Myotis septentrionalis (Northern Long-eared Bat) has recently suffered a >90% decline in population size in North America due to white-nose syndrome (WNS). We assessed genetic diversity, population structure, current effective population size, and demographic history of M. septentrionalis distributed across the United States to determine baseline levels pre-WNS. We analyzed RADseq data from 81 individuals from Kentucky, Louisiana, Michigan, Minnesota, North Carolina, Oklahoma, and Wisconsin. Additionally, we examined population genetic structure using discriminant analysis of principal components, fastStructure, and STRUCTURE. We then estimated effective population size and demographic history using fastsimcoal2. Similar levels of genetic diversity were found across all samples. We found no population genetic structure in the varied analyses from these contemporary samples. The best model for demographic history estimated a rapid population expansion followed by a slower expansion approximately 340,000 years ago. The vagility of M. septentrionalis, along with male dispersal and random mating, may provide a buffer against serious bottleneck effects stemming from rapid population declines due to WNS. This research provides a baseline for tracking and monitoring the influence of WNS on genetic diversity such as potential reduced diversity or increased population structuring in the future.
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This report presents results from two data collection efforts in the Lake Wenatchee Fire & Rescue service district in Chelan County, Washington: parcel level rapid wildfire risk assessments and household surveys sent to the owners of assessed parcels. Respondents reported that they were moderately aware of the risk of wildfire to their home, were discussing wildfire with their neighbors, and were taking action to reduce risk. There are gaps in respondents’ understanding of wildfire risk that might be addressed through educational outreach. Respondents were supportive of wildfire risk reduction strategies at the community level, including fuel treatments and policy options.
","language":"English","publisher":"USDA Forest Service Rocky Mountain Research Station","doi":"10.2737/RMRS-RN-103","usgsCitation":"Goolsby, J., Champ, P.A., Wittenbrink, S., Donovan, C., King, K., Brenkert-Smith, H., Meldrum, J., Barth, C.M., Wagner, C., and Forrester, C., 2024, Living with wildfire in Lake Wenatchee, Chelan County, Washington: 2022 Data report: Research Note RMRS-RN-103, vi, 130 p., https://doi.org/10.2737/RMRS-RN-103.","productDescription":"vi, 130 p.","ipdsId":"IP-162048","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":432651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","county":"Chelan County","otherGeospatial":"Lake Wenatchee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.99524534788046,\n 47.91597533876984\n ],\n [\n -120.99524534788046,\n 47.73844761896257\n ],\n [\n -120.60585715934415,\n 47.73844761896257\n ],\n [\n -120.60585715934415,\n 47.91597533876984\n ],\n [\n -120.99524534788046,\n 47.91597533876984\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goolsby, Julia 0000-0002-2229-5685","orcid":"https://orcid.org/0000-0002-2229-5685","contributorId":295471,"corporation":false,"usgs":false,"family":"Goolsby","given":"Julia","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":909825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":909826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wittenbrink, Suzanne","contributorId":333353,"corporation":false,"usgs":false,"family":"Wittenbrink","given":"Suzanne","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":909827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donovan, Colleen","contributorId":240586,"corporation":false,"usgs":false,"family":"Donovan","given":"Colleen","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":909828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Kris","contributorId":342230,"corporation":false,"usgs":false,"family":"King","given":"Kris","email":"","affiliations":[{"id":81845,"text":"Lake Wenatchee Fire & Rescue","active":true,"usgs":false}],"preferred":false,"id":909829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":909830,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909831,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":909832,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wagner, Carolyn","contributorId":240587,"corporation":false,"usgs":false,"family":"Wagner","given":"Carolyn","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":909833,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Forrester, Chiara","contributorId":328660,"corporation":false,"usgs":false,"family":"Forrester","given":"Chiara","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":909834,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256402,"text":"ofr20241049 - 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
AimSpatiotemporal variation in resource availability is a strong driver of animal distributions. In the northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high-elevation songbirds.
Location
Northeastern United States.
Methods
We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in the tree mast. We also assessed population responses using systematic auditory surveys analysed with an open-population binomial mixture model. Further, we used modelled red squirrel abundance in nest-survival models to evaluate whether their abundance is correlated with the daily nest survival of three songbird species.
Results
The tree mast provided a critical resource pulse that resulted in a two-fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663–1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443–1545 m asl) after a large mast event. The daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels.
Main Conclusions
Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13861","usgsCitation":"Hallworth, M.T., Sirén, A., DeLuca, W., Duclos, T., McFarland, K.P., Hill, J.M., Rimmer, C.C., and Morelli, T.L., 2024, Boom and bust: The effects of masting on seed predator range dynamics and trophic cascades: Diversity and Distributions, v. 30, no. 8, e13861, 13 p., https://doi.org/10.1111/ddi.13861.","productDescription":"e13861, 13 p.","ipdsId":"IP-129255","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":489019,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13861","text":"Publisher Index Page"},{"id":486083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","otherGeospatial":"northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.10575084649285,\n 43.37093588190811\n ],\n [\n -79.75309493274816,\n 41.99258454725708\n ],\n [\n -75.45135133469833,\n 42.005404340434055\n ],\n [\n -74.94884725348038,\n 41.60054495898998\n ],\n [\n -74.90966943261284,\n 40.41115406110167\n ],\n [\n -69.50442652451818,\n 41.140541678401235\n ],\n [\n -70.21215396984903,\n 43.20792379414405\n ],\n [\n -66.98409511373686,\n 44.634391727144134\n ],\n [\n -67.64757065599491,\n 47.18695334041415\n ],\n [\n -68.97147672811981,\n 47.48919160963973\n ],\n [\n -71.61820175777069,\n 45.07428624184727\n ],\n [\n -74.6889940707864,\n 45.17423846474814\n ],\n [\n -79.10575084649285,\n 43.37093588190811\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"30","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Hallworth, Michael T.","contributorId":213805,"corporation":false,"usgs":false,"family":"Hallworth","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":38879,"text":"National Zoological Park, Migratory Bird Center","active":true,"usgs":false}],"preferred":false,"id":937261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sirén, Alexej","contributorId":300102,"corporation":false,"usgs":false,"family":"Sirén","given":"Alexej","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":937262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeLuca, William","contributorId":192836,"corporation":false,"usgs":false,"family":"DeLuca","given":"William","affiliations":[],"preferred":false,"id":937263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duclos, Timothy","contributorId":236781,"corporation":false,"usgs":false,"family":"Duclos","given":"Timothy","email":"","affiliations":[{"id":41510,"text":"Department of Environmental Conservation, University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":937264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McFarland, Kent P.","contributorId":213789,"corporation":false,"usgs":false,"family":"McFarland","given":"Kent","email":"","middleInitial":"P.","affiliations":[{"id":38867,"text":"Vermont Center for Ecostudies","active":true,"usgs":false}],"preferred":false,"id":937265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hill, Jason M.","contributorId":191616,"corporation":false,"usgs":false,"family":"Hill","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":937266,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rimmer, Christopher C.","contributorId":213817,"corporation":false,"usgs":false,"family":"Rimmer","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":38867,"text":"Vermont Center for Ecostudies","active":true,"usgs":false}],"preferred":false,"id":937267,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":937268,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70256401,"text":"ofr20241038 - 2024 - Identifying transportation data and system needs for a Federal lands transportation data platform","interactions":[],"lastModifiedDate":"2024-08-01T13:46:37.3606","indexId":"ofr20241038","displayToPublicDate":"2024-07-31T13:10:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1038","displayTitle":"Identifying Transportation Data and System Needs for a Federal Lands Transportation Data Platform","title":"Identifying transportation data and system needs for a Federal lands transportation data platform","docAbstract":"Modern transportation and land-use planning efforts include information from many sources to address topics such as safety, efficiency, commercial, and social needs. This wide breadth of topics provides opportunities for collaboration and development of common tools for diverse users. In many cases, different information systems provide the spatial data and geographic content necessary for transportation and land-use planners to consider multiple lines of evidence. The Federal Highway Administration Office of Federal Lands Highway (FLH) and Federal Land Management Agency partners use detailed spatial and quantitative data to inform transportation decisions. However, logistic challenges to data sharing exist because data are often managed by separate agencies; data-exchange frameworks and interagency data agreements are insufficient; and consistency from aggregated data requires maintenance, coordination, and supporting infrastructure.
The FLH and U.S. Geological Survey collaboratively examined (1) use and availability of spatial data for transportation planning and (2) a possible mechanism to use more shared and consistent data in a common planning environment. The goals of this collaborative effort were to describe data needs from the perspective of planners and to identify opportunities for shared data resources. Results presented here focus on two workshops and a subsequent investigation of data and tools available from partner agencies. The objectives of this report are to (1) describe information used in transportation planning with geographic data; (2) identify spatially explicit data that inform transportation plans and could be shared among all partners; and (3) describe current platforms, planning and administrative opportunities, and potential barriers to developing an integrated planning tool.
Key information and data needs were identified in three major classes: system, user, and influential factors. System data are parts of the transportation network and information about the condition of individual segments and the network. User data provide details about the function of the system and insights into potential needs; for example, user trips between source and destinations inform road and network demands that can lead to congestion and safety issues (in the future, user data might also include scenarios and projections based on land-use plans). Influential data represent social and environmental factors that influence transit demands and network conditions. These factors could be popular locations or seasonal events that influence demand and congestion; wildlife habitat or migration intersections that affect safety and management priorities; or geologic features that influence hazards, maintenance, and safety. Responses described here provide specific information for web-tool design and give a framework for interagency communication and cooperation to address specific information needs for integrated planning. Existing web-mapping and web-services, and the data that inform them, are also described. Commonly, these data are created and published by one agency, and the core users are outside of that agency; for example, threatened species distributions are published by the U.S. Fish and Wildlife Service for consideration by planners in advance of National Environmental Policy Act (42 U.S.C. 4321 et seq.) evaluation.
This report is provided to inform FLH leaders and Federal Land Management Agency partners by articulating user needs and requirements for integrated planning tool(s). Programmers creating a secure web-based data-sharing platform (with data-viewing, -analysis and -download functions) can use the information presented here to organize data and user interfaces. This integrated perspective can help FLH and Federal Land Management Agency partners develop transportation networks that better serve the needs of people in local communities and across States and the Nation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20241038","collaboration":"Prepared in cooperation with the Federal Highway Administration, Federal Lands Highway Divisions","programNote":"Climate Adaptation Science Center & Land Change Science","usgsCitation":"Manier, D., Grisham, N., Armstrong, A., Henley, E., Doolittle, J., and Inman, R., 2024, Identifying transportation data and system needs for a Federal lands transportation data platform: U.S. Geological Survey Open-File Report 2024–1038, 37 p., https://doi.org/10.3133/ofr20241038.","productDescription":"vi, 37 p.","onlineOnly":"Y","ipdsId":"IP-153797","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":431727,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1038/ofr20241038.xml"},{"id":431726,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1038/images"},{"id":431683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1038/ofr20241038.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1038"},{"id":431682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1038/coverthb.jpg"},{"id":431765,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241038/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1038"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This paper presents a summary of previously published work (Celebi 2023) related to the new Self-Anchored Suspension (SAS) bridge that went into service within the last decade as a replacement for the older truss bridge spanning between Yerba Buena Island and Oakland, California, within the San Francisco Bay Area. During the October 19, 1989 M6.9 Loma Prieta earthquake, which occurred ~100 km south of the Bay Bridge, a section of the upper deck of the truss bridge fell onto the lower deck – thus closing this important lifeline between San Francisco and Oakland. The SAS is unique, self-anchored, and suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the decks. The SAS bridge is extensively instrumented by the California Geological Survey’s Strong Motion Instrumentation Program (CSMIP). There are approximately 85 channels of accelerometers in the seismic monitoring system that recorded the October 14, 2019 Mw4.6 Pleasant Hill earthquake. The data allow a complex but identifiable coupled response of the deck, tower, and cable system. Both acceleration and displacement time-history data are used to extract significant frequencies using system identification methods, including spectral analyses. Results are compared to those from finite-element-model (FEM) analyses carried out during the design and analysis process of the bridge in 2002 (Nader et al. 2002). There are differences between FEM analyses results and those from the low amplitude shaking caused by a seismic event. An apparent frequency (period) of the SAS bridge is assessed (approximately 5.2 seconds). In a plot of deck length versus period, there is an almost linear relationship with periods of other regular suspension bridges, such as the Golden Gate Bridge and the Carquinez Bridge, both in the San Francisco Bay.
Throughout Hawai'i, fishponds are considered by their local communities as important cultural touchstones, a source of local, sustainably produced food, and an important component to the development of community-based management for nearshore fisheries. Within Kaloko-Honokōhau National Historical Park, the restoration of Kaloko Fishpond for traditional aquaculture management is a goal of both the National Park Service (NPS) and Hui Kaloko-Honokōhau, a community-based group of kia'i, i.e., caretakers and native Hawaiian cultural practitioners. However, existing data on the demographics and condition of the fish populations within the pond, and the fish-habitat quality are poor to non-existent. Therefore, the objectives of this study were to: catalog fish species composition and distribution in the pond; estimate the abundance of focal species/taxonomic groups; and evaluate the occupancy patterns of the invasive algae Acanthophora spicifera and Upside-down Jellyfish Cassiopea andromeda. As part of these objectives, a survey protocol and analysis framework were designed and evaluated to ensure that the NPS and community group would be able to refine and implement them to continue their monitoring efforts. We conducted dual-observer shore-based visual surveys multiple times per week during September-October 2020 and April-September 2022. A total of 41 species/taxonomic groups were recorded over the course of the surveys. The largest number of species/taxonomic groups were observed at survey stations located on or near the kuapā, or wall separating the fishpond from the ocean. N-mixture models fitted to the data estimated a total population of 353 – 392 mullets, 134 – 192 flagtails (āholehole), and 189 – 277 Milkfish (Awa) Chanos chanos occurring within the 1.2-ha portion of Kaloko Fishpond that could be surveyed visually from the shoreline. Multi-season occupancy models fitted to the surveyed presence of A. spicifera and Upside-down Jellyfish indicted sites throughout most of the pond exhibited moderate and consistent occupancy (ψ = 0.30 – 0.40) throughout much of the pond, except for the northeast corner of the pond (Kaloko Iki) where colonization rates were lower and extinction rates higher than other areas within Kaloko. The visual survey method developed for this study provides a low-cost and effective starting point for the development of methodology that can be used both by NPS personnel and volunteers from the community group. However, we were only able to estimate fish populations for approximately 24% of the area of Kaloko Fishpond with this method. Given that the deeper areas of Kaloko Fishpond are completely inaccessible to the visual survey method used, generating population estimates for the entire pond based on the parameters estimated in the current study is not recommended without further investigation into fish movement and habitat use. Various means to refine this protocol to better meet the needs and abilities of the NPS and community group are proposed.
","language":"English","publisher":"University of Hawai'i","usgsCitation":"Grabowski, T.B., Tabandera, R., Greenwald, N., and Larson, A., 2024, Assessing habitat use and population dynamics of fisheries resources at Kaloko Fishpond: Hawai’i Cooperative Fishery Research Unit Technical Report Series HCFRU-003, 80 p.","productDescription":"80 p.","ipdsId":"IP-154335","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494691,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/43639","linkFileType":{"id":5,"text":"html"}},{"id":494905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kalako Fishpond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.03516036623958,\n 19.68986132494203\n ],\n [\n -156.03516036623958,\n 19.686419593600434\n ],\n [\n -156.0305731643826,\n 19.686419593600434\n ],\n [\n -156.0305731643826,\n 19.68986132494203\n ],\n [\n -156.03516036623958,\n 19.68986132494203\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tabandera, Ricky","contributorId":360473,"corporation":false,"usgs":false,"family":"Tabandera","given":"Ricky","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greenwald, Nathaniel","contributorId":360476,"corporation":false,"usgs":false,"family":"Greenwald","given":"Nathaniel","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Annie","contributorId":360479,"corporation":false,"usgs":false,"family":"Larson","given":"Annie","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947094,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256788,"text":"70256788 - 2024 - Low-flow period seasonality, trends, and climate linkages across the United States","interactions":[],"lastModifiedDate":"2024-08-13T14:41:37.568761","indexId":"70256788","displayToPublicDate":"2024-07-31T09:46:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Low-flow period seasonality, trends, and climate linkages across the United States","docAbstract":"Low-flow period properties, including timing, magnitude, and duration, influence many key processes for water resource managers and ecosystems. We computed annual low-flow period duration and timing metrics from 1951 to 2020 for 1032 conterminous United States (CONUS) streamgages and analyzed spatial patterns, trends through time, and relationships to climate. Results show northwestern and eastern CONUS streamgages had longer and more inter-annually consistent low-flow period durations, while central CONUS periods were shorter and more variable. Low-flow periods most often occurred in summer months but start and end dates occurred later in north-central and mountainous western CONUS, which have the greatest number of low flows during cold seasons. Low-flow periods are becoming longer in southeastern and northwestern CONUS but shorter in much of the rest of CONUS. Temperature was correlated with low-flow period duration in southeastern and northwestern CONUS, and precipitation was correlated with duration everywhere, but most strongly in eastern CONUS.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02626667.2024.2369639","usgsCitation":"Simeone, C., McCabe, G.J., Hecht, J.S., Hammond, J., Hodgkins, G.A., Olson, C.G., Wieczorek, M., and Wolock, D.M., 2024, Low-flow period seasonality, trends, and climate linkages across the United States: Hydrological Sciences Journal, v. 69, no. 10, p. 1387-1398, https://doi.org/10.1080/02626667.2024.2369639.","productDescription":"12 p.","startPage":"1387","endPage":"1398","ipdsId":"IP-144967","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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Center","active":true,"usgs":true}],"preferred":true,"id":908952,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olson, Carolyn G. 0000-0002-4198-6158","orcid":"https://orcid.org/0000-0002-4198-6158","contributorId":302954,"corporation":false,"usgs":true,"family":"Olson","given":"Carolyn","email":"","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":908953,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":908954,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":908955,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268897,"text":"70268897 - 2024 - Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire","interactions":[],"lastModifiedDate":"2025-07-10T14:01:02.446404","indexId":"70268897","displayToPublicDate":"2024-07-31T08:53:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire","docAbstract":"Movement plays a key role in allowing animal species to adapt to sudden environmental shifts. Anthropogenic climate and land use change have accelerated the frequency of some of these extreme disturbances, including megafire. These megafires dramatically alter ecosystems and challenge the capacity of several species to adjust to a rapidly changing landscape. Ungulates and their movement behaviors play a central role in the ecosystem functions of fire-prone ecosystems around the world. Previous work has shown behavioral plasticity is an important mechanism underlying whether large ungulates are able to adjust to recent changes in their environments effectively. Ungulates may respond to the immediate effects of megafire by adjusting their movement and behavior, but how these responses persist or change over time following disturbance is poorly understood.
We examined how an ecologically dominant ungulate with strong site fidelity, Columbian black-tailed deer (Odocoileus hemionus columbianus), adjusted its movement and behavior in response to an altered landscape following a megafire. To do so, we collected GPS data from 21 individual female deer over the course of a year to compare changes in home range size over time and used resource selection functions (RSFs) and hidden Markov movement models (HMMs) to assess changes in behavior and habitat selection.
We found compelling evidence of adaptive capacity across individual deer in response to megafire. Deer avoided exposed and severely burned areas that lack forage and could be riskier for predation immediately following megafire, but they later altered these behaviors to select areas that burned at higher severities, potentially to take advantage of enhanced forage.
These results suggest that despite their high site fidelity, deer can navigate altered landscapes to track rapid shifts in encounter risk with predators and resource availability. This successful adjustment of movement and behavior following extreme disturbance could help facilitate resilience at broader ecological scales.
","language":"English","publisher":"BMC","doi":"10.1186/s40462-024-00488-4","usgsCitation":"Calhoun, K., Connor, T., Gaynor, K., Van Scoyoc, A., Mcinturff, M.C., Kreling, S., and Brashares, J., 2024, Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire: Movement Ecology, v. 12, 53, 15 p., https://doi.org/10.1186/s40462-024-00488-4.","productDescription":"53, 15 p.","ipdsId":"IP-147496","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00488-4","text":"Publisher Index Page"},{"id":492008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Mendocino County","otherGeospatial":"Hopland Research and Extension Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.19199932451019,\n 39.34410383705571\n ],\n [\n -123.19199932451019,\n 38.95674957822277\n ],\n [\n -122.6413335916133,\n 38.95674957822277\n ],\n [\n -122.6413335916133,\n 39.34410383705571\n ],\n [\n -123.19199932451019,\n 39.34410383705571\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Calhoun, Kendall L.","contributorId":357766,"corporation":false,"usgs":false,"family":"Calhoun","given":"Kendall L.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Thomas","contributorId":357767,"corporation":false,"usgs":false,"family":"Connor","given":"Thomas","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaynor, Kaitlyn M.","contributorId":357768,"corporation":false,"usgs":false,"family":"Gaynor","given":"Kaitlyn M.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":942543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Scoyoc, Amy","contributorId":357769,"corporation":false,"usgs":false,"family":"Van Scoyoc","given":"Amy","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mcinturff, Michael C 0000-0002-4858-1292","orcid":"https://orcid.org/0000-0002-4858-1292","contributorId":337290,"corporation":false,"usgs":true,"family":"Mcinturff","given":"Michael","email":"","middleInitial":"C","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":942545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kreling, Samantha E.S.","contributorId":357770,"corporation":false,"usgs":false,"family":"Kreling","given":"Samantha E.S.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":942546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brashares, Justin S.","contributorId":357771,"corporation":false,"usgs":false,"family":"Brashares","given":"Justin S.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942547,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257022,"text":"70257022 - 2024 - Forecasting inundation of catastrophic landslides from precursory creep","interactions":[],"lastModifiedDate":"2024-08-07T11:48:20.121781","indexId":"70257022","displayToPublicDate":"2024-07-31T06:47:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting inundation of catastrophic landslides from precursory creep","docAbstract":"Forecasting landslide inundation upon catastrophic failure is crucial for reducing casualties, yet it remains a long-standing challenge owing to the complex nature of landslides. Recent global studies indicate that catastrophic hillslope failures are commonly preceded by a period of precursory creep, motivating a novel scheme to foresee their hazard. Here, we showcase an approach to hindcast landslide inundation by linking satellite-captured precursory displacements to modeling of consequent granular-fluid flows. We present its application to the 2021 Chunchi, Ecuador landslide, which failed catastrophically and evolved into a mobile debris flow after four months of precursory creep, destroying 68 homes along its lengthy flow path. Underpinned by uncertainty quantification and in situ validations, we highlight the feasibility and potential of forecasting landslide inundation damage using observable precursors. This forecast approach is broadly applicable for flow hazards initiated from geomaterial failures.
Unraveling how Global Mean Sea Level (GMSL) fluctuated during past warm periods can improve our understanding of linkages between sea-level fluctuations, orbital forcing, and ice-sheet dynamics. Current estimates of GMSL for Marine Isotope Stages (MIS) 5a and 5c — two warm intervals following the relatively well-documented MIS 5e — contain meters of uncertainty and fewer data due to several challenges. These challenges include concealment of datable in-situ coral facies by MIS 1 deposits and inaccessibility due to submergence by modern sea level. We present a comprehensive dataset based on U–Th dating and stratigraphic correlation of 23 cores totaling over 170 m of recovered coral-reef deposits across the tectonically stable Florida Keys Reef Tract (FKRT). Following detailed facies descriptions, 34 in-situ, minimally altered aragonitic coral samples (≤2.7% calcite) below the Holocene-Pleistocene boundary were targeted for U–Th geochronology. Fourteen closed-system coral U–Th ages from MIS 5a include the commonly used sea-level indicator Acropora palmata, but also the massive coral taxa Pseudodiploria strigosa, Siderastrea siderea, Orbicella spp., and Porites astreoides. Dating yielded ages in the range of 88–81 ka (average 2σ uncertainty of less than 200 years). These ages suggest MIS 5a reef initiation at ∼88 ka BP, a peak near 83 ka with minimum elevations between −6.0 ± 0.5 and −5.6 ± 0.5 m MSL (2σ uncertainty and subsidence-corrected), and reef termination and sea-level fall by ∼81 ka BP. Notably, the range of peak MIS 5a relative sea-level estimates of −6.5 to −5.1 m MSL are more than 2 m shallower (higher) than previous estimates of −11 to −9 m. Our higher resolution regional sea-level reconstruction across four subregions of the Florida Keys reef tract aligns with changes in July insolation at 65° N: a trend that most other records, such as deep-sea sediments, do not have the accuracy and precision to resolve. Three massive coral samples from MIS 5c, consisting of Pseudodiploria clivosa, and Orbicella spp., yielded ages in the range of 104 to 99 ka (average 2σ uncertainty less than 200 years); however, because only one sample met the closed-system criteria, our ability to estimate MIS 5c sea level is relatively limited. More empirical estimates of sea-level from the MIS 5a and MIS 5c intervals based on numerical dating of reliable local sea-level constraints are critical for GMSL calculations and relating changes in sea-level amplitude and timing to global ice volume modeling and glacio-isostatic effects, all of which can improve predictions of future sea-level changes in coastal regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2024.100222","usgsCitation":"Hsia, S., Toth, L., Mortlock, R.A., and Kerans, C., 2024, Re-evaluating Marine Isotope Stage 5a paleo-sea-level trends from across the Florida Keys reef tract: Quaternary Science Advances, v. 15, 100222, 17 p., https://doi.org/10.1016/j.qsa.2024.100222.","productDescription":"100222, 17 p.","ipdsId":"IP-166304","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466974,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2024.100222","text":"Publisher Index Page"},{"id":462598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.23876328908999,\n 26.097759348420453\n ],\n [\n -83.46117316122947,\n 26.097759348420453\n ],\n [\n -83.46117316122947,\n 24.102407845404713\n ],\n [\n -79.23876328908999,\n 24.102407845404713\n ],\n [\n -79.23876328908999,\n 26.097759348420453\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hsia, Scarlette 0000-0002-2230-9004","orcid":"https://orcid.org/0000-0002-2230-9004","contributorId":339740,"corporation":false,"usgs":false,"family":"Hsia","given":"Scarlette","email":"","affiliations":[{"id":39890,"text":"University of Texas at Austin, Jackson School of Geosciences","active":true,"usgs":false}],"preferred":false,"id":914916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":914917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mortlock, Richard A.","contributorId":299718,"corporation":false,"usgs":false,"family":"Mortlock","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":914918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kerans, Charles","contributorId":75838,"corporation":false,"usgs":false,"family":"Kerans","given":"Charles","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":914919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257097,"text":"70257097 - 2024 - Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands","interactions":[],"lastModifiedDate":"2024-08-13T14:43:37.410358","indexId":"70257097","displayToPublicDate":"2024-07-30T08:21:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands","docAbstract":"Public lands across the United States are managed for multiple uses, resources, and values ranging from energy development to rare plant conservation. Intensified energy development and other land use changes across the Southwestern United States have increased the need for proactive management to mitigate impacts to rare plants. Habitat suitability models can inform decision-making and lead to more effective conservation of rare plants and their habitats, but high-quality models that are suited for use at local scales are lacking for many species. Our team of scientists and managers developed ensembles of habitat suitability models for five rare plant species in New Mexico using a coproduced, iterative framework complemented by comprehensive ground truthing and tailoring of products for use in public land decisions. Our process resulted in substantial differences from initial models through changes to environmental predictors, species occurrence and background data, and development of new species-specific predictors. Involving species experts and end users in model development can strengthen the process and resulting model and build understanding and trust in final products. Both factors can promote use of models to inform public land permitting and planning decisions that may affect rare plants, including by guiding development away from highly suitable habitats.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13179","usgsCitation":"Jarnevich, C.S., Carter, S.K., Davidson, Z.M., MacPhee, N.D., Alexander, P.J., Hayes, B., Belmaric, P.N., and Harms, B., 2024, Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands: Conservation Science and Practice, v. 6, no. 8, e13179, 15 p., https://doi.org/10.1111/csp2.13179.","productDescription":"e13179, 15 p.","ipdsId":"IP-158708","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439242,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13179","text":"Publisher Index Page"},{"id":432438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.69579033294093,\n 38.37268084260387\n ],\n [\n -108.69579033294093,\n 35.269919346315746\n ],\n [\n -103.45845941486846,\n 35.269919346315746\n ],\n [\n -103.45845941486846,\n 38.37268084260387\n ],\n [\n -108.69579033294093,\n 38.37268084260387\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davidson, Zoe M. 0000-0003-2043-8598","orcid":"https://orcid.org/0000-0003-2043-8598","contributorId":336894,"corporation":false,"usgs":false,"family":"Davidson","given":"Zoe","email":"","middleInitial":"M.","affiliations":[{"id":80903,"text":"Bureau of Land Management Headquarters","active":true,"usgs":false}],"preferred":false,"id":909382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacPhee, Nicole D.","contributorId":337152,"corporation":false,"usgs":false,"family":"MacPhee","given":"Nicole","email":"","middleInitial":"D.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":909383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexander, Patrick J.","contributorId":337153,"corporation":false,"usgs":false,"family":"Alexander","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":909384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayes, Brandon","contributorId":337154,"corporation":false,"usgs":false,"family":"Hayes","given":"Brandon","email":"","affiliations":[{"id":80983,"text":"Student Services Contractor to USGS FORT","active":true,"usgs":false}],"preferred":false,"id":909385,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Belmaric, Pairsa N.","contributorId":337156,"corporation":false,"usgs":false,"family":"Belmaric","given":"Pairsa","email":"","middleInitial":"N.","affiliations":[{"id":80983,"text":"Student Services Contractor to USGS FORT","active":true,"usgs":false}],"preferred":false,"id":909386,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harms, Benjamin R","contributorId":267283,"corporation":false,"usgs":false,"family":"Harms","given":"Benjamin R","affiliations":[],"preferred":false,"id":909387,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70258336,"text":"70258336 - 2024 - Resilient riverine social–ecological systems: A new paradigm to meet global conservation targets","interactions":[],"lastModifiedDate":"2024-11-04T19:39:44.275519","indexId":"70258336","displayToPublicDate":"2024-07-29T09:30:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"Resilient riverine social–ecological systems: A new paradigm to meet global conservation targets","docAbstract":"The United Nations' Convention on Biological Diversity set forth the 30 × 30 target, an agenda for countries to protect at least 30% of their terrestrial, inland water, and coastal and marine areas by 2030. With <6 years to reach that goal, riverine conservation professionals are faced with the difficult decision of prioritizing which rivers or river segments should be conserved (protected and/or restored). While incorporating resilience into conservation planning is essential for enhancing, restoring, and maintaining the vital riverine ecosystem services (ES) most threatened by climate change and other environmental and human stresses, this paradigm is at odds with traditional conservation approaches that are either opportunistic or reactionary, where only unique and highly visible ecosystems have been prioritized. Barriers to implementing resilience-based riverine conservation planning include: (1) difficulties in conceptualizing and quantifying resilience; (2) insufficient consideration of the social components of riverine systems; (3) the inapplicability of terrestrial-only conservation models to aquatic systems; and (4) the traditional ad hoc and opportunistic approach to conservation. To overcome these barriers, we propose a resilience-based riverine conservation framework that includes: (1) assessing riverine resilience using indicator frameworks; (2) considering rivers as dynamically coupled social–ecological systems; (3) explicitly incorporating terrestrial–aquatic network connectivity into conservation decision-making; and (4) strategic systems planning using a novel resilience–conservation matrix as a tool. This framework has the potential to transform conservation practices around the globe to more effectively protect river systems and enhance their resilience to climate change and human development.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1753","usgsCitation":"Perry, D.M., Praskievicz, S.J., McManamay, R., Saxena, A., Grimm, K.C., Zegre, N., Bair, L., Ruddell, B., and Rushforth, R., 2024, Resilient riverine social–ecological systems: A new paradigm to meet global conservation targets: WIREs Water, v. 11, no. 6, e1753, 22 p., https://doi.org/10.1002/wat2.1753.","productDescription":"e1753, 22 p.","ipdsId":"IP-155185","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":439243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1753","text":"Publisher Index Page"},{"id":433693,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Denielle M.","contributorId":215885,"corporation":false,"usgs":false,"family":"Perry","given":"Denielle","email":"","middleInitial":"M.","affiliations":[{"id":39324,"text":"School of Earth and Sustainability, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":912928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Praskievicz, Sarah J. 0000-0002-9380-7625","orcid":"https://orcid.org/0000-0002-9380-7625","contributorId":245989,"corporation":false,"usgs":false,"family":"Praskievicz","given":"Sarah","email":"","middleInitial":"J.","affiliations":[{"id":49396,"text":"University of North Carolina-Greensboro","active":true,"usgs":false}],"preferred":false,"id":912929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McManamay, Ryan","contributorId":205277,"corporation":false,"usgs":false,"family":"McManamay","given":"Ryan","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":912930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saxena, Alark","contributorId":344137,"corporation":false,"usgs":false,"family":"Saxena","given":"Alark","email":"","affiliations":[{"id":82298,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ 86001","active":true,"usgs":false}],"preferred":false,"id":912931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grimm, K. C.","contributorId":173997,"corporation":false,"usgs":false,"family":"Grimm","given":"K.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":912932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zegre, Nicholas","contributorId":344138,"corporation":false,"usgs":false,"family":"Zegre","given":"Nicholas","email":"","affiliations":[{"id":82301,"text":"Forestry & Natural Resources, West Virginia University, Morgantown, WV 25606","active":true,"usgs":false}],"preferred":false,"id":912933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bair, Lucas 0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":912934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ruddell, Benjamin L.","contributorId":247513,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin L.","affiliations":[{"id":49567,"text":"Northern Arizona University, Professor","active":true,"usgs":false}],"preferred":false,"id":912935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":912936,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70258229,"text":"70258229 - 2024 - 3-D geological modeling for numerical flow simulation studies of gas hydrate reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope","interactions":[],"lastModifiedDate":"2024-09-09T14:11:34.989533","indexId":"70258229","displayToPublicDate":"2024-07-29T07:09:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1513,"text":"Energy and Fuels","active":true,"publicationSubtype":{"id":10}},"title":"3-D geological modeling for numerical flow simulation studies of gas hydrate reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope","docAbstract":"Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. This study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-step gridding were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a 3-D trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.
Identifying areas expected to remain buffered from climate change and maintain biodiversity and ecological function (i.e., climate refugia) is important for climate adaptation planning. As structurally diverse transitional zones between terrestrial and aquatic environments, riparian areas are often biological hotspots and provide critical corridors for species movement, particularly in arid and semi-arid regions. In our study region in the western and central USA, identifying riparian areas that could serve as climate refugia is a priority for wildlife managers. We mapped areas with connected riparian habitats that, based on landscape diversity and projected changes in summer temperatures and landscape runoff, are expected to serve as climate refugia. To incorporate uncertainty and balance the need for near- and long-term planning, we mapped potential refugia for 2 future time periods (2040–2069, 2070–2099) based on 2 climate models that represented divergent but plausible climate outcomes. The approach we developed is not constrained by physiology or behavior of target species and can be used to identify areas expected to fare comparatively well under a wide range of future climate scenarios. Our approach can also be used to identify areas where restoration could increase riparian connectedness and climate resilience.
Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.
Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.
Silver Carp (Hypophthalmichthys molitrix) populations have established and expanded throughout the lower Mississippi River basin (LMRB). Information pertaining to Silver Carp population mixing among rivers within the LMRB is lacking. Documented relations between Silver Carp otolith and river water barium (Ba) and strontium (Sr) microchemical signatures may enable estimation of origins of Silver Carp in the LMRB. Replicate water samples and otoliths from 308 Silver Carp were collected from the Cache, Arkansas, White, Yazoo, St. Francis, L’Anguille, and Mississippi rivers, and Merrisach Lake (situated along a canal connecting the lower reaches of the Arkansas and White rivers) within the LMRB. Water and carp otolith microchemical signatures exhibited consistent differences among water bodies. A classification and regression tree model exhibited 80% accuracy when assigning carp collected from the White, Arkansas, and Mississippi rivers based on fish-water microchemical signatures. Model accuracy decreased as smaller rivers were incorporated into models. Predicted natal origin based on otolith microchemical signatures suggested the White River (43%) and the lower Mississippi River (39%) were the likely origins for ~ 82% of the Silver Carp sampled. Despite the prevalence of adult Silver Carp within the Arkansas River system, fewer (18%) appeared to have originated there compared to the White and Mississippi rivers. Long-term water sampling and additional isotopic measurements may refine analyses to better determine the relative contributions of Silver Carp from the smaller river systems. Population mixing of Silver Carp among tributary rivers appears to be common within the LMRB, and removal efforts may benefit from evaluating the magnitude of fish movement and connectivity among rivers.
Assisted migration is a means of introducing a species into a previously unoccupied area. Although this idea is relatively new for many species, there are many extant examples involving fish that can be instructive. We studied a case of assisted migration where upstream access of migrating adult coho salmon Oncorhynchus kisutch over a naturally impassible barrier was established through construction of fish ladders. Although these passage structures have successfully allowed coho salmon to colonize upstream locations, managers had concerns regarding how efficiently these structures passed fish, as well as questions regarding access to specific upstream habitats, and passage barriers further upstream. To address these concerns, we developed a stage-based population model to explore: (1) influences of passage over structures, (2) rearing habitats upstream of the structures, and (3) consequences of additional barriers to passage in the system. Model simulations suggest high fish passage at the ladders was associated with the highest smolt and adult abundance of coho salmon. The importance of passage was strongly influenced by juveniles rearing in a lake, where increased lake rearing at each passage scenario increased abundance of smolts and adults. Opening habitat further upstream was estimated to increase adult and smolt abundance up to 12%. Results of model simulations also helped to identify uncertainties that could be evaluated further (e.g., juvenile rearing in the lake). In general, our findings point to the importance of considering a full range of processes that can drive expected outcomes for assisted migration.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4355","usgsCitation":"Benjamin, J.R., Dunham, J., Scheidt, N., Rothenbuecher, C., and Sipher, C., 2024, Assisted migration of coho salmon: Influences of passage and habitat availability on population dynamics: River Research and Applications, v. 40, no. 10, p. 2009-2021, https://doi.org/10.1002/rra.4355.","productDescription":"13 p.","startPage":"2009","endPage":"2021","ipdsId":"IP-158160","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":498297,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4355","text":"Publisher Index Page"},{"id":431563,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.29673317980877,\n 44.305114347462364\n ],\n [\n -123.71555076750579,\n 44.305114347462364\n ],\n [\n -123.71555076750579,\n 43.946813166036065\n ],\n [\n -123.29673317980877,\n 43.946813166036065\n ],\n [\n -123.29673317980877,\n 44.305114347462364\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":907105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":907106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheidt, Nicholas","contributorId":298910,"corporation":false,"usgs":false,"family":"Scheidt","given":"Nicholas","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":907107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rothenbuecher, Carla","contributorId":340405,"corporation":false,"usgs":false,"family":"Rothenbuecher","given":"Carla","email":"","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":907108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sipher, Cory","contributorId":340406,"corporation":false,"usgs":false,"family":"Sipher","given":"Cory","email":"","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":907109,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256563,"text":"70256563 - 2024 - Little brown bats (Myotis lucifugus) are resistant to SARS-CoV-2 infection","interactions":[],"lastModifiedDate":"2024-10-24T11:05:47.527143","indexId":"70256563","displayToPublicDate":"2024-07-26T09:23:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Little brown bats (Myotis lucifugus) are resistant to SARS-CoV-2 infection","title":"Little brown bats (Myotis lucifugus) are resistant to SARS-CoV-2 infection","docAbstract":"It has been proposed that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that spread through human populations as a pandemic originated in Asian bats. There is concern that infected humans could transmit the virus to native North American bats; therefore, the susceptibility of several North American bat species to the pandemic virus has been experimentally assessed. Big brown bats (Eptesicus fuscus) were shown to be resistant to infection by SARS-CoV-2, whereas Mexican free-tailed bats (Tadarida brasiliensis) became infected and orally excreted moderate amounts of virus for up to 18 d postinoculation. Little brown bats (Myotis lucifugus) frequently contact humans, and their populations are threatened over much of their range due to white-nose syndrome, a fungal disease that is continuing to spread across North America. We experimentally challenged little brown bats with SARS-CoV-2 to determine their susceptibility and host potential and whether the virus presents an additional risk to this species. We found that this species was resistant to infection by SARS-CoV-2. These findings provide reassurance to wildlife rehabilitators, biologists, conservation scientists, and the public at large who are concerned with possible transmission of this virus to threatened bat populations.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/JWD-D-23-00114","usgsCitation":"Hall, J.S., Nashold, S., Hofmeister, E.K., Leon, A.E., Falendysz, E., Ip, H., Malave, C.M., Rocke, T.E., Carossino, M., Balasuriya, U.B., and Knowles, S., 2024, Little brown bats (Myotis lucifugus) are resistant to SARS-CoV-2 infection: Journal of Wildlife Diseases, v. 60, no. 4, p. 924-930, https://doi.org/10.7589/JWD-D-23-00114.","productDescription":"7 p.","startPage":"924","endPage":"930","ipdsId":"IP-154474","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":432027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Jeffrey S. 0000-0001-5599-2826 jshall@usgs.gov","orcid":"https://orcid.org/0000-0001-5599-2826","contributorId":2254,"corporation":false,"usgs":true,"family":"Hall","given":"Jeffrey","email":"jshall@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nashold, Sean 0000-0002-8869-6633","orcid":"https://orcid.org/0000-0002-8869-6633","contributorId":341157,"corporation":false,"usgs":false,"family":"Nashold","given":"Sean","affiliations":[{"id":56047,"text":"USGS National Wildlife Health Center","active":true,"usgs":false}],"preferred":false,"id":908013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hofmeister, Erik K. 0000-0002-2305-519X ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-2305-519X","contributorId":269350,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leon, Ariel Elizabeth 0000-0001-9246-4619","orcid":"https://orcid.org/0000-0001-9246-4619","contributorId":247573,"corporation":false,"usgs":true,"family":"Leon","given":"Ariel","email":"","middleInitial":"Elizabeth","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falendysz, Elizabeth 0000-0003-2895-8918 efalendysz@usgs.gov","orcid":"https://orcid.org/0000-0003-2895-8918","contributorId":127751,"corporation":false,"usgs":true,"family":"Falendysz","given":"Elizabeth","email":"efalendysz@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ip, Hon S. 0000-0003-4844-7533","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":126815,"corporation":false,"usgs":true,"family":"Ip","given":"Hon S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908017,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Malave, Carly M. 0000-0001-6673-737X","orcid":"https://orcid.org/0000-0001-6673-737X","contributorId":341158,"corporation":false,"usgs":false,"family":"Malave","given":"Carly","email":"","middleInitial":"M.","affiliations":[{"id":56047,"text":"USGS National Wildlife Health Center","active":true,"usgs":false}],"preferred":false,"id":908018,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908019,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carossino, Mariano","contributorId":245857,"corporation":false,"usgs":false,"family":"Carossino","given":"Mariano","email":"","affiliations":[],"preferred":false,"id":908020,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Balasuriya, Udeni B.R.","contributorId":245862,"corporation":false,"usgs":false,"family":"Balasuriya","given":"Udeni","email":"","middleInitial":"B.R.","affiliations":[],"preferred":false,"id":908021,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":908022,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70257712,"text":"70257712 - 2024 - Modeling the effects of spatial distribution on dynamics of an invading Melaleuca quinquenervia (Cav.) Blake population","interactions":[],"lastModifiedDate":"2024-08-23T14:11:57.191191","indexId":"70257712","displayToPublicDate":"2024-07-26T09:08:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Modeling the effects of spatial distribution on dynamics of an invading Melaleuca quinquenervia (Cav.) Blake population","title":"Modeling the effects of spatial distribution on dynamics of an invading Melaleuca quinquenervia (Cav.) Blake population","docAbstract":"To predict the potential success of an invading non-native species, it is important to understand its dynamics and interactions with native species in the early stages of its invasion. In spatially implicit models, mathematical stability criteria are commonly used to predict whether an invading population grows in number in an early time period. But spatial context is important for real invasions as an invading population may first occur as a small number of individuals scatter spatially. The invasion dynamics are therefore not describable in terms of population level state variables. A better approach is spatially explicit individual-based modeling (IBM). We use an established spatially explicit IBM to predict the invasion of the non-native tree, Melaleuca quinquenervia (Cav.) Blake, to a native community in southern Florida. We show that the initial spatial distribution, both the spatial density of individuals and the area they cover, affects its success in growing numerically and spreading. The formation of a cluster of a sufficient number and density of individuals may be needed for the invader to locally outcompete the native species and become established. Different initial densities, identical in number and density but differing in random positions of individuals, can produce very different trajectories of the invading population through time, even affecting invasion success and failure.
","language":"English","publisher":"MDPI","doi":"10.3390/f15081308","usgsCitation":"Lu, Y., Xia, J., Holt, R., and DeAngelis, D., 2024, Modeling the effects of spatial distribution on dynamics of an invading Melaleuca quinquenervia (Cav.) Blake population: Forests, v. 15, no. 8, 1308, 18 p., https://doi.org/10.3390/f15081308.","productDescription":"1308, 18 p.","ipdsId":"IP-167809","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439250,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f15081308","text":"Publisher Index Page"},{"id":433094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Lu, Yuanming","contributorId":298492,"corporation":false,"usgs":false,"family":"Lu","given":"Yuanming","email":"","affiliations":[{"id":35560,"text":"Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":911490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xia, Junfei","contributorId":298493,"corporation":false,"usgs":false,"family":"Xia","given":"Junfei","email":"","affiliations":[{"id":64593,"text":"Rosenstiel School of Marine and Atmospheric Science, University of Miami","active":true,"usgs":false}],"preferred":false,"id":911491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holt, Robert D.","contributorId":343600,"corporation":false,"usgs":false,"family":"Holt","given":"Robert D.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":911492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":217986,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":911493,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256393,"text":"70256393 - 2024 - Reversal in estuarine sand supply driven by Holocene sea level rise: A model for sand transport in large structural estuaries, San Francisco Bay, California, USA","interactions":[],"lastModifiedDate":"2024-07-30T11:41:41.12318","indexId":"70256393","displayToPublicDate":"2024-07-26T06:39:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Reversal in estuarine sand supply driven by Holocene sea level rise: A model for sand transport in large structural estuaries, San Francisco Bay, California, USA","docAbstract":"