n -97.3828125,\n 25.64152637306577\n ],\n [\n -96.591796875,\n 27.527758206861886\n ],\n [\n -94.04296874999999,\n 28.69058765425071\n ],\n [\n -91.40625,\n 29.22889003019423\n ],\n [\n -89.82421875,\n 28.536274512989916\n ],\n [\n -88.59374999999999,\n 28.304380682962783\n ],\n [\n -88.76953125,\n 29.611670115197377\n ],\n [\n -84.55078125,\n 29.305561325527698\n ],\n [\n -83.14453125,\n 27.059125784374068\n ],\n [\n -81.82617187499999,\n 25.005972656239187\n ],\n [\n -80.771484375,\n 25.48295117535531\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":222814,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838877,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223713,"text":"70223713 - 2021 - Disruption of the Francisella noatunensis orientalis pdpA gene results in virulence attenuation and protection in zebrafish","interactions":[],"lastModifiedDate":"2021-10-18T14:27:55.063182","indexId":"70223713","displayToPublicDate":"2021-08-23T07:32:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1987,"text":"Infection and Immunity","active":true,"publicationSubtype":{"id":10}},"title":"Disruption of the Francisella noatunensis orientalis pdpA gene results in virulence attenuation and protection in zebrafish","docAbstract":"Wetland creation is a common practice to mitigate for the loss of natural wetlands. However, there is still uncertainty about how effectively created wetlands replace habitat provided by natural wetlands. This uncertainty is due in part because post-construction monitoring of biological communities, and vertebrates especially, is rare and typically short-term (<5 years). We estimated occupancy of 4 amphibian species in 8 created mitigation wetlands, 7 impacted wetlands, and 7 reference wetlands in the Greater Yellowstone Ecosystem in Wyoming, USA. Mitigation wetlands were created to replace wetland habitat that was lost during road construction and ranged in age from 1 to 10 years when sampled. Impacted wetlands were natural wetlands partially filled by road construction and were adjacent to a highway. We sampled for amphibian larvae during 6 summers from 2013 to 2020 and used multi-species occupancy models that estimated detection and occupancy of each of 4 amphibian species to determine how amphibian responses changed over time, especially in mitigation wetlands. Occupancy did not differ between impacted and reference wetlands for any of the 4 amphibian species. Western Toads (Anaxyrus boreas) were most common (although briefly) in created wetlands, and occupancy of Columbia Spotted Frogs (Rana luteiventris), Western Tiger Salamanders (Ambystoma mavortium), and Boreal Chorus Frogs (Pseudacris maculata) was lower in created wetlands than in impacted or reference wetlands. Individual wetland area was positively associated with occupancy for all 4 species and wetland vegetation cover was positively associated with Boreal Chorus Frog and Columbia Spotted Frog occupancy; these results emphasize the importance of design characteristics when planning mitigation wetlands. The link between wetland age and occupancy was complex and included threshold and quadratic relationships for three of the four species, but only Boreal Chorus Frog occupancy was still increasing slowly at the end of our study. Our results indicate created wetlands did not attain the suitability of impacted and natural wetlands for local amphibians, even several years after construction. The complex relationships between wetland age and species-specific occupancy illustrate the importance of long-term monitoring in describing population responses to the construction of wetlands as mitigation for wetland loss.
The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time.
To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement:
“How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?”
A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives.
To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201122","collaboration":"Prepared in Cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fournier, A.M.V., Wilson, R.R., Lyons, J.E., Gleason, J.S., Adams, E.M., Barnhill, L.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M.J.L., Eaton, M.J., Frederick, P.C., Just, M.G., Seymour, M.A., Tirpak, J.M, and Woodrey, M.S., 2021, Structured decision making and optimal bird monitoring in the northern Gulf of Mexico: U.S. Geological Survey Open-File Report 2020–1122, 62 p., https://doi.org/10.3133/ofr20201122.","productDescription":"Report: ix, 62 p.; 6 Companion Files","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100582","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387878,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.xlsm","text":"2. Portfolio Analysis Spreadsheet","size":"139 KB"},{"id":387871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1122/coverthb.jpg"},{"id":387876,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix.xlsx","text":"5. Matrix of Management Actions and Bird Species","size":"45.5 KB"},{"id":387874,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds.xlsx","text":"1. Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"727 KB"},{"id":387872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122.pdf","text":"Report","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1122"},{"id":387875,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds_csv.zip","text":"1. Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"47.1 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387873,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_database.docx","text":"6. R Code for Using Deepwater Horizon Project Tracker Database","size":"14.1 KB"},{"id":387882,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_proposals.docx","text":"3. R Code to Simulate Monitoring Proposals","size":"15.7 KB"},{"id":387881,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios_csv.zip","text":"4. All Test Projects and Portfolios","size":"101 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387877,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix_csv.zip","text":"5. Matrix of Management Actions and Bird Species","size":"2.83 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387880,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios.xlsm","text":"4. All Test Projects and Portfolios","size":"1.28 MB"},{"id":387879,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.zip","text":"2. Portfolio Analysis Spreadsheet","size":"5.35 KB","linkHelpText":"- Zip file of tables in CSV format"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 25.997549919572112\n ],\n [\n -91.43920898437499,\n 26.194876675795218\n ],\n [\n -87.703857421875,\n 26.175158990178133\n ],\n [\n -86.37451171875,\n 27.205785724383325\n ],\n [\n -85.4736328125,\n 26.204734267107604\n ],\n [\n -83.155517578125,\n 24.647017162630366\n ],\n [\n -81.71630859375,\n 24.347096633808512\n ],\n [\n -80.22216796875,\n 24.926294766395593\n ],\n [\n -79.881591796875,\n 26.115985925333536\n ],\n [\n -80.584716796875,\n 27.926474039865017\n ],\n [\n -81.221923828125,\n 27.858503954841247\n ],\n [\n -81.793212890625,\n 28.806173508854776\n ],\n [\n -82.957763671875,\n 30.344435586368462\n ],\n [\n -83.265380859375,\n 30.65681556429287\n ],\n [\n -84.957275390625,\n 30.751277776257812\n ],\n [\n -85.0341796875,\n 31.015278981711266\n ],\n [\n -87.51708984375,\n 30.987027960280326\n ],\n [\n -87.7587890625,\n 31.512995857454676\n ],\n [\n -88.29711914062499,\n 31.55981453201843\n ],\n [\n -88.450927734375,\n 30.996445897426373\n ],\n [\n -89.395751953125,\n 30.949346915468563\n ],\n [\n -89.97802734375,\n 30.826780904779774\n ],\n [\n -90.758056640625,\n 30.477082932837682\n ],\n [\n -91.92260742187499,\n 30.543338954230222\n ],\n [\n -94.10888671875,\n 30.344435586368462\n ],\n [\n -94.7900390625,\n 30.230594564932193\n ],\n [\n -95.69091796875,\n 29.735762444449076\n ],\n [\n -95.51513671875,\n 29.372601506681402\n ],\n [\n -95.9326171875,\n 29.19053283229458\n ],\n [\n -96.5478515625,\n 29.094577077511826\n ],\n [\n -97.6025390625,\n 28.488005204159457\n ],\n [\n -97.987060546875,\n 27.819644755099446\n ],\n [\n -98.009033203125,\n 27.176469131898898\n ],\n [\n -97.767333984375,\n 26.352497858154024\n ],\n [\n -97.2509765625,\n 25.997549919572112\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107381","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Roman, D., Mastin, L.G., Anderson, K.R., Damby, D., Itikarai, I., Mulina, K., Saunders, S., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea: Journal of Geophysical Research, v. 420, 107381, 14 p., https://doi.org/10.1016/j.jvolgeores.2021.107381.","productDescription":"107381, 14 p.","ipdsId":"IP-131054","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":451112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://hdl.handle.net/11603/26623","text":"Publisher Index Page"},{"id":389733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Papua New Guinea","otherGeospatial":"Bismark volcanic arc, New Britain Island, Ulawun volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 151.512451171875,\n -5.637852598770853\n ],\n [\n 151.58660888671875,\n -5.577716982024741\n ],\n [\n 151.78985595703125,\n -5.607785565054955\n ],\n [\n 152.0947265625,\n -5.473831889192786\n ],\n [\n 152.16064453124997,\n -5.328909448322195\n ],\n [\n 151.9683837890625,\n -5.123772299948803\n ],\n [\n 151.99859619140625,\n -4.989713620805972\n ],\n [\n 151.6387939453125,\n -4.784468966579362\n ],\n [\n 151.578369140625,\n -4.913096316540102\n ],\n [\n 151.171875,\n -4.869311036456154\n ],\n [\n 151.0894775390625,\n -4.891204034424394\n ],\n [\n 150.99334716796875,\n -5.307031369598637\n ],\n [\n 150.99884033203125,\n -5.356255950601535\n ],\n [\n 151.512451171875,\n -5.637852598770853\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"420","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, Kevin","contributorId":194091,"corporation":false,"usgs":false,"family":"Reath","given":"Kevin","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":823932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snee, Eveanjelene 0000-0002-3660-4020","orcid":"https://orcid.org/0000-0002-3660-4020","contributorId":265978,"corporation":false,"usgs":false,"family":"Snee","given":"Eveanjelene","email":"","affiliations":[{"id":54849,"text":"School of Earth and Ocean Sciences, Cardiff University, Cardiff, Wales, UK","active":true,"usgs":false}],"preferred":false,"id":823933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maher, Sean","contributorId":265979,"corporation":false,"usgs":false,"family":"Maher","given":"Sean","affiliations":[{"id":54850,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":823934,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":823935,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carn, Simon A","contributorId":191165,"corporation":false,"usgs":false,"family":"Carn","given":"Simon","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":823936,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":823937,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823938,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823939,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Damby, David 0000-0002-3238-3961","orcid":"https://orcid.org/0000-0002-3238-3961","contributorId":206614,"corporation":false,"usgs":true,"family":"Damby","given":"David","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823940,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Itikarai, Ima","contributorId":265986,"corporation":false,"usgs":false,"family":"Itikarai","given":"Ima","email":"","affiliations":[{"id":54853,"text":"Rabaul Volcano Observatory, Department of Mining and Petroleum, Geological Survey of Papua New Guinea, Rabaul, Papua New Guinea","active":true,"usgs":false}],"preferred":false,"id":823941,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mulina, Kila","contributorId":265987,"corporation":false,"usgs":false,"family":"Mulina","given":"Kila","email":"","affiliations":[{"id":54853,"text":"Rabaul Volcano Observatory, Department of Mining and Petroleum, Geological Survey of Papua New Guinea, Rabaul, Papua New Guinea","active":true,"usgs":false}],"preferred":false,"id":823942,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Saunders, Steve","contributorId":265988,"corporation":false,"usgs":false,"family":"Saunders","given":"Steve","email":"","affiliations":[{"id":54853,"text":"Rabaul Volcano Observatory, Department of Mining and Petroleum, Geological Survey of Papua New Guinea, Rabaul, Papua New Guinea","active":true,"usgs":false}],"preferred":false,"id":823943,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Assink, Jelle D.","contributorId":236650,"corporation":false,"usgs":false,"family":"Assink","given":"Jelle","email":"","middleInitial":"D.","affiliations":[{"id":47493,"text":"R and D Seismology and Acoustics, Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":823944,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"de Negri Levia, Rodrigo 0000-0003-1283-2579","orcid":"https://orcid.org/0000-0003-1283-2579","contributorId":265983,"corporation":false,"usgs":false,"family":"de Negri Levia","given":"Rodrigo","email":"","affiliations":[{"id":54852,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA; NDC-CTBT of the Chilean Nuclear Energy Commission, Chile","active":true,"usgs":false}],"preferred":false,"id":823953,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Perttu, Anna 0000-0003-3590-1549","orcid":"https://orcid.org/0000-0003-3590-1549","contributorId":265984,"corporation":false,"usgs":false,"family":"Perttu","given":"Anna","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":823946,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70224747,"text":"70224747 - 2021 - Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","interactions":[],"lastModifiedDate":"2021-10-04T12:47:21.058229","indexId":"70224747","displayToPublicDate":"2021-08-20T07:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9372,"text":"Journal of Geophysical Research--Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","docAbstract":"Numerical models of sediment transport in estuarine systems rely on parameter values that are often poorly constrained and can vary on timescales relevant to model processes. The selection of parameter values can affect the accuracy of model predictions, while environmental variation of these parameters can impact the temporal and spatial ranges of sediment fluxes, erosion, and deposition in the real world. We implemented a numerical model of San Pablo Bay, an embayment within San Francisco Bay, California, for November–December 2014, and compared model outputs to observations of water level, velocity, wave parameters, salinity, and suspended sediment concentration (SSC) in the shallow regions. Idealized model runs show that wind timing relative to the phase of the tides is the strongest control on sediment fluxes and bed erosion. We varied sediment erodibility in the outflow of the Petaluma River; while this causes erosion and deposition to vary strongly through the shallows system, total export from the shallows does not change. Model runs with realistic winds show that wind likely resuspends faster settling particles or allows for more particle flocculation; particle settling velocity controls system-wide sediment accumulation. At the margins of the system, the magnitude of SSC is closely tied to wind direction when winds occur during flood tide, but sediment deposition is less connected: Both bed evolution and SSC need to be considered in the prediction of marsh fate. Spatial patterns of light attenuation due to SSC is strongly tied to assumed settling velocity.
The Little Colorado River is a major tributary to the Colorado River with a confluence at the boundary between Marble and Grand Canyons within Grand Canyon National Park, Arizona. The bedrock gorge of the lower Little Colorado River is home to the largest known population of Gila cypha (humpback chub), an endangered fish endemic to the Colorado River Basin. Channel conditions might affect the spawning success of the humpback chub. Perennial base flow in the lower Little Colorado River deposits travertine, which forms dams and cascades. Geomorphic change in the lower Little Colorado River is controlled by the growth and collapse of travertine dams, debris flows from tributaries, and reworking of dams and debris fans by Little Colorado River floods.
A study was conducted by the U.S. Geological Survey, in cooperation with the Glen Canyon Dam Adaptive Management Program and the U.S. Fish and Wildlife Service, to document historical floods and geomorphic change in the lower Little Colorado River. For this study, we used historical and gaging records and hydraulic modeling of surveyed high-water marks from historical Little Colorado River floods to construct a peak-flow history of the lower Little Colorado River. We analyzed base-flow longitudinal profiles and historical photographs to determine changes in the longitudinal profile of the lower Little Colorado River from 1909 to 2019. The peak-flow magnitudes and the frequency of larger floods have declined since the late 1800s, and the longitudinal profile of the Little Colorado River has substantially changed between 1909 and 2019. Aggradation of as much as 6 meters in some reaches occurred between 1926 and 1992, mostly before the 1950s. This aggradation was caused largely by the documented growth of travertine dams continuing through at least 2013 at several locations. Other reaches were incised by as much as 10 meters between 1926 and 1992, but mostly before the 1950s, largely from the breaching of travertine dams. Travertine dams in the Little Colorado River have survived large flooding events and then later collapsed during floods of lower streamflow or even periods of base flow. The decline in peak-flow magnitude and frequency has changed the dominant geomorphic processes in this formerly dynamic reach. Large incision events have not been documented since the early 1950s; for this reason, the reach has only aggraded or remained stable since that time. This loss of geomorphic disturbance has likely affected, and will likely continue to affect, the spawning habitat of the endangered humpback chub in the lower Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215049","usgsCitation":"Unema, J.A., Topping, D.J., Kohl, K.A., Pillow, M.J., and Caster, J.J., 2021, Historical floods and geomorphic change in the lower Little Colorado River during the late 19th to early 21st centuries: U.S. Geological Survey Scientific Investigations Report 2021–5049, 34 p., https://doi.org/10.3133/sir20215049.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-113057","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5049/covrthb.jpg"},{"id":388148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5049/sir20215049.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VGWRV1","linkHelpText":"Topographic data, historical peak-stage data, and 2D flow models for the lowermost Little Colorado River, Arizona, USA, 2017"}],"country":"United States","state":"Arizona, Utah, New Mexico, Utah","otherGeospatial":"Lower Little Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 33.247875947924385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
U.S. Geological Survey
MS 303
12201 Sunrise Valley Drive
Reston, VA 20192
Strandlines of peak-stage indicators (such as driftwood logs, woody debris, and trash) provide valuable data for understanding the maximum stage and extent of inundation during floods. A series of seven strandlines have been preserved along the Colorado River in Grand Canyon National Park, Arizona, USA. A survey and analysis of these strandlines was completed from the Colorado River at Lees Ferry, Ariz., gaging station to the Colorado River near Grand Canyon, Ariz., gaging station. Owing to the longitudinally discontinuous nature of the strandlines, several lines of evidence were used to determine the year of the flood associated with each strandline segment. This evidence included strandline relative vertical position, degree of peak-stage indicator weathering, datable trash drift, and map-view location. The seven distinct strandlines identified were deposited during floods with the following peak discharges (in cubic feet per second [ft3/s]) at the Colorado River at Lees Ferry, Ariz., gaging station (year of flood in parentheses): 210,000 ft3/s (1884), 170,000 ft3/s (1921), 125,000 ft3/s (1957), 108,000 ft3/s (1958), 97,000 ft3/s (1983), 52,500 ft3/s (1986), and 45,000 ft3/s (multiple events between 1996 and 2012). Stage-discharge relations were developed in areas where all, or most of the strandlines were present, and were compared to predicted stage-discharge relations from a one-dimensional flow model. River width exerted a strong control on these relations, with much greater stage change occurring for a given discharge change in narrower bedrock-dominated reaches than in wider reaches with more extensive channel-margin alluvium. This comprehensive dataset allows for the verification of model-predicted flood stage along the Colorado River in Grand Canyon National Park.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215048","usgsCitation":"Sabol, T.A., Griffiths, R.E., Topping, D.J., Mueller, E.R., Tusso, R.B., and Hazel, J.E., Jr., 2021, Strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona: U.S. Geological Survey Scientific Investigations Report 2021-5048, 41 p., https://doi.org/10.3133/sir20215048.","productDescription":"Report: vi, 41 p.; Data Release; Version History","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-118687","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388103,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GIQ9ZN","linkHelpText":"Surveyed peak-stage elevations, coordinates, and indicator data of strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona"},{"id":388754,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5048/versionhist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":387949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5048/covrthb.jpg"},{"id":388753,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5048/sir20215048.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 35.78662688467009\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.
Here we respond to Baveye and colleagues' recent critique of our PROMISE model, describing how this new framework significantly advances our understanding of soil spatial heterogeneity and its influence on organic matter transformations.
Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
In semi-arid environments, aperiodic rainfall pulses determine plant production and resource availability for higher trophic levels, creating strong bottom-up regulation. The influence of climatic factors on population vital rates often shapes the dynamics of small mammal populations in such resource-restricted environments. Using a 21-year biannual capture–recapture dataset (1993 to 2014), we examined the impacts of climatic factors on the population dynamics of the brush mouse (Peromyscus boylii) in semi-arid oak woodland of coastal-central California. We applied Pradel's temporal symmetry model to estimate capture probability (p), apparent survival (φ), recruitment (f), and realized population growth rate (λ) of the brush mouse and examined the effects of temperature, rainfall, and El Niño on these demographic parameters. The population was stable during the study period with a monthly realized population growth rate of 0.993 ± SE 0.032, but growth varied over time from 0.680 ± 0.054 to 1.450 ± 0.083. Monthly survival estimates averaged 0.789 ± 0.005 and monthly recruitment estimates averaged 0.175 ± 0.038. Survival probability and realized population growth rate were positively correlated with rainfall and negatively correlated with temperature. In contrast, recruitment was negatively correlated with rainfall and positively correlated with temperature. Brush mice maintained their population through multiple coping strategies, with high recruitment during warmer and drier periods and higher survival during cooler and wetter conditions. Although climatic change in coastal-central California will likely favor recruitment over survival, varying strategies may serve as a mechanism by which brush mice maintain resilience in the face of climate change. Our results indicate that rainfall and temperature are both important drivers of brush mouse population dynamics and will play a significant role in predicting the future viability of brush mice under a changing climate.