Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.
Preventing the spread of aquatic invasive species is an important management action. Identifying the characteristics of lakes that are susceptible to invasion creates an opportunity for management groups to prioritize limited resources for high-risk areas. In this study, we leveraged big data from a popular fishing app and other publicly available sources of environmental and human-use exposure measurements to develop machine learning models to predict aquatic invasive species presence in 30,375 lakes in the upper Mississippi river basin of the United States. Our results predicted that an additional 665, 771, 544, 703, and 638 lakes in the basin are invaded or at high risk of invasion by Eurasian watermilfoil, curly-leaf pondweed, rusty crayfish, Chinese mystery snail, and dreissenid mussels, respectively. Lake invasions were predicted by a combination of environmental, human-use exposure, and community dynamics variables. Features that made a lake more attractive to recreationists were consistently important across our models including the presence of a boat ramp, larger lake size, and surrounding natural landscape. The importance of co-occurring invasive species in some models could reflect several scenarios including invasional meltdown, facilitation among species, similar pathways for introduction, or similar response to the environment. Our models predicted a higher proportion of invasions in less popular lakes compared to known invasions. The finding underscores the potential importance of less popular lakes in the invasion process and suggests that the detection of invasions may be lower in these lakes. These results serve as a valuable tool for data-driven management decisions and can provide actionable insights for effective aquatic invasive species management.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03367-6","usgsCitation":"Weir, J.L., Daniel, W., Hyder, K., Skov, C., and Venturelli, P.A., 2024, Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions: Biological Invasions, v. 26, p. 3163-3178, https://doi.org/10.1007/s10530-024-03367-6.","productDescription":"16 p.","startPage":"3163","endPage":"3178","ipdsId":"IP-162115","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":432144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weir, Jessica L.","contributorId":330438,"corporation":false,"usgs":false,"family":"Weir","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219312,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyder, Kieran","contributorId":291284,"corporation":false,"usgs":false,"family":"Hyder","given":"Kieran","email":"","affiliations":[{"id":62658,"text":"The Centre for Environment, Fisheries and Aquaculture Science (Cefas) and Collaborative Centre for Sustainable Use of the Seas (CCSUS), School of Environmental Sciences, University of East Anglia, Norwich Research Park","active":true,"usgs":false}],"preferred":false,"id":908935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skov, Christian","contributorId":268055,"corporation":false,"usgs":false,"family":"Skov","given":"Christian","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":908936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Venturelli, Paul A.","contributorId":171477,"corporation":false,"usgs":false,"family":"Venturelli","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256119,"text":"70256119 - 2024 - Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","interactions":[],"lastModifiedDate":"2024-07-23T20:23:11.322287","indexId":"70256119","displayToPublicDate":"2024-06-15T09:11:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 kilometers upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102376","usgsCitation":"Soong, D., Jackson, P.R., Kocovsky, P.M., Morrison, L., Garcia, T., Santacruz, S., Chen, C., Zhu, Z., and Embke, H.S., 2024, Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model: Journal of Great Lakes Research, v. 50, no. 4, 102376, 14 p., https://doi.org/10.1016/j.jglr.2024.102376.","productDescription":"102376, 14 p.","ipdsId":"IP-157787","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439399,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102376","text":"Publisher Index Page"},{"id":431354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83,\n 41.5\n ],\n [\n -83.25,\n 41.5\n ],\n [\n -83.25,\n 41.25\n ],\n [\n -83,\n 41.25\n ],\n [\n -83,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":906762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Lori","contributorId":340259,"corporation":false,"usgs":false,"family":"Morrison","given":"Lori","email":"","affiliations":[{"id":81526,"text":"Alaska Water Resources","active":true,"usgs":false}],"preferred":false,"id":906763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Tatiana","contributorId":340260,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":81527,"text":"AquaIntel Inc.","active":true,"usgs":false}],"preferred":false,"id":906764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santacruz, Santiago","contributorId":340261,"corporation":false,"usgs":false,"family":"Santacruz","given":"Santiago","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":906765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Cindy","contributorId":340262,"corporation":false,"usgs":false,"family":"Chen","given":"Cindy","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":906766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhenduo","contributorId":340263,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","affiliations":[{"id":81528,"text":"Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":906767,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":906768,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260439,"text":"70260439 - 2024 - Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","interactions":[],"lastModifiedDate":"2024-11-01T13:35:38.261218","indexId":"70260439","displayToPublicDate":"2024-06-15T08:26:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","docAbstract":"According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m*. We further identify site-specific m* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256593,"text":"70256593 - 2024 - Designing count-based studies in a world of hierarchical models","interactions":[],"lastModifiedDate":"2024-08-23T15:45:06.312749","indexId":"70256593","displayToPublicDate":"2024-06-14T10:36:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Designing count-based studies in a world of hierarchical models","docAbstract":"Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22622","usgsCitation":"Latif, Q., Valente, J., Johnston, A., Davis, K., Fogarty, F., Green, A.W., Jones, G.M., Leu, M., Michel, N.L., Pavlacky, D.C., Rigby, E., Rushing, C.S., Sanderlin, J., Tingley, M.W., and Zhao, Q., 2024, Designing count-based studies in a world of hierarchical models: Journal of Wildlife Management, v. 88, no. 7, e22622, 31 p., https://doi.org/10.1002/jwmg.22622.","productDescription":"e22622, 31 p.","ipdsId":"IP-153768","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/content/qt7h5896rk/qt7h5896rk.pdf","text":"External Repository"},{"id":433103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Latif, Quresh S.","contributorId":341286,"corporation":false,"usgs":false,"family":"Latif","given":"Quresh S.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, Alison","contributorId":341287,"corporation":false,"usgs":false,"family":"Johnston","given":"Alison","email":"","affiliations":[{"id":65006,"text":"University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":908191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Kayla L.","contributorId":341288,"corporation":false,"usgs":false,"family":"Davis","given":"Kayla L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":908192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Frank A.","contributorId":341289,"corporation":false,"usgs":false,"family":"Fogarty","given":"Frank A.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":908193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Adam W.","contributorId":341290,"corporation":false,"usgs":false,"family":"Green","given":"Adam","email":"","middleInitial":"W.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":908194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Gavin M.","contributorId":341291,"corporation":false,"usgs":false,"family":"Jones","given":"Gavin","email":"","middleInitial":"M.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":908195,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leu, Matthias","contributorId":341292,"corporation":false,"usgs":false,"family":"Leu","given":"Matthias","affiliations":[{"id":57314,"text":"William & Mary","active":true,"usgs":false}],"preferred":false,"id":908196,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michel, Nicole L.","contributorId":341293,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L.","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":908197,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pavlacky, David C. Jr.","contributorId":341294,"corporation":false,"usgs":false,"family":"Pavlacky","given":"David","suffix":"Jr.","email":"","middleInitial":"C.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908198,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rigby, Elizabeth A.","contributorId":341295,"corporation":false,"usgs":false,"family":"Rigby","given":"Elizabeth A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908199,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rushing, Clark S.","contributorId":341296,"corporation":false,"usgs":false,"family":"Rushing","given":"Clark","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":908200,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sanderlin, Jamie S.","contributorId":341297,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie S.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":908201,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tingley, Morgan W.","contributorId":341298,"corporation":false,"usgs":false,"family":"Tingley","given":"Morgan","email":"","middleInitial":"W.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908202,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zhao, Qing","contributorId":341299,"corporation":false,"usgs":false,"family":"Zhao","given":"Qing","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908203,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70254920,"text":"sim3522 - 2024 - Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire","interactions":[],"lastModifiedDate":"2026-01-29T21:54:14.125073","indexId":"sim3522","displayToPublicDate":"2024-06-13T12:35:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3522","displayTitle":"Bedrock Geologic Map of the Woodstock Quadrangle, Grafton County, New Hampshire","title":"Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire","docAbstract":"The bedrock geology of the Woodstock 7.5-minute quadrangle consists of highly deformed metasedimentary rocks of the Central Maine trough, including the Silurian Rangeley and Perry Mountain Formations and the Devonian Littleton Formation. The central, northern, and eastern parts of the quadrangle are underlain by the oldest rocks in the area, the Rangeley Formation. In the southwest and south-central part of the quadrangle, metaturbidites of the Perry Mountain Formation and subsequent Littleton Formation overly the Rangeley Formation in a deformed F1 synform, herein informally called the Bagley Brook basin. The metasedimentary rocks were intruded by widespread syn- to post-tectonic granitoids of the Devonian New Hampshire Plutonic Suite and minor post-metamorphic Jurassic-Cretaceous mafic dikes of the White Mountain Plutonic-Volcanic Suite. The metasedimentary rocks were affected by at least two episodes of deformation in the Devonian Acadian orogeny. The dominant regional foliation is second-generation (S2/D2) and formed during the development of sillimanite-muscovite mineral assemblages. Large bodies of the Early Devonian Kinsman Granodiorite intruded the metasedimentary rocks semi-concordantly during D2 deformation. Dikes of the Late Devonian Concord Granite cut the Kinsman Granodiorite and the metasedimentary rocks and were emplaced either syn- or post-D2. The map pattern in the Rangeley Formation is dominated by northeast to northwest trending, moderately to steeply north-dipping F2 and F3 folds. Map-scale F1 folds are defined by the Bagley Brook basin. Previous division of Rangeley Formation stratigraphy in this region into “upper” and “lower” parts was not corroborated by 1:24,000-scale mapping of lithodemic units, and rocks previously mapped as part of the Smalls Falls and Madrid Formations are here reassigned to the Rangeley Formation. Some rocks previously mapped as the lower part of the Littleton Formation are now assigned to the Perry Mountain Formation. The Littleton Formation on this map is approximately equivalent to rocks previously mapped as the upper part of the same formation.
Steeply dipping fractures in the quadrangle show a preferred northeast orientation, consistent with subsurface fracture orientations in the well fields near Mirror Lake. Jurassic-Cretaceous mafic dikes and normal faults show preferred northeast orientations, similar to the fractures, suggesting that the extensional stress field that controlled dike orientation during the Mesozoic also produced the dominant brittle fabrics in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3522","collaboration":"Prepared in cooperation with the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey; and the U.S. Department of Agriculture Forest Service","usgsCitation":"Walsh, G.J., Burton, W.C., Armstrong, T.R., and Crider, E.A., Jr., 2024, Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire: U.S. Geological Survey Scientific Investigations Map 3522, 1 sheet, scale 1:24,000, includes 20-p. pamphlet, https://doi.org/10.3133/sim3522.","productDescription":"Report: viii, 20 p.; 1 Sheet: 38.22 x 36.27 inches; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133738","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":429746,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3522/sim3522_sheet.pdf","text":"Sheet","size":"131 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3522 Sheet"},{"id":429740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3522/coverthb.jpg"},{"id":429895,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3522/images/"},{"id":429741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3522/sim3522_pamphlet.pdf","text":"Pamphlet","size":"24.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3522 PDF"},{"id":429743,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3522/sim3522.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3522 XML"},{"id":429745,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96TJ9OI","text":"USGS data release","linkHelpText":"Database for the bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire"},{"id":499290,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117075.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Hampshire","county":"Grafton County","otherGeospatial":"Woodstock quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.75,\n 44\n ],\n [\n -71.75,\n 43.875\n ],\n [\n -71.625,\n 43.875\n ],\n [\n -71.625,\n 44\n ],\n [\n -71.75,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 926A
Reston, VA 20192
Northern myotis Myotis septentrionalis are one of the bat species most affected by white-nose syndrome (WNS), and disease-induced declines may cause compounding effects when combined with other threats such as habitat loss and fragmentation. Recent evidence suggests that peripheral populations are persisting in post-WNS years; however, the environmental factors that influence the occurrence of this species along the Atlantic Coastal Plain are virtually unknown. We conducted a large-scale acoustic survey on 3 islands: Long Island, New York, and Martha’s Vineyard and Nantucket, Massachusetts, USA, and used a multi-scale occupancy modeling approach to determine the landscape and abiotic factors affecting the distribution of northern myotis. Our estimates of occupancy and detection probability suggest widespread presence across the islands. At the local (200 m) scale, we identified strong negative effects of development on Long Island and Nantucket and a strong positive effect of forest habitat on Martha’s Vineyard. None of the variables we measured sufficiently explained the landscape (1 km2 ) occupancy of this species, which was very high (ψ = 0.81–0.97), representing an outlier for this species in the post-WNS landscape. The lack of association at the landscape scale suggests that general differences in land cover are not a driving factor of higher occupancy of peripheral northern myotis populations, while local site- specific conditions may be supporting critical foraging or roosting habitat. Because islands are particularly vulnerable to human-driven habitat alteration due to the constraint of limited space, and development pressure is expected to increase, this study provides a baseline to enable managers to assess the effects of future environmental disturbances and monitor population trends to support long-term survival of northern myotis.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01335","usgsCitation":"Hoff, S., Mosher, B., Watson, M., Johnson, L., Olson, E., O’Dell, D., Pendergast, C.J., Bogan, D.A., Herzog, C.J., and Turner, W.C., 2024, Widespread occupancy of the endangered northern myotis on northeastern Atlantic Coastal Plain islands: Endangered Species Research, v. 54, p. 141-153, https://doi.org/10.3354/esr01335.","productDescription":"13 p.","startPage":"141","endPage":"153","ipdsId":"IP-158096","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439405,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01335","text":"Publisher Index Page"},{"id":433568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, New York","otherGeospatial":"Long Island, Martha's Vineyard, Nantucket","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.72456524610662,\n 41.44291621427166\n ],\n [\n -73.72456524610662,\n 40.57243247892035\n ],\n [\n -69.85737774610656,\n 40.57243247892035\n ],\n [\n -69.85737774610656,\n 41.44291621427166\n ],\n [\n -73.72456524610662,\n 41.44291621427166\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationDate":"2024-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoff, Samantha","contributorId":342962,"corporation":false,"usgs":false,"family":"Hoff","given":"Samantha","email":"","affiliations":[{"id":81956,"text":"University at Albany","active":true,"usgs":false}],"preferred":false,"id":910538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mosher, Brittany A.","contributorId":342963,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany A.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":910539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Mandy","contributorId":342964,"corporation":false,"usgs":false,"family":"Watson","given":"Mandy","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Luanne","contributorId":342965,"corporation":false,"usgs":false,"family":"Johnson","given":"Luanne","affiliations":[{"id":81959,"text":"BiodiversityWorks","active":true,"usgs":false}],"preferred":false,"id":910541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Elizabeth","contributorId":342966,"corporation":false,"usgs":false,"family":"Olson","given":"Elizabeth","email":"","affiliations":[{"id":81959,"text":"BiodiversityWorks","active":true,"usgs":false}],"preferred":false,"id":910542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Dell, Danielle","contributorId":342967,"corporation":false,"usgs":false,"family":"O’Dell","given":"Danielle","email":"","affiliations":[{"id":81960,"text":"Nantucket Conservation Foundation","active":true,"usgs":false}],"preferred":false,"id":910543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pendergast, Casey J.","contributorId":342968,"corporation":false,"usgs":false,"family":"Pendergast","given":"Casey","email":"","middleInitial":"J.","affiliations":[{"id":81956,"text":"University at Albany","active":true,"usgs":false}],"preferred":false,"id":910544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bogan, Daniel A.","contributorId":342969,"corporation":false,"usgs":false,"family":"Bogan","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":81961,"text":"Siena College","active":true,"usgs":false}],"preferred":false,"id":910545,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herzog, Carl J.","contributorId":342970,"corporation":false,"usgs":false,"family":"Herzog","given":"Carl","email":"","middleInitial":"J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910546,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910547,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259099,"text":"70259099 - 2024 - Low rate of population establishment of a freshwater invertebrate (Gammarus lacustris) in experimental conservation translocations","interactions":[],"lastModifiedDate":"2024-09-27T11:52:54.565701","indexId":"70259099","displayToPublicDate":"2024-06-13T06:48:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Low rate of population establishment of a freshwater invertebrate (Gammarus lacustris) in experimental conservation translocations","docAbstract":"Conservation translocations may be a useful tool for the restoration of declining freshwater invertebrates, but they are poorly represented in the literature. We conducted a before-after/control-impact (BACI) experiment to test the efficacy of conservation translocation for re-establishing abundant populations of the amphipod Gammarus lacustris, a declining species and wildlife food resource in depressional wetlands in the upper Midwest of the United States of America. Each study site (n = 19) contained at least one treatment wetland receiving translocated G. lacustris from a local donor and one control wetland. We selected study wetlands based on a suite of wetland characteristics and randomly assigned recipient versus control treatment. Gammarus lacustris was detected post-translocation at only 2 of 22 recipient wetlands (1 of 19 sites). Overall, there was a statistical increase in G. lacustris density in recipient wetlands compared to controls; however, the results were of minimal biological significance due to being driven by a single site with low G. lacustris densities. Accordingly, our results suggest that future conservation translocations of amphipods might be successful if limited to recently restored wetlands or informed by a more complex habitat suitability model to differentiate dispersal limitations from habitat limitations. To develop such a model would involve identifying the fewest, most influential physical and biological factors (e.g. wetland size/structure, fish, aquatic vegetation, and water chemistry) from the numerous inter-related factors that correlate with the abundance of naturally occurring G. lacustris; candidate wetlands to receive amphipods would be those for which the model predicts abundant G. lacustris but in which they do not presently occur.
Two Monitoring Avian Productivity and Survivorship (MAPS) stations were operated at Marine Corps Base Camp Pendleton (MCBCP), California, in 2021: one at De Luz Creek and one at the Santa Margarita River. The stations were established to provide data on Neotropical migratory birds at MCBCP to support the dual missions of environmental stewardship and military readiness.
A total of 1,227 individual birds were captured in 2021 between the two stations: 395 at De Luz and 832 at Santa Margarita (both 15 banding days). Of these 1,227 individuals captured, 955 were newly banded (273 at De Luz and 682 at Santa Margarita), 150 were recaptures banded before 2021 (28 at De Luz and 122 at Santa Margarita, excluding recaptures released before reading band number [1 at De Luz and 3 at Santa Margarita]), and 118 were unbanded (93 at De Luz and 25 at Santa Margarita). Return rate in 2021 was much lower than the annual mean at De Luz (1995–2019) and similar to the annual mean at the Santa Margarita station (1998–2020). The sex ratio of known-sex adult birds was skewed toward males at both stations in 2021.
Species richness was similar at De Luz from 2019 to 2021, increased at Santa Margarita from 2020 to 2021 and was above annual means at both sites (1995–2019 and 1998–2020, respectively). The most abundant species at De Luz were Wrentit (Chamaea fasciata) and Allen’s Hummingbird (Selasphorus sasin). Song Sparrow (Melospiza melodia) and Common Yellowthroat (Geothlypis trichas) were most abundant at Santa Margarita.
Since 2002, we have examined the population trends of 12 species at De Luz and 13 species at Santa Margarita for which numbers of known-age individuals were adequate for statistical analysis. We estimated population size and calculated indices of productivity and survival for a subset of these species with sufficient captures and recaptures for valid parameter estimation—four at De Luz and six at Santa Margarita. We determined that in 2021, abundance of 42 percent (5 of 12) of focal species at De Luz and 38 percent (5 of 13) of focal species at Santa Margarita was below the annual mean abundance. Of the focal species below mean abundance, 40 percent (2 of 5) at De Luz and 60 percent (3 of 5) at Santa Margarita were migrant populations. Of the focal species, 25 (3 of 12) percent at De Luz and 31 percent (4 of 13) at Santa Margarita had declining population trends during the span of station operation. With few exceptions, these declines appeared to be associated with conditions on the breeding grounds.
Annual productivity (calculated as the ratio of juveniles to adults among individual captures) was zero for all focal species at De Luz in 2021. At Santa Margarita, productivity increased from year 2020 to 2021 for Common Yellowthroat, Song Sparrow, and Yellow Warbler (Setophaga petechia) and declined from year 2020 to 2021 for Least Bell’s Vireo (Vireo bellii pusillus), but productivity was above the 1998–2020 mean for all four species, whereas productivity was maintained for Orange-crowned Warbler (Leiothlypis celata) and Yellow-breasted Chat (Icteria virens). Winter precipitation affected productivity of Black-headed Grosbeak (Pheucticus melanocephalus), Common Yellowthroat, and Song Sparrow at De Luz and affected productivity of Common Yellowthroat, Orange-crowned Warbler, Song Sparrow, Yellow-breasted Chat, and Yellow Warbler at Santa Margarita.
We calculated the mean annual adult survival for 1998–2020 at Santa Margarita, excluding years when the station was not operated. Survival could not be calculated for De Luz in 2021 because the station was not operated in 2020. Model-averaged annual adult survival ranged from 42 to 66 percent for residents and from 30 to 66 percent for migrants at Santa Margarita. Survival of Common Yellowthroat, Song Sparrow, and possibly Yellow Warbler was found to be affected by winter precipitation. Sex was a significant predictor of survival for Common Yellowthroat, Least Bell’s Vireo, Orange-crowned Warbler, and Yellow-breasted Chat at Santa Margarita, where females were found to have lower survival than males.
At Santa Margarita, multiple regression analyses examining adult survival and productivity as predictors of future population size indicated that resident Song Sparrow and migrant Yellow Warbler populations were affected by population size from the previous year, migrant Yellow-breasted Chat populations were affected by productivity from the previous year, and migrant Orange-Crowned Warbler populations were affected by survival from the previous year. Updated previous-year population size predictions could not be calculated for De Luz because the station was not operated in 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241024","collaboration":"Prepared in cooperation with Assistant Chief of Staff, Environmental Security, U.S. Marine Corps Base Camp Pendleton","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Mendia, S.M., and Kus, B.E., 2024, Neotropical migratory bird monitoring study at Marine Corps Base Camp Pendleton, California—2021 annual data summary: U.S. Geological Survey Open-File Report 2024–1024, 69 p., https://doi.org/10.3133/ofr20241024.","productDescription":"viii, 69 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-155196","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":429918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1024/covrthb.jpg"},{"id":429919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429920,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.xml"},{"id":429921,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1024/images"},{"id":429922,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241024/full"}],"country":"United States","state":"California","otherGeospatial":"Marine Corps Base Camp Pendleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.38,\n 33.275\n ],\n [\n -117.38,\n 33.26\n ],\n [\n -117.35,\n 33.26\n ],\n [\n -117.35,\n 33.275\n ],\n [\n -117.38,\n 33.275\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.33,\n 33.38\n ],\n [\n -117.33,\n 33.37\n ],\n [\n -117.32,\n 33.37\n ],\n [\n -117.32,\n 33.38\n ],\n [\n -117.33,\n 33.38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Sources and fluxes of methane to the atmosphere from permafrost are significant but poorly constrained in global climate models. We present data collected from the variable permafrost setting of the outer Mackenzie River Delta, including observations of aquatic methane seepage, core determinations of in situ methane occurrence and seep gas isotope geochemistry. The sources and locations of in situ geologic methane occurrence and aquatic and atmospheric gas release appear to be controlled by the regional geology and permafrost conditions. Where permafrost is >250 m thick, thermogenic gas deposits at depth are isolated by laterally continuous, low permeability ice-bearing sediments with few through-going thawed taliks. Thus, the observed in situ methane and aquatic gas seepage appears to be dominated by microbial methane. In contrast, where permafrost is <80 m thick, taliks are more likely to be through-going, providing permeable conduits from depth and migration pathways for both thermogenic and biogenic gas. Continuous annual fluid sampling of two lakes and a river channel documents aquatic methane flux from microbial sources, more deeply buried thermogenic sources, and mixtures of both. Using estimates of in situ methane concentration from deep core samples and observations of in situ free gas occurrences, we conclude that the reservoir of in situ geologic methane within ice bonded permafrost is substantial and that this methane is presently migrating with ongoing atmospheric release. It is our assessment that the permafrost setting, and processes described are sensitive to future climate change as the permafrost warms.
The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.
Large wood (LW) plays important geomorphic and ecological roles in rivers and is widely used as a restoration tool. Changes to floodplain land use and historical removal have altered wood dynamics in fluvial systems globally. We know little about the distribution and dynamics of LW in great rivers (approximately >105 km2) like the Upper Mississippi and Illinois Rivers despite its ecosystem importance and use in restoration projects. We assessed LW occurrence data collected by the fisheries component of the Upper Mississippi River Restoration Program's Long Term Resource Monitoring element. We analysed 25 years of data collected across six reaches of the Upper Mississippi and Illinois Rivers that represented contrasting physiographic settings, and across four aquatic area types comprising gradients of hydrology, connectivity and geomorphology. We tested hypotheses on drivers of LW occurrence using generalised linear mixed effects models, where occurrence was predicted by reach- and local-scale environmental variables. Occurrence varied significantly across reaches and aquatic area types. In general, wood occurred more frequently upriver and in side channels compared to other aquatic areas. Large wood was most strongly predicted systemically by reach identity but not local-scale variables, underscoring the importance of broad-scale physiographic gradients in defining hydrogeomorphic processes. Floodplain forests and shoreline revetment were consistently important predictors across reaches. Our findings show that the spatial variability of LW occurrence reflects the physical variability of the Upper Mississippi and Illinois Rivers. They also reveal the value in using geomorphic classifications as frameworks for understanding physical processes like LW dynamics because of their ability to contextualise site-scale conditions. The baseline understanding of LW abundance across different hydrogeomorphic gradients and scales presented here can give insight into how to more effectively target restoration efforts in great rivers and contribute to a broader understanding of LW dynamics where such studies have been lacking.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5911","usgsCitation":"Van Appledorn, M., Jankowski, K.J., Gahm, K., Budd, S., Baumann, D., Bennie, B., Erickson, R.A., Haro, R.J., and Rohweder, J.J., 2024, The where and why of large wood occurrence in the Upper Mississippi and Illinois Rivers: Earth Surface Processes and Landforms, v. 49, no. 11, p. 3383-3398, https://doi.org/10.1002/esp.5911.","productDescription":"16 p.","startPage":"3383","endPage":"3398","ipdsId":"IP-156995","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430017,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.73578092131349,\n 45.12080357067936\n ],\n [\n -91.01148319916909,\n 42.06683000374717\n ],\n [\n -92.03223565670272,\n 40.356697147602766\n ],\n [\n -91.38931153946832,\n 38.69309735425202\n ],\n [\n -89.14510076231794,\n 36.650083721260515\n ],\n [\n -88.69332581972627,\n 40.70515855929407\n ],\n [\n -89.58402008394364,\n 41.68908264434286\n ],\n [\n -90.9223085282444,\n 44.553043048533425\n ],\n [\n -93.73578092131349,\n 45.12080357067936\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gahm, Kaija","contributorId":338731,"corporation":false,"usgs":false,"family":"Gahm","given":"Kaija","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":903502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budd, Serenity","contributorId":338732,"corporation":false,"usgs":false,"family":"Budd","given":"Serenity","email":"","affiliations":[{"id":38728,"text":"Virginia Commonwealth University","active":true,"usgs":false}],"preferred":false,"id":903503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumann, Douglas","contributorId":328549,"corporation":false,"usgs":false,"family":"Baumann","given":"Douglas","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennie, Barbara","contributorId":328550,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903506,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":903507,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903508,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255313,"text":"70255313 - 2024 - Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","interactions":[],"lastModifiedDate":"2024-07-15T15:38:36.010069","indexId":"70255313","displayToPublicDate":"2024-06-11T06:39:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","docAbstract":"Mule deer (Odocoileus hemionus) are important economically, culturally, and recreationally to the Pueblo of Santa Ana in central New Mexico, USA. Studies of habitat selection improve our understanding of mule deer ecology in central New Mexico and provide the Tribe with valuable information for management of mule deer. We used global positioning system telemetry-collar data collected on mule deer around the Pueblo of Santa Ana to create resource selection functions from proximity-based habitat predictors using a generalized linear mixed model. We created separate resource selection functions for females and males during summer and winter at different times of the day. Season generally had a greater effect on mule deer habitat use than the time of day. Female and male mule deer selected for similar habitats but were sexually segregated in their summer distributions. These findings are consistent with results from other locations where mule deer partitioned habitat similarly between seasons and sexes. Supported models reaffirm accepted patterns of habitat selection for mule deer to the Pueblo of Santa Ana where local results were lacking. Our results can help managers identify locations in and around the Pueblo of Santa Ana where future development such as highway expansion are likely to conflict with mule deer activity and locations where habitat enhancement projects such as adding water sources can have the greatest effect for the deer population.
No abstract available.
","conferenceTitle":"85th EAGE Annual Conference & Exhibition","conferenceDate":"June 10-13, 2024","conferenceLocation":"Oslo, Norway","language":"English","publisher":"European Association of Geoscientists & Engineers","doi":"10.3997/2214-4609.2024101754","usgsCitation":"Ellis, G.S., Palmowski, D., and Lefeuvre, N., 2024, Hydrogen systems and reactive transport modelling: An approach to natural hydrogen exploration, 85th EAGE Annual Conference & Exhibition, v. 2024, Oslo, Norway, June 10-13, 2024, 5 p., https://doi.org/10.3997/2214-4609.2024101754.","productDescription":"5 p.","ipdsId":"IP-161901","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485990,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":937090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmowski, Daniel","contributorId":355192,"corporation":false,"usgs":false,"family":"Palmowski","given":"Daniel","affiliations":[{"id":84722,"text":"Terranta","active":true,"usgs":false}],"preferred":false,"id":937091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lefeuvre, Nicolas","contributorId":355193,"corporation":false,"usgs":false,"family":"Lefeuvre","given":"Nicolas","affiliations":[{"id":27334,"text":"Universite Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":937092,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257513,"text":"70257513 - 2024 - Human activity drives establishment, but not invasion, of non-native plants on islands","interactions":[],"lastModifiedDate":"2024-09-06T14:50:40.411326","indexId":"70257513","displayToPublicDate":"2024-06-10T07:40:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Human activity drives establishment, but not invasion, of non-native plants on islands","docAbstract":"Island ecosystems are particularly susceptible to the impacts of invasive species. Many rare and endangered species that are endemic to islands are negatively affected by invasions. Past studies have shown that the establishment of non-native species on islands is related to native plant richness, habitat heterogeneity, island age, human activity, and climate. However, it is unclear whether the factors promoting establishment (i.e. the formation of self-sustaining populations) also promote subsequent invasion (i.e. spread and negative impacts). Using data from 4308 non-native plant species across 46 islands and archipelagos globally, we examined which biogeographic characteristics influence established and invasive plant richness using generalized linear models nested within piecewise structural equation models. Our results indicate that anthropogenic land use (i.e. human modification) is strongly associated with establishment but not invasion, that climate (maximum monthly temperature) is strongly associated with invasion but not establishment, and that habitat heterogeneity (represented by maximum elevation and island area) is strongly associated with both establishment and invasion. Island isolation explains native plant richness well, but is not associated with established and invasive plant richness, likely due to anthropogenic introductions. We conclude that anthropogenic land use on islands is likely to be a proxy for the number of introductions (i.e. propagule pressure), which is more important for establishment than invasion. Conversely, islands with more diverse habitats and favorable (warm) climate conditions are likely to contain more available niche space (i.e. ‘vacant niches’) which create opportunities for both establishment and invasion. By evaluating multiple stages of the invasion process, we differentiate between the biogeographic characteristics that influence plant establishment (which does not necessarily lead to ecological impacts) versus those that influence subsequent plant invasion (which does lead to negative impacts).
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.07379","usgsCitation":"Pfadenhauer, W.G., DiRenzo, G.V., and Bradley, B.A., 2024, Human activity drives establishment, but not invasion, of non-native plants on islands: Ecography, e07379, 14 p., https://doi.org/10.1111/ecog.07379.","productDescription":"e07379, 14 p.","ipdsId":"IP-159338","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439421,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07379","text":"Publisher Index Page"},{"id":434944,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XES5OI","text":"USGS data release","linkHelpText":"Code for Human activity drives establishment, but not invasion, of non-native plants on islands"},{"id":433550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pfadenhauer, William G.","contributorId":343029,"corporation":false,"usgs":false,"family":"Pfadenhauer","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":910582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Bethany A.","contributorId":343032,"corporation":false,"usgs":false,"family":"Bradley","given":"Bethany","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910583,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257875,"text":"70257875 - 2024 - Evolutionary ecology of masting: Mechanisms, models, and climate change","interactions":[],"lastModifiedDate":"2024-09-11T16:27:42.339891","indexId":"70257875","displayToPublicDate":"2024-06-10T07:10:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Evolutionary ecology of masting: Mechanisms, models, and climate change","docAbstract":"We evaluate Cascadia subduction ground-motion models (GMMs), considered for the 2023 US National Seismic Hazard Model (NSHM) update, by comparing observations to model predictions. The observations comprise regional recordings from intraslab earthquakes, including contributions from 2021 and 2022 events in southern Cascadia and global records from interface earthquakes. Since the 2018 NSHM update, new GMMs for Cascadia have been published by the Next Generation Attenuation (NGA)-Subduction Project that require independent evaluation. In the regional intraslab comparisons, we highlight a characteristic frequency dependence for Cascadia data, with short periods having lower ground motions and longer periods being comparable to other subduction zones. We evaluate differences in northern and southern Cascadia and find that the NGA-Subduction GMMs developed using southern Cascadia data perform better in this region than the model that did not consider these data. We compare ground-motion variability in Cascadia with the NGA-Subduction model predictions and find differences at short periods (T = 0.1 s) due to the use of global versus regional data in the development of these models. Moreover, the within-event component of aleatory variability from the GMMs overpredicts the standard deviation of Cascadia recordings at very short periods (T < 0.05 s). Using global interface earthquakes as a proxy to evaluate the Cascadia GMMs, we find long-period overprediction from a simulation-based GMM and some of the empirical GMMs. When comparing recent observations, we find a similar misfit to GMMs and the 2010 and 2022 Ferndale earthquakes. Finally, we observe different basin amplification factors arising in different subsets of the data, which indicate that differences in basin factors between empirical GMMs could arise from the data selection choices by the developers. As part of evaluating the regional basin terms, we apply basin amplification factors from the magnitude 9 Cascadia earthquake simulations to the empirical GMMs for interface earthquakes. The comparisons presented in this study indicate that the NGA-Subduction GMMs for Cascadia perform well relative to observations and older subduction GMMs.
","language":"English","publisher":"Sage Journals","doi":"10.1177/87552930241256673","usgsCitation":"Smith, J.A., Moschetti, M.P., and Thompson, E.M., 2024, Comparing subduction ground-motion models to observations for Cascadia: Earthquake Spectra, v. 40, no. 3, p. 1787-1817, https://doi.org/10.1177/87552930241256673.","productDescription":"31 p.","startPage":"1787","endPage":"1817","ipdsId":"IP-159689","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":488672,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930241256673","text":"Publisher Index Page"},{"id":484070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -128,\n 50\n ],\n [\n -128,\n 40\n ],\n [\n -120,\n 40\n ],\n [\n -120,\n 50\n ],\n [\n -128,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, James Andrew 0000-0002-5565-9254 jimsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-9254","contributorId":332933,"corporation":false,"usgs":true,"family":"Smith","given":"James","email":"jimsmith@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":932437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932439,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254846,"text":"70254846 - 2024 - Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams","interactions":[],"lastModifiedDate":"2024-06-10T15:03:47.13455","indexId":"70254846","displayToPublicDate":"2024-06-07T09:58:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams","docAbstract":"Headwater stream temperature often exhibits spatial variation at the kilometer-scale, but the relative importance of the underlying hydrogeological processes and riverine perturbations remains poorly understood. In this study, we investigated the relative importance of groundwater (GW) and other processes on downstream annual stream temperature signal characteristics using deterministic heat budget model (HFLUX) scenarios within an idealized stream reach representative of mountainous forested conditions. We summarized annual stream thermal regimes from the relationship of paired sinusoidal air and water temperature signals (amplitude ratio, phase lag, and mean ratio). Results showed that downstream changes in annual temperature depended on the thermal gradient between water and the hypothetical equilibrium temperature (where all heat fluxes sum to zero). GW inflow, riparian shading, and the boundary input signal were the most significant factors affecting downstream annual water temperature signals, while flow volume and channel dimensions impacted how quickly annual temperature signals changed. Effects of GW were dominated by advective rather than conductive heat exchange processes, but conduction played a larger role when GW input was more spatially diffuse. Our results indicated several mechanisms by which local processes may affect stream thermal resilience to disturbances and can help guide management of wildfire and climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131391","usgsCitation":"Johnson, Z., Briggs, M., Snyder, C.D., Johnson, B.G., and Hitt, N.P., 2024, Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams: Journal of Hydrology, v. 638, 131391, 18 p., https://doi.org/10.1016/j.jhydrol.2024.131391.","productDescription":"131391, 18 p.","ipdsId":"IP-117590","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":497980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.131391","text":"Publisher Index Page"},{"id":429755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"638","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":902704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":902705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":902706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Brittany G. 0000-0002-8837-997X bdjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8837-997X","contributorId":245863,"corporation":false,"usgs":false,"family":"Johnson","given":"Brittany","email":"bdjohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":902707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":238185,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":902708,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254862,"text":"70254862 - 2024 - Rainfall intensification amplifies exposure of American Southwest to conditions that trigger postfire debris flows","interactions":[],"lastModifiedDate":"2024-06-10T14:56:12.774391","indexId":"70254862","displayToPublicDate":"2024-06-07T09:49:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17801,"text":"npj Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Rainfall intensification amplifies exposure of American Southwest to conditions that trigger postfire debris flows","docAbstract":"Short-duration, high-intensity rainfall can initiate deadly and destructive debris flows after wildfire. Methods to estimate the conditions that can trigger debris flows exist and guidance to determine how often those thresholds will be exceeded under the present climate are available. However, the limited spatiotemporal resolution of climate models has hampered efforts to characterize how rainfall intensification driven by global warming may affect debris-flow hazards. We use novel, dynamically downscaled (3.75-km), convection-permitting simulations of short-duration (15-min) rainfall to evaluate threshold exceedance for late 21st-century climate scenarios in the American Southwest. We observe significant increases in the frequency and magnitude of exceedances for regions dominated by cool- and warm-season rainfall. We also observe an increased frequency of exceedance in regions where postfire debris flows have not been documented, and communities are unaccustomed to the hazard. Our findings can inform planning efforts to increase resiliency to debris flows under a changing climate.
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Walker S.","contributorId":337846,"corporation":false,"usgs":false,"family":"Ashley","given":"Walker","email":"","middleInitial":"S.","affiliations":[{"id":13666,"text":"Northern Illinois University","active":true,"usgs":false}],"preferred":false,"id":902730,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254999,"text":"70254999 - 2024 - Earthquake scenario development in conjunction with the 2023 USGS National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2024-08-13T14:15:54.485877","indexId":"70254999","displayToPublicDate":"2024-06-07T08:58:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake scenario development in conjunction with the 2023 USGS National Seismic Hazard Model","docAbstract":"We present earthquake scenarios developed to accompany the release of the 2023 update to the US Geological Survey National Seismic Hazard Model (NSHM). Scenarios can serve a range of local and regional needs, from developing proactive-targeted mitigation strategies for minimizing impending risk to aiding emergency management planning. These deterministic scenarios can also be used to communicate seismic hazard and risk to audiences who are not well versed in methods, such as probabilistic seismic hazard analyses. Specifically, we discuss the scenarios developed, challenges, and lessons learned in the development process, and how this work aided the development of the 2023 NSHM itself. In total, 28 scenarios were developed for Hawaii, Utah, Alaska, and Virginia considering the 2023 NSHM science, past scenario efforts, and input from local experts and stakeholders. Finally, we investigate how NSHM modeling decisions can change estimated impacts to Utah and Hawaii in more detail showing, for example, that a shallower dip of the Wasatch fault under Salt Lake City can increase predicted ground-motion intensities and therefore estimated losses and deaths.
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In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.
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The 3D Hydrography Program","interactions":[],"lastModifiedDate":"2024-09-20T16:59:02.860799","indexId":"cir1519","displayToPublicDate":"2024-06-06T12:48:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1519","displayTitle":"The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program","title":"The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program","docAbstract":"The U.S. Geological Survey is initiating the 3D Hydrography Program (3DHP), the first systematic remapping of the Nation’s surface waters since the original 1:24,000-scale topographic mapping program was active from 1947 to 1992. Building on decades of experience maintaining the National Hydrography Dataset (NHD), the Watershed Boundary Dataset (WBD), and the NHDPlus High Resolution (NHDPlus HR), the 3DHP will completely refresh the Nation’s hydrography data and improve discovery and sharing of water-related data. The design of the 3DHP is based on the results of a study that estimated that the fully implemented program would have the potential to provide more than $1 billion in benefits to Federal, State, Tribal, Territorial, and local governments and to private and nonprofit organizations every year, in addition to myriad societal benefits. The 3DHP would directly support better decision making regarding water resources by providing more accurate, complete, and integrated information than is currently available.
The 3DHP datasets will include a three-dimensional (3D) hydrography network generated from and integrated with elevation data from the 3D Elevation Program (3DEP) to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. The 3DHP datasets will inherit key attributes of the NHD, WBD, and NHDPlus HR, and they also will include new attributes and links to other data such as the U.S. Fish and Wildlife Service National Wetlands Inventory, groundwater data, and engineered hydrologic systems such as stormwater networks. The 3DHP will be designed to provide a set of open and interoperable web-based tools, maps, and data catalogs, creating a robust system for users to reference their information about water; the system elements are collectively referred to as the “infostructure.” The 3DHP and the infostructure can provide a foundational geospatial underpinning for the Internet of Water, a community-based effort to modernize tools and technologies to share water data. As proposed, the 3DHP would begin providing products and services to the public in 2024.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1519","isbn":"978-1-4113-4579-9","programNote":"National Geospatial Program","usgsCitation":"Anderson, R., Lukas, V., and Aichele, S.S., 2024, The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program (ver. 1.1, July 2024): U.S. Geological Survey Circular 1519, 12 p., https://doi.org/10.3133/cir1519.","productDescription":"iv, 12 p.","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-138071","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":429527,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1519/coverthb2.jpg"},{"id":429903,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1519/cir1519.XML"},{"id":429904,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1519/images/"},{"id":429528,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1519/cir1519.pdf","text":"Report","size":"6.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1519 PDF"},{"id":430036,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1519/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1519 HTML"},{"id":430673,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1519/versionHist.txt","size":"1.42 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: June 6, 2024; Version 1.1: July 1, 2024","contact":"Director, National Geospatial Program
U.S. Geological Survey
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