Riparian trees and their annual growth rings can be used to reconstruct drought histories related to streamflow. Because the death of individual trees reduces competition for survivors, however, tree-ring chronologies based only on surviving trees may underestimate drought impacts. This problem can be addressed by calculating productivity at the stand scale to account for tree mortality and establishment. In the semi-arid Great Basin in the western United States, we calculated riparian wood production from 1946 to 2016 along a stream where most flow has been removed by a diversion pipeline since 1961. The water table was found to be generally below the root zone of cottonwoods (Populus angustifolia and P. angustifolia × trichocarpa) in the pipeline-dewatered reach but within it in reference reaches. To reconstruct forest productivity through time, we separately combined measurements of tree-ring basal area increment with either changing forest area from aerial photos or a census of cross-dated living and dead cottonwoods. Both approaches revealed productivity declines in the dewatered reach relative to adjacent reference reaches, and the decline accelerated in the 2000s. Tree-ring narrowing resulted in divergence between the dewatered reach and one reference reach within 5 years after diversion. However, the dewatered reach did not diverge from the other reference reach until 40 years later, when an unprecedented early 2000s atmospheric drought coupled with diversion to cause extensive cottonwood mortality. We conclude that dendrochronological investigations of forest response to environmental stress should incorporate stand dynamics and that the full impacts of flow diversion can be delayed for decades.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2408","usgsCitation":"Schook, D.M., Friedman, J.M., Hoover, J.D., Rice, S.E., Thaxton, R.D., and Cooper, D.J., 2022, Riparian forest productivity decline initiated by streamflow diversion then amplified by atmospheric drought 40 years later: Ecohydrology, v. 15, no. 3, e2408, 14 p., https://doi.org/10.1002/eco.2408.","productDescription":"e2408, 14 p.","ipdsId":"IP-134136","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2408","text":"Publisher Index Page"},{"id":398106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Utah","otherGeospatial":"Great Basin National Park, Pole Canyon, Snake Creek, Snake Range, Snake Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.36767578124999,\n 38.85815687709717\n ],\n [\n -114.13284301757812,\n 38.85815687709717\n ],\n [\n -114.13284301757812,\n 38.9396506365778\n ],\n [\n -114.36767578124999,\n 38.9396506365778\n ],\n [\n -114.36767578124999,\n 38.85815687709717\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":839582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":839583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoover, Jamie D.","contributorId":238180,"corporation":false,"usgs":false,"family":"Hoover","given":"Jamie","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Steven E.","contributorId":238179,"corporation":false,"usgs":false,"family":"Rice","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thaxton, Richard D.","contributorId":238181,"corporation":false,"usgs":false,"family":"Thaxton","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooper, David J.","contributorId":196510,"corporation":false,"usgs":false,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13017,"text":"Department of Forest and Rangeland Stewardship, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839587,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230200,"text":"ofr20221035 - 2022 - California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3 — Benthic habitat characterization offshore Morro Bay, California","interactions":[],"lastModifiedDate":"2022-08-23T19:18:44.059405","indexId":"ofr20221035","displayToPublicDate":"2022-04-05T09:14:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1035","displayTitle":"California Deepwater Investigations and Groundtruthing (Cal DIG) I, Volume 3—Benthic Habitat Characterization Offshore Morro Bay, California","title":"California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3 — Benthic habitat characterization offshore Morro Bay, California","docAbstract":"Coastal and Marine Ecological Classification Standard (CMECS) geoform, substrate, and biotic component geographic information system (GIS) products were developed for the U.S. Exclusive Economic Zone (U.S. EEZ) of south-central California in the region of Santa Lucia Bank motivated by interest in development of offshore wind-energy capacity and infrastructure. The Bureau of Ocean Energy Management (BOEM), in coordination with the State of California and many other members of the California Task Force, issued calls for information in 2018 for the study area offshore of Morro Bay, California. The study area is in depths of 500 to 1,200 meters (m) and adjacent to a decommissioned nuclear power plant with a developed electric grid connection, and in an area of high wind resource. BOEM is the lead agency responsible for planning and leasing in the U.S. EEZ and funded this project to assess baseline conditions of, and the potential effects on, the seafloor environment. This project, carried out by the U.S. Geological Survey (USGS), resulted in three reports: one on biological analysis of seafloor video data, one on analysis of the geologic framework and hazards, and this report on seafloor habitat. The study area consists of 8,424 square kilometers (km2) of multibeam echo sounder (MBES) data acquired during five surveys from 2016 to 2019. Remotely operated vehicle (ROV) video was acquired in 2019 to supervise the classification of the MBES data into habitats. Derivatives of the MBES data were classified into 16 unique biotopes, 6 substrate types, 28 modifier groups, and 22 geoforms. The study area substrate is predominantly soft sediment (mud and fine sand) covering 7,804 km2 (92.7 percent) of the area. Mixed substrate areas on rocky banks, channel scarps, and the shelf break comprise 404 km2 (4.8 percent) of the study area. Hard substrate areas are found predominantly on the tops and flanks of banks and on bank ridges that separate canyons incising the banks. Hard substrates comprise 211 km2 of the study area (2.5 percent). After the bathymetry and backscatter raster images (rasters) were classified, manual editing was also done to remove noise artifacts. This effort was not completely successful and there are numerous erroneous small areas in the rasters that have been passed on to the CMECS polygon product. Nearly 120,000 annotations of organisms and their habitat were made from 25 video transects selected from 185 hours of ROV video. In total, 2,714 km2 of seafloor were successfully assigned to biotopes. Some biotopes were assigned to separate areas spatially distant from the transects that define the biotope. Expected relations between physical habitat and biota such as the number of species and the substrate induration and rugosity were verified. Slope is typically a predictive variable and was used in the classification of habitat, but the ground truth used for biotic component analysis included very little steeply sloping area. Ground-truth ROV operations were reduced by the sea state; additional ground truth could improve the biotic results and increase confidence in the spatial distribution of classifications reported here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221035","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management, National Oceanic and Atmospheric Administration, and Monterey Bay Aquarium Research Institute","programNote":"Bureau of Ocean Energy Management OCS Study BOEM 2021–045","usgsCitation":"Cochrane, G.R., Kuhnz, L.A. Gilbane, L., Dartnell, P., Walton, M.A.L., and Paull, C.K., 2022, California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3—Benthic habitat characterization offshore Morro Bay, California: U.S. Geological Survey Open-File Report 2022–1035 [also released as Bureau of Ocean Energy Management OCS Study BOEM 2021–045], 18 p., https://doi.org/10.3133/ofr20221035.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-129519","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":398057,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QQZ27U","text":"Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project","description":"Cochrane, G.R., Kuhnz, L.A., Gilbane, L., Dartnell, P., and Walton, M.A., 2022, Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project: U.S. Geological Survey data release, https://doi.org/10.5066/P9QQZ27U."},{"id":398055,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1035/coverthb.jpg"},{"id":398056,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1035/ofr20221035.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","city":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.48431396484375,\n 34.863397850419524\n ],\n [\n -120.9210205078125,\n 35.574682600980914\n ],\n [\n -121.31103515625,\n 35.94243575255426\n ],\n [\n -121.45660400390624,\n 36.04465753921525\n ],\n [\n -122.36846923828125,\n 35.628279555648845\n ],\n [\n -122.09930419921876,\n 34.66032236481892\n ],\n [\n -120.48431396484375,\n 34.863397850419524\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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2885 Mission St.
Santa Cruz, CA 95060
Although bull trout (Salvelinus confluentus) and tailed frogs (Ascaphus montanus) have co-existed in forested Pacific Northwest streams for millennia, these iconic cold-water specialists are experiencing rapid environmental change caused by a warming climate and enhanced wildfire activity. Our goal was to inform future conservation by examining the habitat associations of each species and conditions that facilitate co-occupancy. We repurposed data from previous studies in the northern Rocky Mountains to assess the efficacy of bull trout electrofishing surveys for determining the occurrence of tailed frogs and the predictive capacity of habitat covariates derived from in-stream measurements and geospatial sources to model distributions of both species. Electrofishing reliably detected frog presence (89.2% rate). Both species were strongly associated with stream temperature and flow regime characteristics, and less responsive to riparian canopy cover, slope, and other salmonids. Tailed frogs were also sensitive to wildfire, with occupancy probability peaking around 80 years after a fire. Co-occupancy was most probable in locations with low-to-moderate frequencies of high winter flow events, few other salmonids, a low base-flow index, and intermediate years since fire. The distributions of these species appear to be sensitive to environmental conditions that are changing this century in forests of the northern Rocky Mountains. The amplification of climate-driven effects after wildfire may prove to be particularly problematic in the future. Habitat differences between these two species, considered to be headwater specialists, suggest that conservation measures designed for one may not fully protect the other. Additional studies involving future climate and wildfire scenarios are needed to assess broader conservation strategies and the potential to identify refuge streams where both species are likely to persist, or complementary streams where each could exist separately into the future.
","language":"English","publisher":"MDPI","doi":"10.3390/w14071162","usgsCitation":"Pilliod, D., Arkle, R.S., Thurow, R.F., and Isaak, D.J., 2022, Hydroclimatic conditions, wildfire, and species assemblages influence co-occurrence of bull trout and tailed frogs in northern Rocky Mountain streams: Water, v. 14, no. 7, 1162, 20 p., https://doi.org/10.3390/w14071162.","productDescription":"1162, 20 p.","ipdsId":"IP-137594","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14071162","text":"Publisher Index Page"},{"id":398215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","otherGeospatial":"northern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.20214843749999,\n 43.77109381775651\n ],\n [\n -111.26953125,\n 43.77109381775651\n ],\n [\n -111.26953125,\n 48.951366470947725\n ],\n [\n -117.20214843749999,\n 48.951366470947725\n ],\n [\n -117.20214843749999,\n 43.77109381775651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thurow, Russel F","contributorId":289775,"corporation":false,"usgs":false,"family":"Thurow","given":"Russel","email":"","middleInitial":"F","affiliations":[{"id":62244,"text":"USDA Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":839762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isaak, Dan J","contributorId":289776,"corporation":false,"usgs":false,"family":"Isaak","given":"Dan","email":"","middleInitial":"J","affiliations":[{"id":62244,"text":"USDA Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":839763,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230314,"text":"70230314 - 2022 - Evaluating sources of bias in pedigree-based estimates of breeding population size","interactions":[],"lastModifiedDate":"2022-07-08T15:41:48.075522","indexId":"70230314","displayToPublicDate":"2022-04-05T08:47:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating sources of bias in pedigree-based estimates of breeding population size","docAbstract":"Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS, is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS. We applied results of the simulation analysis to evaluate NˆS for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that NS is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2602","usgsCitation":"White, S.L., Sard, N.M., Brundage III, H., Johnson, R.L., Lubinski, B.A., Eackles, M.S., Park, I.A., Fox, D.A., and Kazyak, D., 2022, Evaluating sources of bias in pedigree-based estimates of breeding population size: Ecological Applications, v. 32, no. 5, e2602, 13 p., https://doi.org/10.1002/eap.2602.","productDescription":"e2602, 13 p.","ipdsId":"IP-114638","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2602","text":"Publisher Index Page"},{"id":398310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.794677734375,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 39.614152077002664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sard, Nicholas M","contributorId":289872,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas","email":"","middleInitial":"M","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":839958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brundage III, Harold M","contributorId":289873,"corporation":false,"usgs":false,"family":"Brundage III","given":"Harold M","affiliations":[{"id":62274,"text":"Environmental Research and Consulting Inc","active":true,"usgs":false}],"preferred":false,"id":839959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839961,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839962,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian A","contributorId":289876,"corporation":false,"usgs":false,"family":"Park","given":"Ian","email":"","middleInitial":"A","affiliations":[{"id":62277,"text":"DNREC","active":true,"usgs":false}],"preferred":false,"id":839963,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":839964,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839965,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241857,"text":"70241857 - 2022 - Resist-accept-direct (RAD) considerations for climate change adaptation in fisheries: The Wisconsin experience","interactions":[],"lastModifiedDate":"2023-03-29T12:27:44.143585","indexId":"70241857","displayToPublicDate":"2022-04-05T07:25:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Resist-accept-direct (RAD) considerations for climate change adaptation in fisheries: The Wisconsin experience","docAbstract":"Decision-makers in inland fisheries management must balance ecologically and socially palatable objectives for ecosystem services within financial or physical constraints. Climate change has transformed the potential range of ecosystem services available. The Resist-Accept-Direct (RAD) framework offers a foundation for responding to climate-induced ecosystem modification; however, ecosystem trajectories and current practices must be understood to improve future decisions. Using Wisconsin's diverse inland fisheries as a case study, management strategies for recreational and subsistence fisheries in response to climate change were reviewed within the RAD framework. Current strategies largely focus on resist actions, while future strategies may need to shift toward accept or direct actions. A participatory adaptive management framework and co-production of policies between state and tribal agencies could prioritise lakes for appropriate management action, with the goal of providing a landscape of diverse fishing opportunities. This knowledge co-production represents a process of social learning requiring substantial investments of funding and time.
1. Large-scale, long-term biodiversity monitoring is essential to conservation, land management, and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including `unknown', morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic ratess, occurrence, and species richness.
2. Here we present a joint classification-occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain `morphospecies' designations, and makes use of individual specimens with known species identities in a semi-supervised setting. We compare the performance of our model to a classification-only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed.
3. The model supported imperfect classifier accuracy and favored certain true species classifications strongly for some morphospecies. The model outperformed (e.g., precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species.
4. Spatial and temporal dynamics from modeled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data, and non-species classifications.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13858","usgsCitation":"Spiers, A., Royle, A., Torrens, C., and Joseph, M., 2022, Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach: Methods in Ecology and Evolution, v. 13, no. 7, p. 1528-1539, https://doi.org/10.1111/2041-210X.13858.","productDescription":"12 p.","startPage":"1528","endPage":"1539","ipdsId":"IP-127556","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448235,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13858","text":"Publisher Index Page"},{"id":398632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398615,"type":{"id":15,"text":"Index Page"},"url":"https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13858"}],"volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Spiers, Anna","contributorId":290178,"corporation":false,"usgs":false,"family":"Spiers","given":"Anna","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":840413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":840414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torrens, Christa","contributorId":290179,"corporation":false,"usgs":false,"family":"Torrens","given":"Christa","email":"","affiliations":[{"id":62371,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":840415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joseph, Maxwell","contributorId":290181,"corporation":false,"usgs":false,"family":"Joseph","given":"Maxwell","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":840416,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235749,"text":"70235749 - 2022 - Reevaluation of the role of blocked Oropsylla hirsuta prairie dog fleas (Siphonaptera: Ceratophyllidae) in Yersinia pestis (Enterobacterales: Enterobacteriaceae) transmission","interactions":[],"lastModifiedDate":"2022-08-17T11:49:24.032807","indexId":"70235749","displayToPublicDate":"2022-04-05T06:47:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2385,"text":"Journal of Medical Entomology","active":true,"publicationSubtype":{"id":10}},"title":"Reevaluation of the role of blocked Oropsylla hirsuta prairie dog fleas (Siphonaptera: Ceratophyllidae) in Yersinia pestis (Enterobacterales: Enterobacteriaceae) transmission","docAbstract":"Prairie dogs in the western United States experience periodic epizootics of plague, caused by the flea-borne bacterial pathogen Yersinia pestis. An early study indicated that Oropsylla hirsuta (Baker), often the most abundant prairie dog flea vector of plague, seldom transmits Y. pestis by the classic blocked flea mechanism. More recently, an alternative early-phase mode of transmission has been proposed as the driving force behind prairie dog epizootics. In this study, using the same flea infection protocol used previously to evaluate early-phase transmission, we assessed the vector competence of O. hirsuta for both modes of transmission. Proventricular blockage was evident during the first two weeks after infection and transmission during this time was at least as efficient as early-phase transmission 2 d after infection. Thus, both modes of transmission likely contribute to plague epizootics in prairie dogs.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jme/tjac021","usgsCitation":"Miarinjara, A., Eads, D.A., Bland, D.M., Matchett, M.R., Biggins, D.E., and Hinnebusch, B.J., 2022, Reevaluation of the role of blocked Oropsylla hirsuta prairie dog fleas (Siphonaptera: Ceratophyllidae) in Yersinia pestis (Enterobacterales: Enterobacteriaceae) transmission: Journal of Medical Entomology, v. 59, no. 3, p. 1053-1059, https://doi.org/10.1093/jme/tjac021.","productDescription":"7 p.","startPage":"1053","endPage":"1059","ipdsId":"IP-135601","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448238,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjac021","text":"Publisher Index Page"},{"id":405252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Miarinjara, Adelaide","contributorId":295322,"corporation":false,"usgs":false,"family":"Miarinjara","given":"Adelaide","email":"","affiliations":[{"id":13450,"text":"NIH","active":true,"usgs":false}],"preferred":false,"id":849179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":849180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bland, David M.","contributorId":295324,"corporation":false,"usgs":false,"family":"Bland","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13450,"text":"NIH","active":true,"usgs":false}],"preferred":false,"id":849181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matchett, Marc R.","contributorId":193409,"corporation":false,"usgs":false,"family":"Matchett","given":"Marc","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":849182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":849183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hinnebusch, B. Joseph","contributorId":295326,"corporation":false,"usgs":false,"family":"Hinnebusch","given":"B.","email":"","middleInitial":"Joseph","affiliations":[{"id":13450,"text":"NIH","active":true,"usgs":false}],"preferred":false,"id":849184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230418,"text":"70230418 - 2022 - Microbially induced anaerobic oxidation of magnetite to maghemite in a hydrocarbon-contaminated aquifer","interactions":[],"lastModifiedDate":"2022-04-12T11:37:08.934453","indexId":"70230418","displayToPublicDate":"2022-04-05T06:33:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Microbially induced anaerobic oxidation of magnetite to maghemite in a hydrocarbon-contaminated aquifer","docAbstract":"Iron mineral transformations occurring in hydrocarbon-contaminated sites are linked to the biodegradation of the hydrocarbons. At a hydrocarbon-contaminated site near Bemidji, Minnesota, USA, measurements of magnetic susceptibility (MS) are useful for monitoring the natural attenuation of hydrocarbons related to iron cycling. However, a transient MS, previously observed at the site, remains poorly understood and the iron mineral phases acting as reactants and products associated with this MS perturbation remain largely unknown. To address these unknowns, we acquired mineral magnetism measurements, including hysteresis loops, backfield curves, and isothermal remanent magnetizations on sediment core samples retrieved from the site and magnetite-filled mineral packets installed within the aquifer. Our data show that the core samples and magnetite packs display decreasing magnetization with time and that this loss in magnetization is accompanied by increasing bulk coercivity consistent with decreased average grain size and/or partial oxidation. Low-temperature magnetometry on all samples displayed behavior consistent with magnetite, but samples within the plume also show evidence of maghemitization. This interpretation is supported by the occurrence of shrinkage cracks on the surface of the grains imaged via scanning electron microscopy. Magnetite transformation to maghemite typically occurs under oxic conditions, here, we propose that maghemitization occurs within the anoxic portions of the plume via microbially mediated anaerobic oxidation. Mineral dissolution also occurs within the plume. Microorganisms capable of such anaerobic oxidation have been identified within other areas at the Bemidji site, but additional microbiological studies are needed to link specific anaerobic iron oxidizers with this loss of magnetization.
The environmental factors controlling soil biodiversity along resource gradients remain poorly understood in wet tropical ecosystems. Aboveground biodiversity is expected to be driven by changes in nutrient availability in these ecosystems, however, much less is known about the importance of nutrient availability in driving soil biodiversity. Here, we combined a cross-continental soil survey across tropical regions with a three decades' field experiment adding nitrogen (N) and phosphorus (P) (100 kg N ha−1y−1 and 100 kg P ha−1y−1) to Hawai'ian tropical forests with contrasting substrate ages (300 and 4,100,000 years) to investigate the influence of nutrient availability to explain the biodiversity of soil bacteria, fungi, protists, invertebrates and key functional genes. We found that soil biodiversity was driven by soil acidification during long-term pedogenesis and across environmental gradients, rather than by nutrient limitations. In fact, our results showed that experimental N additions caused substantial acidification in soils from Hawai'i. These declines in pH were related to large decreases in soil biodiversity from tropical ecosystems in four continents. Moreover, the microbial activity did not change in response to long-term N and P additions. We concluded that environmental filtering drives the biodiversity of multiple soil organisms, and that the acidification effects associated with N additions can further create substantial undesired net negative effects on overall soil biodiversity in naturally tropical acid soils. This knowledge is integral for the understanding and management of soil biodiversity in tropical ecosystems globally.
The first author, Gary S. Fuis, conducted this mapping in the summer of 1967 in partial fulfillment of the entry requirements into the Ph.D program of the Division of Geological and Planetary Sciences of the California Institute of Technology, Pasadena, Calif. The area mapped lies wholly within the Fort Rock Ranch, a private ranch spanning ~50 square miles in Mohave and Yavapai Counties, Arizona. Access to the ranch is limited, and it is uncertain whether a detailed geologic map of the Aquarius Mountains can be recreated today. Therefore, we are making this map available to the public in this Open-File Report.
The original mapping was compiled on an enlarged single aerial photograph at an approximate scale of 1:15,600. The second author, J. Luke Blair, photogrammetrically rectified the original map and added modern topography, which was not available at the time the original map was completed. Modern roads and drainages were also added, including I–40, built after the original map was completed. Both authors reformatted the original map using current USGS geologic map standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221016","usgsCitation":"Fuis, G.S, and Blair, J.L., 2022, Preliminary geologic map of early Miocene felsic eruptive centers in the Aquarius Mountains, west-central Arizona: U.S. Geological Survey Open-File Report 2022-1016, scale 1:15,000, https://doi.org/10.3133/ofr20221016.","productDescription":"1 Sheet: 65.39 × 42.11 inches","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-123374","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501761,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112843.htm","linkFileType":{"id":5,"text":"html"}},{"id":397987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1016/covrthb.jpg"},{"id":397988,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1016/ofr20221016_sheet.pdf","size":"26 MB","linkFileType":{"id":1,"text":"pdf"}}],"scale":"15000","country":"United States","state":"Arizona","otherGeospatial":"Aquarius Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.73046875,\n 34.86001735420488\n ],\n [\n -113.10,\n 34.86001735420488\n ],\n [\n -113.10,\n 35.3\n ],\n [\n -113.73046875,\n 35.3\n ],\n [\n -113.73046875,\n 34.86001735420488\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
Many introduced plants pose invasion risks globally and threaten the biodiversity of native ecosystems. Such non-native plants can become invasive when they have advantages over native plants, such as having fewer natural enemies. Invasive plants often have the ability to alter ecosystem properties after they have become established, which can make it difficult to eliminate the invasive. In principle, this can cause a regime shift that may not be reversed through intense control efforts that increase mortality and reduce growth of the invasive species. Here we use spatially explicit agent-based modeling to simulate the invasion of an introduced tree species into a habitat occupied by a native species. The model describes an invasive tree with fast growth and high seed production and, in addition, produces litter that has a suppressive effect on native seedlings. These are properties, for example, shared by the invasive Melaleuca quinquenervia in southern Florida habitats. We use simulation modeling to test the following logical hypotheses: Partial suppression of native tree seedlings by the invasive tree's litter (1) will accelerate the spread of the invasive tree into native vegetation, (2) will impede efforts to control invasive spread through biocontrol, and (3) can cause a regime shift that is not reversed even if the biocontrol lowers invasive growth and reproduction to levels substantially lower than those of the native species. Additionally, (4) the earlier in the invasion biocontrol is introduced, the more effective it will be in reversing the invasion. The simulations support all four hypotheses. While these results highlight the potential for biocontrol of invasive tree species, our findings also suggest that successful elimination of positive litter feedbacks and invasive spread may critically depend on the timing of control efforts within the invasion process.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2022.109962","usgsCitation":"Lu, Y., DeAngelis, D.L., Xia, J., and Jiang, J., 2022, Modeling the impact of invasive species litter on conditions affecting its spread and potential regime shift: Ecological Modelling, v. 468, 109962, 15 p., https://doi.org/10.1016/j.ecolmodel.2022.109962.","productDescription":"109962, 15 p.","ipdsId":"IP-134210","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"468","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lu, Yuanming","contributorId":298492,"corporation":false,"usgs":false,"family":"Lu","given":"Yuanming","email":"","affiliations":[{"id":35560,"text":"Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":855741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":855742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xia, Junfei","contributorId":298493,"corporation":false,"usgs":false,"family":"Xia","given":"Junfei","email":"","affiliations":[{"id":64593,"text":"Rosenstiel School of Marine and Atmospheric Science, University of Miami","active":true,"usgs":false}],"preferred":false,"id":855743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jiang, Jiang","contributorId":191968,"corporation":false,"usgs":false,"family":"Jiang","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":855744,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230925,"text":"70230925 - 2022 - Space use and site fidelity of wintering whooping cranes on the Texas Gulf Coast","interactions":[],"lastModifiedDate":"2022-07-07T16:52:55.346919","indexId":"70230925","displayToPublicDate":"2022-04-04T08:28:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Space use and site fidelity of wintering whooping cranes on the Texas Gulf Coast","docAbstract":"The Aransas-Wood Buffalo population (the only non-reintroduced, migratory population) of endangered whooping cranes (Grus americana) overwinters along the Texas Gulf Coast, USA. Understanding whooping crane space use on the wintering grounds reveals essential aspects of this species' ecology, which subsequently assists with conservation. Using global positioning system telemetry data from marked whooping cranes during 2009–2017, we fit continuous-time stochastic process models to describe movement and home range using autocorrelated kernel density estimation (AKDE) and explored variation in home range size in relation to age, sex, reproductive status, and drought conditions. We used the Bhattacharyya coefficient of overlap and distance between home range centroids to quantify site fidelity. We examined the effects of time between winter home ranges and the sex of the crane on site fidelity using Bayesian mixed-effects beta regression. Winter whooping crane 95% AKDE home range size averaged 30.1 ± 45.2 (SD) km2 (median = 14.3, range = 1.1–308.6). Home ranges of sub-adult females were approximately 2 times larger than those of sub-adult males or families. As drought worsened, home ranges typically expanded. Between consecutive years, the home ranges of an adult crane exhibited 68 ± 31% overlap (site fidelity), but fidelity to winter sites declined in subsequent winters. The overlap of adult home ranges with the nearest unrelated family averaged 33 ± 28%. As a whooping crane aged, overlap with its winter home range as a juvenile declined, regardless of sex. By 4 years of age, a whooping crane had approximately 14 ± 28% overlap with its juvenile winter home range. Limited evidence suggested male whooping cranes return to within 2 km of their juvenile home range by their fifth winter. Previous data obtained from aerial surveys led ecologists to assume that whooping crane families normally used small areas (~2 km2) and expressed persistent site fidelity. Our analyses showed <8% of families had home ranges ≤2 km2, with the average area 15 times greater, and waning site fidelity over time. Our work represents an analysis of whooping crane home ranges for this population, identifying past misconceptions of winter space use and resulting in better estimates of space requirements for future conservation efforts.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22226","usgsCitation":"Butler, M.J., Stewart, D., Harris, G.M., Bidwell, M., and Pearse, A.T., 2022, Space use and site fidelity of wintering whooping cranes on the Texas Gulf Coast: Journal of Wildlife Management, v. 86, no. 5, e22226, 17 p., https://doi.org/10.1002/jwmg.22226.","productDescription":"e22226, 17 p.","ipdsId":"IP-132895","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":399808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Aransas National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.327880859375,\n 27.858503954841247\n ],\n [\n -96.3006591796875,\n 27.858503954841247\n ],\n [\n -96.3006591796875,\n 28.420391085674304\n ],\n [\n -97.327880859375,\n 28.420391085674304\n ],\n [\n -97.327880859375,\n 27.858503954841247\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Butler, Matthew J","contributorId":239688,"corporation":false,"usgs":false,"family":"Butler","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":841649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":841650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Grant M","contributorId":290710,"corporation":false,"usgs":false,"family":"Harris","given":"Grant","email":"","middleInitial":"M","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":841651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":139204,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark T.","affiliations":[{"id":12696,"text":"Environmental Canada","active":true,"usgs":false}],"preferred":false,"id":841652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":841653,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250536,"text":"70250536 - 2022 - Increased attack rates and decreased incubation periods in raccoons with chronic wasting disease passaged through meadow voles","interactions":[],"lastModifiedDate":"2023-12-15T13:19:00.686284","indexId":"70250536","displayToPublicDate":"2022-04-04T07:17:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Increased attack rates and decreased incubation periods in raccoons with chronic wasting disease passaged through meadow voles","docAbstract":"Chronic wasting disease (CWD) is a naturally-occurring neurodegenerative disease of cervids. Raccoons (Procyon lotor) and meadow voles (Microtus pennsylvanicus) have previously been shown to be susceptible to the CWD agent. To investigate the potential for transmission of the agent of CWD from white-tailed deer to voles and subsequently to raccoons, we intracranially inoculated raccoons with brain homogenate from a CWD-affected white-tailed deer (CWDWtd) or derivatives of this isolate after it had been passaged through voles 1 or 5 times. We found that passage of the CWDWtd isolate through voles led to a change in the biologic behavior of the CWD agent, including increased attack rates and decreased incubation periods in raccoons. A better understanding of the dynamics of cross-species transmission of CWD prions can provide insights into how these infectious proteins evolve in new hosts.
The development of colluvial wedges at the base of fault scarps following normal-faulting earthquakes serves as a sedimentary record of paleoearthquakes and is thus crucial in assessing seismic hazard. Although there is a large body of observations of colluvial wedge development, connecting this knowledge to the physics of sediment transport can open new frontiers in our understanding. To explore theoretical colluvial wedge evolution, we develop a cellular automata model driven by the production and disturbance (e.g., bioturbative reworking) of mobile regolith and fault-scarp collapse. We consider both 90 and 60∘ dipping faults and allow the colluvial wedges to develop over 2000 model years. By tracking sediment transport time, velocity, and provenance, we classify cells into analogs for the debris and wash sedimentary facies commonly described in paleoseismic studies. High values of mobile regolith production and disturbance rates produce relatively larger and more wash-facies-dominated wedges, whereas lower values produced relatively smaller, debris-facies-dominated wedges. Higher lateral collapse rates lead to more debris facies relative to wash facies. Many of the modeled colluvial wedges fully developed within 2000 model years after the earthquake, with many being much faster when process rates are high. Finally, for scenarios with the same amount of vertical displacement, differently sized colluvial wedges developed depending on the rates of geomorphic processes and fault dip. A change in these variables, say by environmental change such as precipitation rates, could theoretically result in different colluvial wedge facies assemblages for the same characteristic earthquake rupture scenario. Finally, the stochastic nature of collapse events, when coupled with high disturbance, illustrates that multiple phases of colluvial deposition are theoretically possible for a single earthquake event.
Birds are highly susceptible to aspergillosis, which can manifest as a primary infection in both domestic and wild birds. Aspergillosis in wild birds causes mortalities ranging in scale from single animals to large-scale epizootic events. However, pathogenicity factors associated with aspergillosis in wild birds have not been examined. Specifically, it is unknown whether wild bird-infecting strains are host-adapted (i.e. phylogenetically related). Similarly, it is unknown whether epizootics are driven by contact with clonal strains that possess unique pathogenic or virulence properties, or by distinct and equally pathogenic strains. Here, we use a diverse collection of Aspergillus fumigatus isolates taken from aspergillosis-associated avian carcasses, representing 24 bird species from a wide geographic range, and representing individual bird mortalities as well as epizootic events. These isolates were sequenced and analyzed along with 130 phylogenetically diverse human clinical isolates to investigate the genetic diversity and phylogenetic placement of avian-associated A. fumigatus, the geographic and host distribution of avian isolates, evidence for clonal outbreaks among wild birds, and the frequency of azole resistance in avian isolates. We found that avian isolates were phylogenetically diverse, with no clear distinction from human clinical isolates, and no sign of host or geographic specificity. Avian isolates from the same epizootic events were diverse and phylogenetically distant, suggesting that avian aspergillosis is not contagious among wild birds and that outbreaks are likely driven by environmental spore loads or host comorbidities. Finally, all avian isolates were susceptible to Voriconazole and none contained the canonical azole resistance gene variants.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/g3journal/jkac075","usgsCitation":"Lofgren, L.A., Lorch, J., Cramer, R.A., Blehert, D.S., Berlowski-Zier, B.M., Winzeler, M., Gutierrez-Perez, C., Kordana, N.E., and Stajich, J.E., 2022, Avian-associated Aspergillus fumigatus displays broad phylogenetic distribution, no evidence for host specificity, and multiple genotypes within epizootic events: G3 Genes|Genomes|Genetics, v. 12, no. 5, jkac075, 8 p., https://doi.org/10.1093/g3journal/jkac075.","productDescription":"jkac075, 8 p.","ipdsId":"IP-138562","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":448263,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/g3journal/jkac075","text":"Publisher Index Page"},{"id":399879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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States\"}}]}","volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Lofgren, Lotus A.","contributorId":290714,"corporation":false,"usgs":false,"family":"Lofgren","given":"Lotus","email":"","middleInitial":"A.","affiliations":[{"id":13325,"text":"University of California Riverside","active":true,"usgs":false}],"preferred":false,"id":841675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":264594,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":841676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cramer, Robert A.","contributorId":290715,"corporation":false,"usgs":false,"family":"Cramer","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":62484,"text":"Dartmouth","active":true,"usgs":false}],"preferred":false,"id":841677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140397,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":841678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berlowski-Zier, Brenda M. 0000-0002-7922-8352 bberlowski-zier@usgs.gov","orcid":"https://orcid.org/0000-0002-7922-8352","contributorId":4288,"corporation":false,"usgs":true,"family":"Berlowski-Zier","given":"Brenda","email":"bberlowski-zier@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":841679,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winzeler, Megan 0000-0002-0361-1582 mwinzeler@usgs.gov","orcid":"https://orcid.org/0000-0002-0361-1582","contributorId":196714,"corporation":false,"usgs":true,"family":"Winzeler","given":"Megan","email":"mwinzeler@usgs.gov","affiliations":[],"preferred":true,"id":841680,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gutierrez-Perez, Cecilia","contributorId":290717,"corporation":false,"usgs":false,"family":"Gutierrez-Perez","given":"Cecilia","email":"","affiliations":[{"id":62484,"text":"Dartmouth","active":true,"usgs":false}],"preferred":false,"id":841681,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kordana, Nicole E.","contributorId":290718,"corporation":false,"usgs":false,"family":"Kordana","given":"Nicole","email":"","middleInitial":"E.","affiliations":[{"id":62484,"text":"Dartmouth","active":true,"usgs":false}],"preferred":false,"id":841682,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stajich, Jason E.","contributorId":225457,"corporation":false,"usgs":false,"family":"Stajich","given":"Jason","email":"","middleInitial":"E.","affiliations":[{"id":41121,"text":"Environmental Genomics, Christian-Albrechts University, Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":841683,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230444,"text":"70230444 - 2022 - Ancient winds, waves, and atmosphere in Gale Crater, Mars, inferred from sedimentary structures and wave modeling","interactions":[],"lastModifiedDate":"2022-04-26T12:21:30.20239","indexId":"70230444","displayToPublicDate":"2022-04-04T06:40:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5718,"text":"Journal of Geophysical Research: Planets","onlineIssn":"2169-9100","active":true,"publicationSubtype":{"id":10}},"title":"Ancient winds, waves, and atmosphere in Gale Crater, Mars, inferred from sedimentary structures and wave modeling","docAbstract":"Wave modeling and analysis of sedimentary structures were used to evaluate whether four examples of symmetrical, reversing, or straight-crested bedforms in Gale crater sandstones are preserved wave ripples; deposition by waves would demonstrate that the lake was not covered by ice at that time. Wave modeling indicates that regardless of atmospheric density, winds that exceeded the threshold of aeolian sand transport could have generated waves capable of producing nearshore wave ripples in most grain sizes of sand.
Reversing 3-m-wavelength bedforms in the Kimberley formation are interpreted not as wave ripples but rather as large aeolian ripples that formed in an atmosphere approximately as thin as at present. These exhumed bedforms define many of the ridges at outcrops that appear striated in satellite images. At Kimberley these bedforms demonstrably underlie and therefore predate subaqueous beds, suggesting that a thin atmosphere existed at least temporarily before subaqueous deposition ceased in the crater.
The other three candidate wave ripples (Square Top, Hunda, and Voe) are consistent with modeled waves, but other origins cannot be excluded. The predominance of flat-laminated (non-rippled) beds in the lacustrine Murray formation suggests that some aspect of the lake was not conducive to formation or preservation of recognizable wave ripples. Water depths may generally have been too deep, lakebed sediment may have been too fine-grained, the lake may have been smaller than modeled, or the lake may have been covered by ice.
Wave modeling and analysis of sedimentary structures were used to evaluate whether ancient lake deposits in Gale crater contain ripples formed by waves on the surface of the lake. Deposition by waves would show that the lake was not covered by ice at that time. Modeling shows that regardless of atmospheric density, winds capable of moving sand on land would generally have been strong enough to form waves that would produce ripples near shore. Large bedforms in the Kimberley formation are interpreted as ripples formed by the wind in an atmosphere similar to that of Mars today. These bedforms underlie and are older than other beds deposited in water, thereby showing that a thin atmosphere existed at least temporarily before deposition in water ceased in the crater. Three other candidate wave ripples are consistent with modeled waves, but other origins are possible. Thick sequences of sedimentary rock in Gale crater are flat-laminated rather than rippled, suggesting that some aspect of the lake was not favorable for their formation or preservation. Much of the lake may have been too deep or ice-covered, or the lake may have been smaller than modeled or had sediment too fine to form easily observed ripples.
Large-scale modelling and prediction provide insight into general influences of climate change on inland recreational fisheries; however, small-scale dynamics and local expertise will be key in developing explicit goals for managing recreational fisheries as the climate changes. The resist-accept-direct (RAD) framework encompasses the entire decision space managers consider when addressing climate influences in their local system, but to decide whether to resist, accept or direct, managers need tools to understand how specific waterbodies will be influenced by climate change. Here, a decision-support tool was developed and applied to the walleye recreational fishery in Wisconsin, USA as an example of how to link the RAD framework to real-world management of a large recreational fishery. The tool and broadscale results described here, indicating a widespread shift away from resist strategies by mid-century, can be used by managers to inform decisions about whether to resist, accept, or direct for specific walleye populations.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12548","usgsCitation":"Dassow, C.J., Latzka, A., Lynch, A., Sass, G., Tingley, R.W., and Paukert, C.P., 2022, A Resist-Accept-Direct decision-support tool for walleye Sander vitreus (Mitchill) management in Wisconsin: Fisheries Management and Ecology, v. 29, no. 4, p. 378-391, https://doi.org/10.1111/fme.12548.","productDescription":"14 p.","startPage":"378","endPage":"391","ipdsId":"IP-135211","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":435895,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P907C81Z","text":"USGS data release","linkHelpText":"A resist-accept-direct tool for walleye (Sander vitreus) management in Wisconsin"},{"id":403896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"29","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Dassow, Colin J.","contributorId":293206,"corporation":false,"usgs":false,"family":"Dassow","given":"Colin","email":"","middleInitial":"J.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":846730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Latzka, Alex W.","contributorId":293207,"corporation":false,"usgs":false,"family":"Latzka","given":"Alex W.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":846731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":846732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sass, Greg G.","contributorId":244466,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":846733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tingley, Ralph W. III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":846734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":846735,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231530,"text":"70231530 - 2022 - Population reproductive structure of Rainbow Trout determined by histology and advancing methods to assign sex and assess spawning capability","interactions":[],"lastModifiedDate":"2022-08-02T14:19:07.727681","indexId":"70231530","displayToPublicDate":"2022-04-03T08:32:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Population reproductive structure of Rainbow Trout determined by histology and advancing methods to assign sex and assess spawning capability","docAbstract":"Rainbow Trout Oncorhynchus mykiss have been intensively studied and gametogenesis has been described, but the use of reproductive indices in field studies has not been widely applied when assessing variability in growth or recruitment dynamics. We integrated descriptions for gametogenesis within the framework of standardized terminology for reproductive development in teleosts to develop sex-specific criteria for assignment of reproductive phases. We used these descriptions and histological analysis of gonad tissue collected from Rainbow Trout in the Colorado River downstream from Glen Canyon Dam to quantify season-, size-, and sex-specific variation in population reproductive structure. The accuracy of nonlethal methods (manual expression and ultrasonography) was evaluated for assigning sex by comparing estimates with those determined by histology. Rainbow Trout were sampled through an annual spawning cycle from October 2018 to April 2019. Spawning capable males were available across the entire period, with a higher proportion earlier in the season compared to later. Females were spawning capable in October, with a peak in February; by April, they were in the early developing phase, indicating that spawning had ended. Elevated levels of atresia (19% for fall spawners) and evidence for delayed maturation were identified, suggesting energetic limitations on the reproductive potential of the population. For both sexes, the probability of being spawning capable increased with fork length, with minimum sizes of ≥283 mm for females and ≥187 mm for males. Sex assignment using ultrasonography was more accurate (46%) than manual expression of gametes (9%), as only a small proportion of males and females expressed gametes. Probabilities of correct sex assignment using ultrasonography were strongly influenced by reproductive phase, with spawning capable fish (females: 100%; males: 77%) having significantly higher probabilities of correct sex assignment compared to immature fish. Furthermore, probabilities of correct sex assignment increased with fish size and were higher for females than for males. Results provide a framework for quantifying spawning capability and population reproductive structure in ongoing research to better understand the drivers of recruitment variability in aquatic ecosystems.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10356","usgsCitation":"Crossman, J.A., Webb, M., Korman, J., and Yard, M., 2022, Population reproductive structure of Rainbow Trout determined by histology and advancing methods to assign sex and assess spawning capability: Transactions of the American Fisheries Society, v. 151, no. 4, p. 422-440, https://doi.org/10.1002/tafs.10356.","productDescription":"19 p.","startPage":"422","endPage":"440","ipdsId":"IP-134953","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435896,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CLR26O","text":"USGS data release","linkHelpText":"Rainbow trout reproduction data from 3 sampling trips (2018-2019) within Glen Canyon, AZ"},{"id":400576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon Dam tailwater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.61972045898438,\n 36.804886560237236\n ],\n [\n -111.50711059570311,\n 36.837866465399735\n ],\n [\n -111.46041870117186,\n 36.923547681089296\n ],\n [\n -111.46041870117186,\n 36.934525498514894\n ],\n [\n -111.48788452148438,\n 36.9367208722872\n ],\n [\n -111.6595458984375,\n 36.83566824724438\n ],\n [\n -111.61972045898438,\n 36.804886560237236\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Crossman, James A.","contributorId":288696,"corporation":false,"usgs":false,"family":"Crossman","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":37568,"text":"BC Hydro","active":true,"usgs":false}],"preferred":false,"id":842937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Molly A. H.","contributorId":193590,"corporation":false,"usgs":false,"family":"Webb","given":"Molly A. H.","affiliations":[],"preferred":false,"id":842938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":842939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yard, Michael D. 0000-0002-6580-6027","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":291738,"corporation":false,"usgs":false,"family":"Yard","given":"Michael D.","affiliations":[{"id":62744,"text":"Retired, US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":842940,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239266,"text":"70239266 - 2022 - Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study","interactions":[],"lastModifiedDate":"2023-01-06T14:28:10.804018","indexId":"70239266","displayToPublicDate":"2022-04-03T08:16:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study","docAbstract":"Spatial capture–recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture–recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km2. Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture–recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2618","usgsCitation":"Schmidt, G.M., Graves, T., Pederson, J.C., and Carroll, S.L., 2022, Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study: Ecological Applications, v. 32, no. 5, e2618, 19 p., https://doi.org/10.1002/eap.2618.","productDescription":"e2618, 19 p.","ipdsId":"IP-126268","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":448272,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2618","text":"Publisher Index Page"},{"id":411485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.1113551477145,\n 40.75776766966564\n ],\n [\n -112.1113551477145,\n 37.522762898285166\n ],\n [\n -109.09220756712764,\n 37.522762898285166\n ],\n [\n -109.09220756712764,\n 40.75776766966564\n ],\n [\n -112.1113551477145,\n 40.75776766966564\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Greta M","contributorId":300615,"corporation":false,"usgs":false,"family":"Schmidt","given":"Greta","email":"","middleInitial":"M","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":860956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":860957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Jordan C","contributorId":300616,"corporation":false,"usgs":false,"family":"Pederson","given":"Jordan","email":"","middleInitial":"C","affiliations":[{"id":65213,"text":"Utah Department of Natural Resources, retired","active":true,"usgs":false}],"preferred":false,"id":860958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Sarah L","contributorId":300618,"corporation":false,"usgs":false,"family":"Carroll","given":"Sarah","email":"","middleInitial":"L","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":860959,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230357,"text":"70230357 - 2022 - Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","interactions":[],"lastModifiedDate":"2022-04-08T12:04:03.799162","indexId":"70230357","displayToPublicDate":"2022-04-03T07:00:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","docAbstract":"For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow-water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel subsites in the Florida Bay (application site) under a different time period (application period). We used foraging little blue herons (Egretta caerulea) as the ecological unit with which to constrain the model's “water depth window,” that is, range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability, which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by subsite and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow water has implications for monitoring habitat conditions for tidally influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.
The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.
Thermal response of the surface to solar insolation is a function of the topography and the thermal physical characteristics of the landscape, which include bulk density, heat capacity, thermal conductivity and surface albedo and emissivity. Thermal imaging is routinely used to constrain thermal physical properties by characterizing or modeling changes in the diurnal temperature profiles. Images need to be acquired throughout the diurnal cycle – typically this is done twice during a diurnal cycle, but we suggest multiple times. Comparison of images acquired over 24 hours requires that either the data be calibrated to surface temperature, or the response of the thermal camera is linear and stable over the image acquisition period. Depending on the type and age of the thermal instrument, imagery may be self-calibrated in radiance, corrected for atmospheric effects, and pixels converted to surface temperature. We used an experimental instrumentation where the calibration should be stable, but calibration coefficients are unknown. Cases may occur where one wishes to validate the camera's calibration. We present a method to validate and calibrate the instrument and characterize the thermal physical properties for areas of interest. Finally, in situ high-temporal-resolution oblique thermal imaging can be invaluable in preparation for conducting overflight missions. We present the following:
The use of oblique thermal high temporal resolution thermal imaging over diurnal or multiday periods for the characterization of landscapes has not been widespread but poses great potential.
A method of collecting and analyzing thermal data that can be used to either determine or validate thermal camera calibration coefficients.
An approach to characterize thermophysical properties of the landscape using oblique temporally high-resolution thermal imaging, combined with in situ ground measurements.