We present a case study using trapping data and species accumulation theory to assess the sampling effort needed to detect species that are rare in habitats sampled as part of the management of invasive European green crab Carcinus maenas on the coast of Washington State and Salish Sea shorelines, such as is desired for early detection of invasive species. In response to detections of green crab on the west coast of North America monitoring efforts, including early detection monitoring, have increased. The goal of invasive species early detection monitoring programs is to detect new infestations soon after introduction so that eradication and quarantine measures can be used to control their spread. However, detection of newly introduced invasive species often occurs after populations are large and well established. The ability to detect newly introduced invasive species is affected by sampling procedures, including how much effort is expended. To assess the level of trapping effort needed to detect green crab and other taxa when they are rare, we calculate sample-based rarefaction curves, total species richness, and estimates of sample completeness. We then use these estimates to describe the relationship between sampling effort and detection at two different spatial scales. Our results suggest that high probability early detection of green crabs or other rare taxa in many coastal waterbodies will require significantly more trapping effort than was expended in 2020. Our analyses further suggest that the effort required to provide for early detection was less at the site-specific spatial scale than at the waterbody scale. However, sample completeness at the site-specific spatial scale was negatively correlated with species richness. Current efforts to look for new green crab populations and to manage existing populations provide an opportunity to look for other invasive organisms that may establish in similar habitats.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2024.15.2.02","usgsCitation":"Counihan, T., and Thom, T., 2024, How much trapping effort is needed for early detection of European green crab?: Management of Biological Invasions, v. 15, no. 2, p. 187-200, https://doi.org/10.3391/mbi.2024.15.2.02.","productDescription":"14 p.","startPage":"187","endPage":"200","ipdsId":"IP-157261","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":439465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2024.15.2.02","text":"Publisher Index Page"},{"id":430891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.23804807588527,\n 47.163183758898896\n ],\n [\n -124.23804807588527,\n 46.32149165184737\n ],\n [\n -123.76087422518893,\n 46.32149165184737\n ],\n [\n -123.76087422518893,\n 47.163183758898896\n ],\n [\n -124.23804807588527,\n 47.163183758898896\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.56864290373127,\n 48.64237528593006\n ],\n [\n -122.56864290373127,\n 48.5481969623774\n ],\n [\n -122.40987717111287,\n 48.5481969623774\n ],\n [\n -122.40987717111287,\n 48.64237528593006\n ],\n [\n -122.56864290373127,\n 48.64237528593006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.69140002305605,\n 48.9958606425881\n ],\n [\n -122.83379815437331,\n 48.9958606425881\n ],\n [\n -122.83379815437331,\n 48.9034195723365\n ],\n [\n -122.69140002305605,\n 48.9034195723365\n ],\n [\n -122.69140002305605,\n 48.9958606425881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":905951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thom, Theresa","contributorId":224436,"corporation":false,"usgs":false,"family":"Thom","given":"Theresa","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":905952,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70254763,"text":"70254763 - 2024 - Identifying the forage base and critical forage taxa for Chesapeake waterbirds","interactions":[],"lastModifiedDate":"2024-12-09T15:33:46.850991","indexId":"70254763","displayToPublicDate":"2024-06-01T08:16:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying the forage base and critical forage taxa for Chesapeake waterbirds","docAbstract":"To effectively maximize the conservation value of management plans intended to capture ecosystem-wide health, it is essential to obtain an understanding of emergent patterns in dietary dynamics spanning many species. Chesapeake Bay, USA, is a critical ecosystem used annually by a diverse assortment of waterbird species, including several of conservation concern. However, the ecosystem is threatened by many ecological pressures driven largely by the dense human population of the surrounding region. These issues necessitate proactive monitoring and management efforts to track the health of ecosystems like the Chesapeake Bay. Such monitoring efforts of population dynamics require adequate data on the connections between trophic levels to understand how changes to the forage base might influence higher trophic levels, such as these diverse avian predators. However, we have historically lacked standardized quantitative data drawing these connections at the community level, as well as the relative importance of these taxa in the diet of such predators. We collated existing quantitative data on avian dietary composition to construct a database on the diets of 58 waterbird species that make use of the Chesapeake Bay. From this database, we quantified the relative importance of forage taxa to the diet of each waterbird species. Such data can enable managers to develop a comprehensive suite of forage taxa indicators whose abundance and distributions can be monitored as a proxy for ecosystem health. It is our goal that this database be harnessed as a tool to enable conservation practitioners to prioritize indicator taxa for monitoring purposes, contributing towards conservation plans that best address the health of the ecosystem at large.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-23-017","usgsCitation":"Hack, M., Sullivan, J.D., Kent, C.M., and Prosser, D., 2024, Identifying the forage base and critical forage taxa for Chesapeake waterbirds: Journal of Fish and Wildlife Management, v. 15, no. 1, p. 164-174, https://doi.org/10.3996/jfwm-23-017.","productDescription":"11 p.","startPage":"164","endPage":"174","ipdsId":"IP-151738","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":429631,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439468,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-017","text":"Publisher Index Page"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.96079553182784,\n 39.59556018695497\n ],\n [\n -76.96079553182784,\n 36.77431286315145\n ],\n [\n -75.58436805301858,\n 36.77431286315145\n ],\n [\n -75.58436805301858,\n 39.59556018695497\n ],\n [\n -76.96079553182784,\n 39.59556018695497\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hack, Matthew","contributorId":304036,"corporation":false,"usgs":false,"family":"Hack","given":"Matthew","email":"","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":902440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":902441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":902442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902443,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254557,"text":"70254557 - 2024 - A genomic hotspot of diversifying selection and structural change in the hoary bat (Lasiurus cinereus)","interactions":[],"lastModifiedDate":"2024-06-03T11:53:52.035227","indexId":"70254557","displayToPublicDate":"2024-05-31T06:51:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"A genomic hotspot of diversifying selection and structural change in the hoary bat (Lasiurus cinereus)","docAbstract":"Previous work found that numerous genes positively selected within the hoary bat (Lasiurus cinereus) lineage are physically clustered in regions of conserved synteny. Here I further validate and expand on those finding utilizing an updated L. cinereus genome assembly and additional bat species as well as other tetrapod outgroups.
A chromosome-level assembly was generated by chromatin-contact mapping and made available by DNAZoo (www.dnazoo.org). The genomic organization of orthologous genes was extracted from annotation data for multiple additional bat species as well as other tetrapod clades for which chromosome-level assemblies were available from the National Center for Biotechnology Information (NCBI). Tests of branch-specific positive selection were performed for L. cinereus using PAML as well as with the HyPhy package for comparison.
Twelve genes exhibiting significant diversifying selection in the L. cinereus lineage were clustered within a 12-Mb genomic window; one of these (Trpc4) also exhibited diversifying selection in bats generally. Ten of the 12 genes are landmarks of two distinct blocks of ancient synteny that are not linked in other tetrapod clades. Bats are further distinguished by frequent structural rearrangements within these synteny blocks, which are rarely observed in other Tetrapoda. Patterns of gene order and orientation among bat taxa are incompatible with phylogeny as presently understood, implying parallel evolution or subsequent reversals. Inferences of positive selection were found to be robust to alternative phylogenetic topologies as well as a strong shift in background nucleotide composition in some taxa.
This study confirms and further localizes a genomic hotspot of protein-coding divergence in the hoary bat, one that also exhibits an increased tempo of structural change in bats compared with other mammals. Most genes in the two synteny blocks have elevated expression in brain tissue in humans and model organisms, and genetic studies implicate the selected genes in cranial and neurological development, among other functions.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.17482","usgsCitation":"Cornman, R.S., 2024, A genomic hotspot of diversifying selection and structural change in the hoary bat (Lasiurus cinereus): PeerJ, v. 12, e17482, 32 p., https://doi.org/10.7717/peerj.17482.","productDescription":"e17482, 32 p.","ipdsId":"IP-159824","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439470,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.17482","text":"Publisher Index Page"},{"id":434952,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YKF5BJ","text":"USGS data release","linkHelpText":"DNA sequences used to analyze evolutionary rates of genes in bats"},{"id":429444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":901903,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254586,"text":"70254586 - 2024 - Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","interactions":[],"lastModifiedDate":"2024-06-04T11:50:56.137709","indexId":"70254586","displayToPublicDate":"2024-05-31T06:49:52","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","docAbstract":"Individual variation in movement strategies of foraging loggerhead turtles have been documented on the scale of tens to hundreds of kilometers within single ocean basins. Use of different strategies among individuals may reflect variations in resources, predation pressure or competition. It is less common for individual turtles to use different foraging strategies on the scale of kilometers within a single coastal bay. We used GPS tags capable of back-filling fine-scale locations to document movement patterns of loggerhead turtles in a coastal bay in Northwest Florida, U.S.A.
Iridium-linked GPS tags were deployed on loggerhead turtles at a neritic foraging site in Northwest Florida. After filtering telemetry data, point locations were transformed to movement lines and then merged with the original point file to define travel paths and assess travel speed. Home ranges were determined using kernel density function. Diurnal behavioral shifts were examined by examining turtle movements compared to solar time.
Of the 11 turtles tagged, three tracked turtles remained in deep (~ 6 m) water for almost the entire tracking period, while all other turtles undertook movements from deep water locations, located along edges and channels, to shallow (~ 1–2 m) shoals at regular intervals and primarily at night. Three individuals made short-term movements into the Gulf of Mexico when water temperatures dropped, and movement speeds in the Gulf were greater than those in the bay. Turtles exhibited a novel behavior we termed drifting.
This study highlighted the value provided to fine-scale movement studies for species such as sea turtles that surface infrequently by the ability of these GPS tags to store and re-upload data. Future use of these tags at other loggerhead foraging sites, and concurrent with diving and foraging data, would provide a powerful tool to better understand fine-scale movement patterns of sea turtles.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-024-00480-y","usgsCitation":"Lamont, M., Slone, D., Reid, J.P., Butler, S.M., and Alday, J.A., 2024, Deep vs shallow: GPS tags reveal a dichotomy in movement patterns of loggerhead turtles foraging in a coastal bay: Movement Ecology, v. 12, 40, 13 p.; Data Release, https://doi.org/10.1186/s40462-024-00480-y.","productDescription":"40, 13 p.; Data Release","ipdsId":"IP-162093","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439474,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00480-y","text":"Publisher Index Page"},{"id":434953,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TCY7X","text":"USGS data release","linkHelpText":"Loggerhead Iridium Locations in Northwest Florida 2019-2021"},{"id":430015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"St. Joseph Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.62202950232283,\n 29.94378242941025\n ],\n [\n -85.58665120898003,\n 29.585759941204998\n ],\n [\n -84.96846208319513,\n 29.460191772100785\n ],\n [\n -85.35762330996906,\n 29.938135428200454\n ],\n [\n -85.62202950232283,\n 29.94378242941025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":903491,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slone, Daniel 0000-0002-9903-9727","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":213750,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":903492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reid, James P. 0000-0002-8497-1132","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":206849,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":903493,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":903494,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alday, Joseph A. 0000-0003-2275-5458","orcid":"https://orcid.org/0000-0003-2275-5458","contributorId":296688,"corporation":false,"usgs":true,"family":"Alday","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":903495,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264767,"text":"70264767 - 2024 - Characterizing sulfur redox state and geochemical implications in deep-time using mineral chemistry network analysis","interactions":[],"lastModifiedDate":"2025-03-24T14:56:35.828612","indexId":"70264767","displayToPublicDate":"2024-05-30T09:50:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing sulfur redox state and geochemical implications in deep-time using mineral chemistry network analysis","docAbstract":"Sulfur (S) is a central element in global biogeochemical cycling and Earth’s redox evolution. Minerals that contain S are an important record of local environmental conditions at the time of their formation based on chemical speciation and redox. However, the oxidation state of S for hundreds of different S-containing minerals and thousands of S-containing mineral localities is unknown, largely sulfides and sulfosalts, and the redox state alone does not fully capture mineral chemistry diversity, thus limiting understanding of S redox evolution. Here, we use mineral chemistry network analysis and the weighted Mineral Element Electronegativity Coefficient of Variation (wMEECV) metric to investigate the element interactions and localities of S-containing minerals from the Mineral Evolution Database (MED) to infer the redox state of S in minerals where the redox state is unknown (SU). Louvain community detection of the S mineral chemistry redox network reveals that there are three main network communities that are separated by redox state. The S6+ community includes minerals that contain the S6+ redox state and a small number of S4+ and S2+ minerals, the S2− community includes S2−-containing minerals, and the SU community includes minerals in which the redox state of S is unknown. The wMEECV values of the SU community closely overlap with the wMEECV values of the S2− community, and do not overlap with the wMEECV values of the S6+ community, indicating the SU community minerals contain predominately reduced S. Assuming that SU community minerals contain reduced S, as supported by their network chemical associations and wMEECV values, then reduced S-containing minerals make up approximately 81 % of S-containing mineral localities in the S mineral chemistry network, even though the majority of all mineral localities (S-containing and non-S-containing) are oxygen (O)-containing minerals. Additionally, reduced S-containing minerals make up the majority (∼75 %) of all non-O containing mineral localities in the MED, representing the importance of reduced S as an electron source and substrate in the evolution of microbial metabolic networks. The range wMEECV values of S6+ community minerals expands through time due primarily to formation of chemically diverse sulfate minerals, coinciding with crustal oxidation from the late Proterozoic to Phanerozoic and the expansion of the marine sulfate reservoir. The intersection of shared constituent elements among reduced and oxidized S in the mineral chemistry network represents redox convergence of weathered S in the geosphere that was crucial in the formation of natural resource deposits and the evolution of biogeochemical cycles.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2024.05.024","usgsCitation":"Moore, E.K., Diedolf, J., Morrison, S.M., and Hummer, D., 2024, Characterizing sulfur redox state and geochemical implications in deep-time using mineral chemistry network analysis: Geochimica et Cosmochimica Acta, v. 376, p. 25-36, https://doi.org/10.1016/j.gca.2024.05.024.","productDescription":"12 p.","startPage":"25","endPage":"36","ipdsId":"IP-153851","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2024.05.024","text":"Publisher Index Page"},{"id":483712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Elisha Kelly 0000-0002-9750-7769","orcid":"https://orcid.org/0000-0002-9750-7769","contributorId":334043,"corporation":false,"usgs":true,"family":"Moore","given":"Elisha","email":"","middleInitial":"Kelly","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":931586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diedolf, Joseph IV","contributorId":352519,"corporation":false,"usgs":false,"family":"Diedolf","given":"Joseph","suffix":"IV","affiliations":[{"id":84250,"text":"Department of Environmental Science, School of Earth and the Environment, Rowan University, Glassboro, NJ, United States","active":true,"usgs":false}],"preferred":false,"id":931587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morrison, Shaunna M.","contributorId":261814,"corporation":false,"usgs":false,"family":"Morrison","given":"Shaunna","email":"","middleInitial":"M.","affiliations":[{"id":53026,"text":"Carnegie Institute for Science","active":true,"usgs":false}],"preferred":false,"id":931588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hummer, Daniel","contributorId":334048,"corporation":false,"usgs":false,"family":"Hummer","given":"Daniel","email":"","affiliations":[{"id":80056,"text":"School of Earth Systems and Sustainability, Southern Illinois University, Carbondale, Il, United States","active":true,"usgs":false}],"preferred":false,"id":931589,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255166,"text":"70255166 - 2024 - Evaluating satellite-transmitter backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin","interactions":[],"lastModifiedDate":"2024-06-18T14:04:23.857439","indexId":"70255166","displayToPublicDate":"2024-05-30T08:41:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14485,"text":"The Wildlife Society Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating satellite-transmitter backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin","docAbstract":"Wildlife tracking studies have become ubiquitous in ecology and now provide previously unobtainable data regarding individual movement, vital rates, and population demographics. However, tracking devices can potentially reduce survival of study subjects, generating biases in the vital rates they seek to measure. Previous studies have found that greater sage-grouse (Centrocercus urophasianus) fitted with Global Positioning System (GPS) tracking devices may experience reduced survival, relative to those tracked with traditional radio transmitters, and have documented skin abrasions and lacerations associated with typical backpack-style GPS harnesses. We implemented an experimental study comparing survival and harness retention between 2 different backpack-style GPS transmitter harnesses. We captured female sage-grouse at 3 study sites in the northwest Great Basin of Oregon, Nevada, and California during 2019–2021. We fit each individual, following previously published recommendations, with either a standard backpack harness or a modified harness hypothesized to reduce skin abrasion and laceration. We used known-fate models in Program MARK to model variation in survival and harness retention separately as a function of harness type, year, age, a linear effect of time, and the ratio of the device to individual body mass. Neither survival nor retention varied systematically by harness type, however retention decreased as a function of body mass ratio. We echo previous recommendations for standardized harness attachment protocols and studies designed to isolate and test potential mechanisms by which tracking devices and attachment methods might affect survival and well-being of sage-grouse and other tracked species.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1523","usgsCitation":"Lundblad, C.G., Anthony, C.R., Dungannon, T., Haab, K.A., Schuyler, E., Sink, C.E., Dugger, K., and Hagen, C., 2024, Evaluating satellite-transmitter backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin: The Wildlife Society Bulletin, v. 48, no. 2, e1523, 15 p., https://doi.org/10.1002/wsb.1523.","productDescription":"e1523, 15 p.","ipdsId":"IP-154308","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439479,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1523","text":"Publisher Index Page"},{"id":430129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California Nevada, Oregon","otherGeospatial":"Great 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University","active":true,"usgs":false}],"preferred":false,"id":903643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haab, Kimberly A.","contributorId":338863,"corporation":false,"usgs":false,"family":"Haab","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schuyler, Elizabeth M.","contributorId":338867,"corporation":false,"usgs":false,"family":"Schuyler","given":"Elizabeth M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sink, Chelsea E.","contributorId":338870,"corporation":false,"usgs":false,"family":"Sink","given":"Chelsea","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagen, Christian A.","contributorId":338874,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254636,"text":"70254636 - 2024 - A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","interactions":[],"lastModifiedDate":"2024-10-07T16:09:44.357781","indexId":"70254636","displayToPublicDate":"2024-05-29T06:47:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","title":"A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","docAbstract":"Species distributions are often indicative of historical biogeographical events and contemporary spatial biodiversity patterns. The Harlequin Duck Histrionicus histrionicus is a sea duck of conservation concern that has a disjunct distribution, with discrete portions of its range associated with northern Pacific and Atlantic Ocean basins. Movement data indicate migratory connectivity within regions of each ocean basin but not cross-continent dispersal, suggesting that genetic structuring could exist at multiple spatial scales. Little is known regarding the impacts of past vicariance events on the species phylogeographical structure and historical demography, or rates of gene flow at different spatial scales. We used data from microsatellite loci and mitochondrial DNA (mtDNA) sequences to quantify levels of genetic diversity within, and the extent of spatial genetic differentiation among locations sampled at multiple spatial scales across the species range. Samples were collected at nonbreeding locations, which represent groupings appropriate for characterizing genetically differentiated subgroups at regional and continental scales. Collectively, genetic data and coalescence modelling suggested that individuals colonized regions currently occupied within both ocean basins in the Holocene from a single refuge in the Atlantic. Further, it seems likely there was secondary contact with lineages derived from populations in Asia, based on the shallow species-wide mtDNA phylogeny and high incidence of recently derived private mtDNA haplotypes. Estimates of inter-location variance in microsatellite allele and mtDNA haplotype frequency were moderate and significant between western (Pacific – North America) and eastern (Atlantic – North America, Greenland and Iceland) ocean basins and among sampling groups within each ocean basin. Genetic differentiation among sampling groups was particularly evident at the species distributional margins in the Atlantic (Iceland) and the Pacific (Shemya Island) Ocean basins. Coalescent modelling results suggest that contemporary spatial genetic patterns in the species arose through the combined influences of secondary contact, shared ancestry and gene flow after the last glacial maxima.
Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.
Explosive volcanic eruptions produce hazardous atmospheric plumes composed of tephra particles, hot gas and entrained air. Such eruptions are generally driven by magmatic fragmentation or steam expansion. However, an eruption mechanism outside this phreatic–magmatic spectrum was suggested by a sequence of 12 explosive eruptions in May 2018 at Kīlauea, Hawaii, that occurred during the early stages of caldera collapse and produced atmospheric plumes reaching 8 km above the vent. Here we use seismic inversions for reservoir pressure as a source condition for three-dimensional simulations of transient multiphase eruptive plume ascent through a conduit and stratified atmosphere. We compare the simulations with conduit ascent times inferred from seismic and infrasound data, and with plume heights from radar data. We find that the plumes are consistent with eruptions caused by a stomp-rocket mechanism involving the abrupt subsidence of reservoir roof rock that increased pressure in the underlying magma reservoir. In our model, the reservoir was overlain by a pocket of accumulated high-temperature magmatic gas and lithic debris, which were driven through a conduit approximately 600 m long to erupt particles at rates of around 3,000 m3 s−1. Our results reveal a distinct collapse-driven type of eruption and provide a framework for integrating diverse geophysical and atmospheric data with simulations to gain a better understanding of unsteady explosive eruptions.
Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer (Odocoileus virginianus) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110756","usgsCitation":"Bondo, K.J., Rosenberry, C., Stainbrook, D., and Walter, W., 2024, Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types: Ecological Modeling, v. 493, 110756, 16 p., https://doi.org/10.1016/j.ecolmodel.2024.110756.","productDescription":"110756, 16 p.","ipdsId":"IP-163658","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bondo, Kristin J.","contributorId":343150,"corporation":false,"usgs":false,"family":"Bondo","given":"Kristin","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":910646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Christopher S.","contributorId":343151,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stainbrook, David","contributorId":343152,"corporation":false,"usgs":false,"family":"Stainbrook","given":"David","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910649,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261997,"text":"70261997 - 2024 - Development and evaluation of public-supply community water service area boundaries for the conterminous United States","interactions":[],"lastModifiedDate":"2025-01-08T15:23:27.857215","indexId":"70261997","displayToPublicDate":"2024-05-26T09:18:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of public-supply community water service area boundaries for the conterminous United States","docAbstract":"The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13210","usgsCitation":"Buchwald, C.A., Houston, N., Stewart, J.S., Alzraiee, A.H., Niswonger, R.G., and Larsen, J., 2024, Development and evaluation of public-supply community water service area boundaries for the conterminous United States: Journal of the American Water Resources Association, v. 60, no. 4, p. 879-896, https://doi.org/10.1111/1752-1688.13210.","productDescription":"18 p.","startPage":"879","endPage":"896","ipdsId":"IP-129020","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1111/1752-1688.13210","text":"Publisher Index 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cabuchwa@usgs.gov","orcid":"https://orcid.org/0000-0001-8968-5023","contributorId":1943,"corporation":false,"usgs":true,"family":"Buchwald","given":"Cheryl","email":"cabuchwa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":922612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922613,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255784,"text":"70255784 - 2024 - Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","interactions":[],"lastModifiedDate":"2024-07-09T14:40:37.188664","indexId":"70255784","displayToPublicDate":"2024-05-26T06:43:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9121,"text":"Frontiers Earth Science Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","title":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","docAbstract":"The threat posed by emerging infectious diseases is a major concern for global public health, animal health and food security, and the role of birds in transmission is increasingly under scrutiny. Each year, millions of mass-reared game-farm birds are released into the wild, presenting a unique and a poorly understood risk to wild and susceptible bird populations, and to human health. In particular, the shedding of enteric pathogens through excrement into bodies of water at shared migratory stop-over sites, and breeding and wintering grounds, could facilitate multi-species long-distance pathogen dispersal and infection of high numbers of naive endemic birds annually. The Mallard (Anas platyrhynchos) is the most abundant of all duck species, migratory across much of its range, and an important game species for pen-rearing and release. Major recent population declines along the US Atlantic coast has been attributed to game-farm and wild mallard interbreeding and the introduction maladaptive traits into wild populations. However, pathogen transmission and zoonosis among game-farms Mallard may also impact these populations, as well as wildlife and human health. Here, we screened 16 game-farm Mallard from Wisconsin, United States, for enteric viral pathogens using metatranscriptomic data. Four families of viral pathogens were identified – Picobirnaviridae (Genogroup I), Caliciviridae (Duck Nacovirus), Picornaviridae (Duck Aalivirus) and Sedoreoviridae (Duck Rotavirus G). To our knowledge, this is the first report of Aalivirus in the Americas, and the first report of Calicivirus outside domestic chicken and turkey flocks in the United States. Our findings highlight the risk of viral pathogen spillover from peri-domestically reared game birds to naive wild bird populations.
Predator non-consumptive effects (NCE) can alter prey foraging time and habitat use, potentially reducing fitness. Prey can mitigate NCEs by increasing vigilance, chewing-vigilance synchronization, and spatiotemporal avoidance of predators. We quantified the relationship between Mexican wolf (Canis lupus baileyi) predation risk and elk (Cervus canadensis) behavior. We conducted behavioral observations on adult female elk and developed predation risk indices using GPS collar data from Mexican wolves, locations of elk killed by wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking. Metrics that quantified both spatial and temporal predation risk were the most predictive. Vigilance was positively associated with increased predation risk. The effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was lower, the probability of foraging increased while resting decreased in high-risk areas. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. Probability of multitasking depended on canopy openness and an interaction between maternal period and predation risk; multitasking decreased prior to parturition and increased post parturition in high-risk areas. Openness was inversely related to multitasking. These results suggest adult female elk are altering the type of vigilance used depending on resource availability/quality, current energetic needs, and predation risk. Our results highlight potentially important, but often-excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator–prey dynamics.
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-024-00620-2","usgsCitation":"Van Ee, J.J., Hagen, C., Pavlacky, D.C., Haukos, D.A., . Lawrence, A., Tanner, A.M., Grisham, B.A., Fricke, K., Liza G. Rossi, Beauprez, G., Kuklinski, K.E., Martin, R., Koslovsky, M.D., Rintz, T.B., and Hooten, M., 2024, Melded integrated population models: Journal of Agricultural, Biological and Environmental Statistics, v. 5, 31 p., https://doi.org/10.1007/s13253-024-00620-2.","productDescription":"31 p.","ipdsId":"IP-157570","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.41733481268557,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 38.10618949808156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Ee, Justin J.","contributorId":341159,"corporation":false,"usgs":false,"family":"Van Ee","given":"Justin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagen, Christian A.","contributorId":341160,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[],"preferred":false,"id":908024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavlacky, David C. Jr.","contributorId":341161,"corporation":false,"usgs":false,"family":"Pavlacky","given":"David","suffix":"Jr.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":908025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":". Lawrence, Andrew J","contributorId":341162,"corporation":false,"usgs":false,"family":". 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Rossi","contributorId":341166,"corporation":false,"usgs":false,"family":"Liza G. Rossi","affiliations":[{"id":13724,"text":"Texas A&M University-Kingsville","active":true,"usgs":false}],"preferred":false,"id":908031,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Beauprez, Grant M.","contributorId":341167,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant M.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908032,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kuklinski, Kurt E.","contributorId":341168,"corporation":false,"usgs":false,"family":"Kuklinski","given":"Kurt","email":"","middleInitial":"E.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":908033,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Martin, Russell","contributorId":341169,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":908034,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Koslovsky, Matthew D.","contributorId":341170,"corporation":false,"usgs":false,"family":"Koslovsky","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":908035,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rintz, Troy B.","contributorId":341171,"corporation":false,"usgs":false,"family":"Rintz","given":"Troy","email":"","middleInitial":"B.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":908036,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hooten, Mevin B.","contributorId":341172,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin B.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":908037,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70254506,"text":"70254506 - 2024 - Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA","interactions":[],"lastModifiedDate":"2024-05-29T15:02:20.28295","indexId":"70254506","displayToPublicDate":"2024-05-24T09:56:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5537,"text":"Ocean Science","active":true,"publicationSubtype":{"id":10}},"title":"Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA","docAbstract":"Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.
","language":"English","publisher":"Copernicus","doi":"10.5194/os-20-661-2024","usgsCitation":"Yates, K., Fehr, Z., Johnson, S.A., and Zawada, D., 2024, Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA: Ocean Science, v. 20, no. 3, p. 661-688, https://doi.org/10.5194/os-20-661-2024.","productDescription":"28 p.","startPage":"661","endPage":"688","ipdsId":"IP-157460","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/os-20-661-2024","text":"Publisher Index Page"},{"id":429348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Looe Key Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.84282237828644,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.603571498742696\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":217808,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fehr, Zachery","contributorId":336991,"corporation":false,"usgs":false,"family":"Fehr","given":"Zachery","affiliations":[{"id":64427,"text":"Cherokee Nation System Solutions","active":true,"usgs":false}],"preferred":false,"id":901671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901673,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254662,"text":"70254662 - 2024 - Best practices for genetic and genomic data archiving","interactions":[],"lastModifiedDate":"2024-07-15T15:11:44.799023","indexId":"70254662","displayToPublicDate":"2024-05-24T09:33:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17794,"text":"Nature, Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Best practices for genetic and genomic data archiving","docAbstract":"Genetic and genomic data are collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, data are typically used only by the generating authors. The reuse of genetic and genomic datasets remains uncommon because it is difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible and FAIR (findable, accessible, interoperable and reusable) compliant, they could be reused for many additional purposes. We discuss the main challenges with existing genetic and genomic data archives, and suggest best practices for archiving genetic and genomic data. Recognizing that this is a longstanding issue due to little formal data management training within the fields of ecology and evolution, we highlight steps that research institutions and publishers could take to improve data archiving.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-024-02423-7","usgsCitation":"Leigh, D.M., Vandergast, A.G., Hunter, M., Crandall, E.D., Funk, W., Garroway, C., Hoban, S.M., Oyler-McCance, S.J., Rellstab, C., Segelbacher, G., Schmidt, C., Vazquez-Dominguez, E., and Paz-Vinas, I., 2024, Best practices for genetic and genomic data archiving: Nature, Ecology and Evolution, v. 8, p. 1224-1232, https://doi.org/10.1038/s41559-024-02423-7.","productDescription":"9 p.","startPage":"1224","endPage":"1232","ipdsId":"IP-157740","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467004,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-04593895","text":"External Repository"},{"id":429571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Leigh, Deborah M.","contributorId":291307,"corporation":false,"usgs":false,"family":"Leigh","given":"Deborah","email":"","middleInitial":"M.","affiliations":[{"id":62679,"text":"WSL Swiss Federal Research Institute","active":true,"usgs":false}],"preferred":false,"id":902175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":902176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":902177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crandall, Eric D. 0000-0001-8580-3651","orcid":"https://orcid.org/0000-0001-8580-3651","contributorId":337181,"corporation":false,"usgs":false,"family":"Crandall","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":902178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":902179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garroway, Colin J","contributorId":302145,"corporation":false,"usgs":false,"family":"Garroway","given":"Colin J","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":902180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":902181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":902182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rellstab, Christian 0000-0002-0221-5975","orcid":"https://orcid.org/0000-0002-0221-5975","contributorId":337184,"corporation":false,"usgs":false,"family":"Rellstab","given":"Christian","email":"","affiliations":[{"id":80990,"text":"Swiss Federal Research Institute, Switzerland","active":true,"usgs":false}],"preferred":false,"id":902183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":902184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schmidt, Chloe 0000-0003-2572-4200","orcid":"https://orcid.org/0000-0003-2572-4200","contributorId":337185,"corporation":false,"usgs":false,"family":"Schmidt","given":"Chloe","email":"","affiliations":[{"id":13699,"text":"German Centre for Integrative Biodiversity Research, Germany","active":true,"usgs":false}],"preferred":false,"id":902185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vazquez-Dominguez, Ella 0000-0001-6131-2014","orcid":"https://orcid.org/0000-0001-6131-2014","contributorId":337186,"corporation":false,"usgs":false,"family":"Vazquez-Dominguez","given":"Ella","email":"","affiliations":[{"id":80993,"text":"Universidad Nacional Autónoma de México, México","active":true,"usgs":false}],"preferred":false,"id":902186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":902187,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254539,"text":"70254539 - 2024 - Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","interactions":[],"lastModifiedDate":"2024-05-31T14:34:42.026925","indexId":"70254539","displayToPublicDate":"2024-05-24T09:27:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","docAbstract":"Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging.
We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment.
Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance.
We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales.
Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10980-024-01901-4","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D.S., Liu, R., Dumandan, P.K., Hojatimalekshah, A., Delparte, D.M., and Caughlin, T., 2024, Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands: Landscape Ecology, v. 39, 108, 16 p., https://doi.org/10.1007/s10980-024-01901-4.","productDescription":"108, 16 p.","ipdsId":"IP-157067","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":439502,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-01901-4","text":"Publisher Index Page"},{"id":429403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.01648249125195,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 44.15423652632305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":901795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":210334,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":901796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Rongsong","contributorId":43480,"corporation":false,"usgs":false,"family":"Liu","given":"Rongsong","email":"","affiliations":[],"preferred":false,"id":901797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dumandan, Patricia Kaye T. 0000-0003-3360-1534","orcid":"https://orcid.org/0000-0003-3360-1534","contributorId":257070,"corporation":false,"usgs":false,"family":"Dumandan","given":"Patricia","email":"","middleInitial":"Kaye T.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901798,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hojatimalekshah, Ahmad","contributorId":337022,"corporation":false,"usgs":false,"family":"Hojatimalekshah","given":"Ahmad","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901799,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delparte, Donna M. 0000-0002-9107-5117","orcid":"https://orcid.org/0000-0002-9107-5117","contributorId":317762,"corporation":false,"usgs":false,"family":"Delparte","given":"Donna","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":901800,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901801,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254574,"text":"70254574 - 2024 - Testing megathrust rupture models using tsunami deposits","interactions":[],"lastModifiedDate":"2024-06-03T12:06:02.266842","indexId":"70254574","displayToPublicDate":"2024-05-24T07:03:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Testing megathrust rupture models using tsunami deposits","docAbstract":"The 26 January 1700 CE Cascadia subduction zone earthquake ruptured much of the plate boundary and generated a tsunami that deposited sand in coastal marshes from northern California to Vancouver Island. Although the depositional record of tsunami inundation is extensive in some of these marshes, few sites have been investigated in enough detail to map the inland extent of sand deposition and depict variability in tsunami deposit thickness and grain size. We collected 129 cores in marshes of the Salmon River estuary in Oregon and reanalyzed 114 core logs from a 1987–88 study that mapped the inland extent of circa 1700 CE sandy tsunami deposits. The ca. 1700 CE tsunami deposit in the Salmon River estuary is easily recognized in cores ≤1 m deep in which a buried marsh peat is overlain by a well sorted sand bed with a sharp lower contact that thins and fines inland. We use tsunami deposit data and models of sandy tsunami sediment transport (using Delft3D-FLOW) to test 15 rupture models that could represent a ca. 1700 CE earthquake. At least 12–16 m of slip offshore of the Salmon River, which results in 0.8–1.0 m of coastal coseismic subsidence, is required to match the ca. 1700 CE sand deposit's inland extent, which is consistent with models of heterogeneous megathrust slip in ca. 1700 CE. Our methods of detailed tsunami deposit mapping, combined with sediment transport modeling, can be used to test models of megathrust ruptures and their tsunamis to potentially improve earthquake and tsunami hazard assessments.
Population in western Sarpy County, Nebraska, has steadily increased over the last several decades and has led to increased groundwater use for domestic purposes. To meet the increase in demand, the Papio-Missouri River Natural Resources District is seeking to use all available sources of groundwater in western Sarpy County. Additionally, elevated groundwater nitrate plus nitrite as nitrogen concentrations were detected, indicating the need to better understand the groundwater quality of the area. Although the general geology of the area is understood, the area does not have detailed information on the extent of the various aquifers, particularly the Dakota aquifer. To characterize these aquifers, the Papio-Missouri River Natural Resources District invested in airborne electromagnetic surveys of the area to better understand the subsurface geology. Although these surveys improved understanding of the groundwater systems in the area, the Papio-Missouri River Natural Resources District wanted to integrate the subsurface information with available water-quality and groundwater-level data.
In response, the U.S. Geological Survey, in cooperation with the Papio-Missouri River Natural Resources District, the Nebraska Natural Resources Commission, and the Nebraska Department of Natural Resources, assembled geologic, hydrogeologic and nitrate plus nitrite as nitrogen information for the selected area into a three-dimensional visualization computer software package called GeoScene3D. The completed GeoScene3D project was assembled to provide a visualization of the groundwater systems and associated water-quality results in Sarpy County and to provide the Papio-Missouri River Natural Resources District managers with information that can be used to make more informed groundwater resource-planning decisions in the future. This report details the development of a three-dimensional model created within GeoScene3D to visualize the subsurface, particularly the Dakota Sandstone in western Sarpy County.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The global decline of pollinators, particularly insects, underscores the importance of enhanced monitoring of their populations and habitats. However, monitoring some pollinator habitat is challenging due to widespread species distributions and shifts in habitat requirements through seasons and life stages. The monarch butterfly (Danaus plexippus), a migratory insect pollinator that breeds widely throughout North America, presents a unique case study for testing a sampling framework to overcome these challenges. Monarchs exhibit discrete resource needs across life stages (e.g., larval requirement for milkweed, adult requirement for floral nectar), utilizing many land use types across their extensive geographic range during breeding and migration seasons. The Integrated Monarch Monitoring Program (IMMP) uses a standardized protocol with a generalized random tessellation stratified (GRTS) sampling design to gather spatially balanced and ecologically representative information on monarch habitats within the United States. The IMMP is applicable to various land use types and habitats used by breeding monarchs and may be extended to sites outside of the GRTS design to collect data on non-random sites of interest, such as legacy or conservation sites. Additionally, the IMMP’s modular design and publicly available training allows for broad participation, including involvement from community scientists. Here, we summarize habitat metrics (milkweed and floral resources) across 1,233 sites covering much of the monarch’s breeding range. We examine variation in milkweed density and floral resource availability on probabilistic (random) and non-probabilistic (convenience) samples and among land use types (site types). Additionally, we assess resource availability within core geographies for monarch breeding and migration, specifically within the U.S. Fish and Wildlife Service’s Monarch Conservation Units (western, northern, and southern United States). Milkweed density, floral frequency, and floral richness were higher on non-random sites and in the North region. Among site types, milkweed density was highest on Rights-of-Way and Unclassified Grassland, while floral frequency was lowest on Rights-of-Way. The IMMP represents the first field-based habitat monitoring program of this scale for monarchs, yielding a robust dataset on monarchs and their habitats across their breeding range and offering a framework for surveying the habitat of insect species with diverse habitat requirements or widespread distributions.
Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range.
","language":"English","publisher":"Springer","doi":"10.1007/s00521-024-09897-3","usgsCitation":"Gonzalez-Afanador, I., Chen, C., Morales-Torres, G., Miehls, S.M., Shi, H., Tan, X., and Sepulveda, N., 2024, Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems: Neural Computing and Applications, v. 36, p. 16195-16212, https://doi.org/10.1007/s00521-024-09897-3.","productDescription":"18 p.","startPage":"16195","endPage":"16212","ipdsId":"IP-164744","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","noUsgsAuthors":false,"publicationDate":"2024-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez-Afanador, Ian","contributorId":354863,"corporation":false,"usgs":false,"family":"Gonzalez-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Claudia","contributorId":354864,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morales-Torres, Gerardo","contributorId":354865,"corporation":false,"usgs":false,"family":"Morales-Torres","given":"Gerardo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Hongyang 0000-0003-4135-3673","orcid":"https://orcid.org/0000-0003-4135-3673","contributorId":214760,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":936591,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936592,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255912,"text":"70255912 - 2024 - Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal","interactions":[],"lastModifiedDate":"2024-07-30T14:50:43.791476","indexId":"70255912","displayToPublicDate":"2024-05-23T09:43:57","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}},"displayTitle":"Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal","title":"Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal","docAbstract":"Effective management of invasive species benefits from a comprehensive understanding of the species’ behavior and interactions with the invaded system. We investigated temporal dynamics of telemetry detections and the potential utility of a traitor approach for informing response efforts to the invasive grass carp (Ctenopharyngodon idella) population in the Sandusky River, a major tributary to Lake Erie. Telemetered grass carp exhibited heightened activity at night and early morning, suggesting that capture and removal be more effective during these time periods. Analysis of catch per unit effort (CPUE) across different removal methods, trammel nets, electrofishing, and hoop nets. suggested that incorporating the traitor approach could improve capture. Low catchability values (<0.026), based on the number of telemetered grass carp present in the river on a weekly basis and the number of those telemetered fish captured, suggest the species is difficult to capture. Optimizing response effort efficiency is important and refining catchability estimates will lessen errors in population models and improve interpretation of low CPUE data. Results from generalized additive models suggest capture could be improved using telemetry data, night removals, and by attempting exploratory removal efforts in fall and winter months. By incorporating telemetry data and acknowledging the complexities of grass carp behavior and ecology, we found that a multifaceted and data-driven approach to invasive species control could be beneficial, ultimately promoting conservation and sustainability in dynamic ecosystems like Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102373","usgsCitation":"Acre, M.R., Hessler, T.M., Bonjour, S.M., Roberts, J., Colborne, S.F., Brenden, T., Nathan, L.R., Broaddus, D., Vandergoot, C.S., Mayer, C.M., Qian, S.S., Hunter, R., Brown, R.E., and Calfee, R.D., 2024, Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal: Journal of Great Lakes Research, v. 50, 102373, 14 p., https://doi.org/10.1016/j.jglr.2024.102373.","productDescription":"102373, 14 p.","ipdsId":"IP-163490","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":439507,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102373","text":"Publisher Index Page"},{"id":430894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie, Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.39142052169268,\n 41.71182863215597\n ],\n [\n -83.39142052169268,\n 41.11734456643944\n ],\n [\n -81.60768939771071,\n 41.11734456643944\n ],\n [\n -81.60768939771071,\n 41.71182863215597\n ],\n [\n -83.39142052169268,\n 41.71182863215597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research 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rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":906003,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70254375,"text":"sir20235064D - 2024 - Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70254375,"text":"sir20235064D - 2024 - Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064D","publicationYear":"2024","noYear":false,"chapter":"D","displayTitle":"Peak Streamflow Trends in Michigan and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-14T12:16:27.670601","indexId":"sir20235064D","displayToPublicDate":"2024-05-23T09:30:14","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"D","displayTitle":"Peak Streamflow Trends in Michigan and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020","docAbstract":"This study characterizes hydroclimatic variability and change in peak streamflow and daily streamflow in Michigan from water years 1921 through 2020. Four analysis periods were examined: the 100-year period from water year 1921 through 2020, the 75-year period from water year 1946 through 2020, the 50-year period from water year 1971 through 2020, and the 30-year period from water year 1991 through 2020. Peak streamflow and climate data were available at 4, 29, 50, and 30 streamgages in the 100-, 75-, 50-, and 30-year periods, respectively. Daily streamflow was available for 4, 29, 74, and 79 streamgages in the 100-, 75-, 50-, and 30-year periods, respectively.
Peak streamflow for each streamgage and analysis period was assessed for monotonic trends and change points. Trends in peak streamflow were predominantly upward, with some isolated downward trends throughout the southern half of Michigan for all four analysis periods. Trends in the Upper Peninsula were downward in 75- and 50-year analysis periods and upward or neutral in the 30-year period. Upward trends in peak flows were largely driven by increases in precipitation, which occurred at nearly every streamgage in all analysis periods, with the greatest magnitude trends in winter and spring in the 50- and 30-year periods.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726