Climate change vulnerability assessments and associated adaptation strategies and actions connect existing climate science with possible effects on natural resources and highlight potential responses. However, these assessments, which are commonly generated for large regional areas, suggest management options in general terms without guidance for choosing among strategies and actions under specific circumstances. Meanwhile, land and resource management plans1 often address smaller geographies, and management actions must address specific rather than general situations. Thus, there is a need for tools that enable managers to bridge the gap by downscaling assessments, plans, and data generated at regional scales to identify adaptation actions and strategies appropriate for smaller management units and project-level planning.
To address this need, we have developed a tool–the Climate Adaptation Integration Tool (CAIT)–that helps resource managers use climate science and assessments, along with local knowledge, to identify those adaptation strategies and actions most appropriate for a given site or situation. Specifically, we provide:
The CAIT is meant to help managers integrate climate change science and assessments into management decisions. The CAIT also serves as a way for managers to document how they have incorporated climate change information into their decision-making and why certain actions were selected over others. A particular strength of the CAIT is that it leads to potential solutions (that is, adaptation strategies and actions) without inflexibly prescribing actions. This flexibility enables managers to incorporate other factors and constraints to create workable management plans and projects that strengthen their ability to achieve long-term conservation goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6C2","usgsCitation":"Kershner, J., Woodward, A., and Torregrosa, A., 2020, Integrating climate change considerations into natural resource planning—An implementation guide: U.S. Geological Survey Techniques and Methods, book 6, chap. C2, 58 p., https://doi.org/10.3133/tm6C2.","productDescription":"v, 58 p.","onlineOnly":"Y","ipdsId":"IP-106677","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":378927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/6c2/coverthb.jpg"},{"id":378928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/6c2/tm6c2.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-C2"}],"contact":"Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Covallis, Oregon 97330
A long history of mining and widespread metals contamination in the Coeur d’Alene River watershed and downstream into the Spokane River has led to the area’s designation as a Superfund site and to extensive, ongoing (as of 2020) remedial actions. Long-term water-quality and streamflow data, collected by the U.S. Geological Survey for up to 29 years at 20 sampling sites in the Coeur d’Alene, Spokane and St. Joe River watersheds, were analyzed to evaluate the impact of remedial actions on metals in surface water. Analyses focused on total and dissolved cadmium, zinc and lead. Trends in total phosphorus, total nitrogen and dissolved orthophosphate were also evaluated; although these nutrients are not constituents of concern for the Superfund site, they are important to the health of Coeur d’Alene Lake.
Dissolved cadmium, zinc and lead concentrations were compared to ambient water-quality criteria at 20 sample sites. For the 12 sites with the most extensive data records, Weighted Regressions on Time, Discharge and Season (WRTDS) models were developed to estimate flow-normalized annual mean concentrations and flow-normalized annual total loads; these results were used to evaluate trends because flow-normalization dampens the impact of interannual streamflow variability on concentrations and loads. WRTDS models with Kalman filtering (WRTDS_K) were developed to estimate annual mean concentrations and annual total loads; these results were used to evaluate spatial patterns in constituent sources. Models were developed for total and dissolved cadmium, lead, and zinc; total phosphorus and nitrogen; and dissolved orthophosphate, although not all constituents were modeled for all sites due to limited sample sizes. Bootstrapped confidence intervals were constructed to determine the statistical likelihood of trends and the slope of trends in flow-normalized concentrations and loads during the period of record (13–29 years, depending on the site), water years 1999–2009, and water years 2009–18.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205096","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Zinsser, L.M., 2020, Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990-2018: U.S. Geological Survey Scientific Investigations Report 2020–5096, 58 p., https://doi.org/10.3133/sir20205096.","productDescription":"Report: vii, 58 p.; Appendix 1-2; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116912","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":378922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5096/coverthb.jpg"},{"id":378923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096"},{"id":378924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix1.pdf","text":"Appendix 1","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 1"},{"id":378925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix2.pdf","text":"Appendix 2","size":"35.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 2"},{"id":378926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LNE8J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"WRTDS annual concentrations, loads and statistical trend likelihoods for sites in the Spokane River watershed, water years 1990-2018"}],"country":"United States","state":"Idaho","otherGeospatial":"Spokane River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.18017578125,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 46.98025235521883\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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During water years (WY) 2013–2017, the U.S. Geological Survey, National Water-Quality Assessment (NAWQA) Project, sampled the National Water Quality Network – Rivers and Streams (NWQN) year-round and reported on 221 pesticides at 72 sites across the United States in agricultural, developed, and mixed land use watersheds. The Pesticide Toxicity Index (PTI) was used to estimate the potential chronic and acute toxicity to three taxonomic groups – fish, cladocerans, and benthic invertebrates. For invertebrates (either cladocerans, benthic invertebrates, or both), the maximum PTI score exceeded the predicted acute toxicity screening level at 18 of the 72 sites (25%) at some point during WY 2013–2017. The predicted toxicity of a single pesticide compound was found to overwhelm the toxicity of other pesticides in the mixtures after concentrations were toxicity weighted. For this study, about 71%, 72%, and 92% of the Fish-, Cladoceran-, and Benthic Invertebrate-PTI scores, respectively, had one pesticide compound primarily contributing to sample potential toxicity (>50%).
There were 17 (13 insecticides, 2 herbicides, 1 fungicide, and 1 synergist) of the 221 pesticide compounds analyzed that were the primary drivers of potential toxicity in each water sample in which the PTI and TUmax (toxic unit score for the pesticide that makes the single largest contribution to the PTI) scores were above predicted chronic (>0.1) or acute (>1) toxicity levels for one of the three taxa. For cladocerans and benthic invertebrates, the drivers of predicted chronic (>0.1) and acute (>1) PTIs were mostly insecticides. For cladocerans, the pesticide compounds driving the PTI scores were bifenthrin, carbaryl, chlorpyrifos, diazinon, dichlorvos, dicrotophos, diflubenzuron, flubendiamide, and tebupirimfos. For benthic invertebrates, atrazine (an herbicide), as well as the insecticides – bifenthrin, carbaryl, carbofuran, chlorpyrifos, diazinon, dichlorvos, fipronil, imidacloprid, and methamidophos – were the drivers of predicted toxicity. For fish, there were three pesticide types that contributed the most to predicted chronic (>0.1) PTIs – acetochlor, an herbicide; carbendazim, a fungicide degradate; and piperonylbutoxide, a synergist.
","language":"English","doi":"10.1016/j.scitotenv.2020.141285","usgsCitation":"Covert, S.A., Shoda, M.E., Stackpoole, S.M., and Stone, W.W., 2020, Pesticide mixtures show potential toxicity to aquatic life in U.S. streams, water years 2013-2017: Science of the Total Environment, v. 745, 141285, 12 p., https://doi.org/10.1016/j.scitotenv.2020.141285.","productDescription":"141285, 12 p.","ipdsId":"IP-117042","costCenters":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":455178,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.141285","text":"Publisher Index Page"},{"id":436772,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T86C5U","text":"USGS data 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The increased availability of high-resolution elevation data from light detection and ranging (lidar) has helped improve methods for extracting these metrics at a greater accuracy across regional scales. The Floodplain and Channel Evaluation Tool (FACET) was developed as an open source tool to calculate a suite of geomorphic metrics describing channel and floodplain geometry from high-resolution digital elevation models (DEMs), providing estimates of channel width, bank height, cross-sectional area, and floodplain extent. Field data from sites in the Chesapeake Bay and Delaware River watersheds were used to calibrate and validate FACET within five physiographic provinces in the Mid-Atlantic region of the United States. Stream banks were identified using either a slope-threshold method at cross sections which are automatically generated at a user-defined interval along the delineated stream network, or by applying a curvature-threshold method for grid cells within a buffered distance from the stream network. The floodplain extent was mapped using a height above nearest drainage (HAND) grid and empirical regression models built for each physiographic province relating the HAND threshold to drainage area. Other user-defined input parameters within FACET control the sensitivity of calculations to DEM resolution, relief, and stream order, allowing for the ability to optimize FACET at multiple scales and/or regions if field survey data are available for calibration. 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Expedition operations included testing two configurations of a rotary pressure-coring tool in a gas hydrate–bearing formation. In the first hole, an extended core barrel (cutting shoe) configuration of the Pressure Coring Tool with Ball Valve (PCTB-CS) was deployed, and in the second hole, the PCTB face bit configuration (PCTB-FB) was deployed. The PCTB-CS successfully recovered and maintained pressure for only one core out of eight deployments. A series of incremental modifications were made during and after the PCTB-CS deployment period that impacted the operations of the subsequent PCTB-FB deployments. Thus, in the second hole, the PCTB-FB successfully recovered and maintained pressure within the hydrate stability zone for 11 cores out of 13 deployments. The PCTB cored gas hydrate–bearing sandy silt interbedded with non–hydrate-bearing clayey silt within the main reservoir. The PCTB also recovered long intervals of unbroken, high-quality core with preserved sedimentary structures. 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Each year, the Federal Highway Administration, in partnership with State Departments of Transportation, undertake a massive exercise to update the National Bridge Inventory (NBI) by combining data from states, federal agencies, local jurisdictions, and tribal governments. The NBI captures pertinent details related to individual bridges (e.g., their usage, repairs, or retrofits). We make use of the 2018 NBI that contain the necessary engineering attributes needed to assign the appropriate Hazus bridge class for each bridge, which can then be used for engineering risk analyses. Basic structural data, component dimensions, and regional replacement cost factors are used to develop an economic exposure model. This is a significant improvement over previous replacement costs, and as a result of this study, results are now available within the Federal Emergency Management Agency’s Hazus platform. Earthquake hazard is defined using the U.S. Geological Survey’s 2018 National Seismic Hazard Model. For each bridge location, we obtain an earthquake shaking hazard curve defined in terms of spectral acceleration at a vibration period of 1.0 sec, ensuring that it properly reflects the site-specific soil conditions. We then integrate it with the bridge-specific fragility curve to compute annual probabilities of exceeding various damage states. Next, we perform economic loss analyses using the repair costs associated with specific damage states, resulting in an estimate of mean total annual financial loss for each bridge; this long-term measure of seismic risk enables us to illustrate the distribution of overall financial risk with respect to geographical region, era of construction, or type of bridge.
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S.","contributorId":265964,"corporation":false,"usgs":false,"family":"Yen","given":"S.","email":"","middleInitial":"S.","affiliations":[{"id":54842,"text":"Caltrans Division of Research, Innovation and System Information","active":true,"usgs":false}],"preferred":false,"id":823873,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bausch, D.","contributorId":265965,"corporation":false,"usgs":false,"family":"Bausch","given":"D.","affiliations":[{"id":54845,"text":"NiyamIT Inc.","active":true,"usgs":false}],"preferred":false,"id":823874,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, Kuo-wan 0000-0002-7520-8151 klin@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-8151","contributorId":1539,"corporation":false,"usgs":true,"family":"Lin","given":"Kuo-wan","email":"klin@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823876,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823877,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rozelle, J.","contributorId":265966,"corporation":false,"usgs":false,"family":"Rozelle","given":"J.","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":823878,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216548,"text":"70216548 - 2020 - Photosynthetic and respiratory acclimation of understory shrubs in response to in situ experimental warming of a wet tropical forest","interactions":[],"lastModifiedDate":"2020-11-25T16:30:19.20556","indexId":"70216548","displayToPublicDate":"2020-09-30T10:15:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Photosynthetic and respiratory acclimation of understory shrubs in response to in situ experimental warming of a wet tropical forest","title":"Photosynthetic and respiratory acclimation of understory shrubs in response to in situ experimental warming of a wet tropical forest","docAbstract":"Despite the importance of tropical forests to global carbon balance, our understanding of how tropical plant physiology will respond to climate warming is limited. In addition, the contribution of tropical forest understories to global carbon cycling is predicted to increase with rising temperatures, however, in situ warming studies of tropical forest plants to date focus only on upper canopies. We present results of an in situ field-scale +4°C understory infrared warming experiment in Puerto Rico (Tropical Responses to Altered Climate Experiment; TRACE). We investigated gas exchange responses of two common understory shrubs, Psychotria brachiata and Piper glabrescens, after exposure to 4 and 8 months warming. We assessed physiological acclimation in two ways: (1) by comparing plot-level physiological responses in heated versus control treatments before and after warming, and (2) by examining physiological responses of individual plants to variation in environmental drivers across all plots, seasons, and treatments. P. brachiata has the capacity to up-regulate (i.e., acclimate) photosynthesis through broadened thermal niche and up-regulation of photosynthetic temperature optimum (Topt) with warmer temperatures. P. glabrescens, however, did not upregulate any photosynthetic parameter, but rather experienced declines in the rate of photosynthesis at the optimum temperature (Aopt), corresponding with lower stomatal conductance under warmer daily temperatures. Contrary to expectation, neither species showed strong evidence for respiratory acclimation. P. brachiata down-regulated basal respiration with warmer daily temperatures during the drier winter months only. P. glabrescens showed no evidence of respiratory acclimation. Unexpectedly, soil moisture, was the strongest environmental driver of daily physiological temperature responses, not vegetation temperature. Topt increased, while photosynthesis and basal respiration declined as soils dried, suggesting that drier conditions negatively affected carbon uptake for both species. Overall, P. brachiata, an early successional shrub, showed higher acclimation potential to daily temperature variations, potentially mitigating negative effects of chronic warming. The negative photosynthetic response to warming experienced by P. glabrescens, a mid-successional shrub, suggests that this species may not be able to as successfully tolerate future, warmer temperatures. These results highlight the importance of considering species when assessing climate change and relay the importance of soil moisture on plant function in large-scale warming experiments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2020.576320","usgsCitation":"Carter, K.R., Wood, T.E., Reed, S., Schwartz, E.C., Reinsel, M.B., and Yang, X., 2020, Photosynthetic and respiratory acclimation of understory shrubs in response to in situ experimental warming of a wet tropical forest: Frontiers in Forests and Global Change, v. 3, 576320, 20 p., https://doi.org/10.3389/ffgc.2020.576320.","productDescription":"576320, 20 p.","ipdsId":"IP-117228","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455183,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2020.576320","text":"Publisher Index Page"},{"id":380786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Luquillo Experimental Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.84981918334961,\n 18.28877145291284\n ],\n [\n -65.82012176513672,\n 18.28877145291284\n ],\n [\n -65.82012176513672,\n 18.312810846425442\n ],\n [\n -65.84981918334961,\n 18.312810846425442\n ],\n [\n -65.84981918334961,\n 18.28877145291284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2020-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Kelsey R.","contributorId":245234,"corporation":false,"usgs":false,"family":"Carter","given":"Kelsey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":805580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Tana E.","contributorId":197805,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","middleInitial":"E.","affiliations":[],"preferred":false,"id":805581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwartz, Elsa C.","contributorId":245235,"corporation":false,"usgs":false,"family":"Schwartz","given":"Elsa","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":805583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reinsel, Madeline B.","contributorId":245236,"corporation":false,"usgs":false,"family":"Reinsel","given":"Madeline","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":805655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":805656,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270772,"text":"70270772 - 2020 - Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions","interactions":[],"lastModifiedDate":"2025-08-28T15:18:35.030108","indexId":"70270772","displayToPublicDate":"2020-09-30T10:12:46","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-146-2020","title":"Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions","docAbstract":"Limited information is known about factors driving the distribution of Least Darter in Oklahaoma. The Least Darter occurs in the Ozark Highland and Arbuckle Uplift ecoregions of Oklahoma which represent the southern extent of its range. Least Darter was historically recorded in Oklahoma from groundwater-fed streams. Our study objectives were to determine the distribution of Least Darter and a subset of congeners across the two ecoregions of Oklahoma and assess factors driving patch occupancy of Least Darter at a fine spatial scale. We used temporally replicated snorkel surveys conducted in July through October 2018-2019 to determine occupany by Least Darter. We snorkeled and seined for four species in each reach including two life stages of Smallmouth Bass (subadult and adult). We sampled 153 sites (i.e., riffle-pool complexes) nested within 61 stream reaches (i.e., 200-500-m long) in the Arbuckle Uplift and Ozark Highland ecoregions. Detection probability was similar between ecoregions. Least Darter was detected at more sites when snorkeling compared to seining (24 versus 18). Smallmouth Bass, Redspot Chub and Southern Redbelly Dace were typically 2-3 times more likely to be detected by snorkeling than by seining. We found relationships between occupancy and habitat parameters that were both shared among species but also species-specific. Least Darter occurrence probability in the Ozark Highlands was lower than in the Arbuckle Uplift. Occurrence probability was higher for subadult Smallmouth Bass and Southern Redbelly Dace in 2018 compared to 2019. Occurrence probabilities of both Least Darter and Southern Redbelly Dace were higher in cooler habitat patches. Southern Redbelly Dace was negatively associated with a higher proportion of pool habitat across a reach. Lastly, subadult Smallmouth Bass and Redspot Chub were more likely to occur in deeper pools and in larger streams (i.e., drainage area). We sampled one study reach (~150-m long with shallow riffles or a waterfall on each end) in the Arbuckle Uplift (winter and summer sampling) and Ozark Highland (winter sampling) ecoregions to determine fine-scale habitat selection during the thermally harsh seasons. We developed transects across the reaches to quantify depth, velocity, substrate, cover and water temperature. We found Least Darter used higher water column velocities and shallower water depths with little vegetation during the winter. The average water depth used was similar during summer and winter (~ 20 cm deep). Least Darter used denser vegetation during the summer and tended to avoid coarse substrates in both seasons. If the conservation of Least Darter is a management goal, actions to mitigate increasing stream water temperatures (e.g., protection of springs and riparian corridors) and protecting stream morphologies that facilitate species separations (i.e., allow for a wide range of water depth and velocities) may be beneficial (e.g., fencing cattle from streams, promoting natural bankful flows during spring)
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css59114986","usgsCitation":"Brewer, S., Sewdberg, D., Mollenhauer, R., and Dattilo, J., 2020, Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions: Cooperator Science Series CSS-146-2020, 63 p., https://doi.org/10.3996/css59114986.","productDescription":"63 p.","ipdsId":"IP-124601","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ozark and Arbuckle Mountain ecoregions","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.48968658701178,\n 37.07593633608397\n ],\n [\n -98.34921377661617,\n 37.07593633608397\n ],\n [\n -98.34921377661617,\n 33.75112170322656\n ],\n [\n -94.48968658701178,\n 33.75112170322656\n ],\n [\n -94.48968658701178,\n 37.07593633608397\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sewdberg, D.","contributorId":360422,"corporation":false,"usgs":false,"family":"Sewdberg","given":"D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dattilo, J.","contributorId":274267,"corporation":false,"usgs":false,"family":"Dattilo","given":"J.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947046,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215221,"text":"70215221 - 2020 - An ecological and conservation perspective","interactions":[],"lastModifiedDate":"2021-01-25T16:00:41.809018","indexId":"70215221","displayToPublicDate":"2020-09-30T09:54:57","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"An ecological and conservation perspective","docAbstract":"Natural ecosystems are facing unprecedented threats which directly threaten human well-being through decreases in critical ecosystem services (IPBES 2019). The top five drivers causing the largest global impacts to biodiversity and ecosystem services include: 1) changes in land and sea use; 2) direct exploitation of organisms; 3) climate change; 4) pollution, and 5) invasive alien species (IPBES 2019). Although One Health acknowledges the link between the health of humans, animals, and the environment, One Health discussions have historically focused on the prevention and control of infectious disease at the human-animal interface rather than these large-scale drivers of health. While One Health has succeeded in bringing awareness to the need for proactive disease control measures such as strengthened biosecurity and vaccine development (e.g., Machalaba et al., 2018; Middleton et al., 2014), disease is only one component of health. In this chapter, we explore the potential for One Health to shift its focus from disease prevention to health promotion to more fully integrate solutions that protect the health of humans, animals, and the ecosystems on which we all depend for our economies, livelihoods, food security, and health. This shift will facilitate a more seamless inclusion of ecological health and environmental conservation in the One Health paradigm and can serve as the basis for a comprehensive approach to complex problems at the root of global health. We also suggest a framework for creating and applying health metrics for wildlife and ecological systems that will be essential for measuring the success of actions aimed at maintaining or shifting systems to desired states.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"One health: The theory and practice of integrated health approaches","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CABI","usgsCitation":"White, C.L., Lankton, J.S., Walsh, D.P., Sleeman, J.M., and Stephen, C., 2020, An ecological and conservation perspective, chap. 3 of One health: The theory and practice of integrated health approaches, p. 25-38.","productDescription":"14 p.","startPage":"25","endPage":"38","ipdsId":"IP-114861","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":382551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"2nd Edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, C. LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":801223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":801224,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":801225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":801226,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stephen, Craig","contributorId":168939,"corporation":false,"usgs":false,"family":"Stephen","given":"Craig","email":"","affiliations":[],"preferred":false,"id":801227,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217168,"text":"70217168 - 2020 - Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy","interactions":[],"lastModifiedDate":"2021-01-08T15:59:46.702087","indexId":"70217168","displayToPublicDate":"2020-09-30T09:44:59","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ST-2017-1751-01","title":"Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy","docAbstract":"The goal of this research was to examine the impacts of Grade Control Structure (GCS) installations at the Heard Scout Pueblo (HSP) study site in the City of Phoenix, Arizona, USA. The study site is around a high-use trail system and is comprised of eroded and incised channels that conduct high flows and associated sediments into a residential neighborhood downstream, a noted stormwater control problem. We established baseline conditions associated with rainfall/runoff response before structures were installed so we could have some data for comparison afterwards.
Innovative monitoring equipment, including video cameras and pressure transducers (to calculate discharge); digital terrain models, sediment samplers and sediment chains (to measure erosion and deposition); soil moisture sensors in monitoring wells (to document infiltration and potential recharge); and weather stations (to track temperature and relative humidity) were established and a small Unmanned Aircraft System (sUAS) survey was completed by July, 11, 2017, in time for the typical summer monsoon season which officially runs from June 15th to September 30th. Only one pre-GCS installation rain event incurred a significant flow event (October 13, 2018).
Natural Channel Design (NCD), a landscape restoration company with decades of experience, was hired through a competitive bid process to develop a novel layout of ~30 GCS installations (sills, modified one-rock dams (ORD), and plugs, as well as a modified Zuni-bowl). The American Conservation Experience (ACE) hand-built the structures based on these designs in the main channel from November 13, 2018 through December 1, 2018. ACE built another ten structures in locations adjacent to the channel from January 15 through January 18, 2019. NCD worked with the landscape forensics to identify a historic channel and reinstate it using GCS.
A surface-water model was also applied, using some of the baseline measurements (terrain and hydraulic conductivity) to track the flows of water and potential infiltration associated with rainfall events before GCS installation, to assist NCD in their design. The same model was applied using the installed GCS locations to simulate impacts of the structures on flow and infiltration. Our model was able to predict the slight reduction and delay in peak flows for small events and simulate infiltration, which was measured and occurred in the channel. Results demonstrated that structures could increase infiltration by ~15% over time. More data describing geomorphology and hydrology after repeated rainfall events will allow for increased analyses.
Innovative monitoring, including the large‐scale particle image velocimetry (LSPIV) were invaluable to this research. Given the arid-land location and added drought conditions, the water levels were not high enough to compute, even using the continuous slope-area method, so discharge was calculated solely using the LSPIV. The careful redundancy of data acquisition is extremely important when studying dryland hydrology.
Weather data indicated that the HSP GCS installations created roughly a three-degree microclimate cooling effect for at least two days following rainfall events, as compared with the untreated channel. The cooling was attributed to increased moisture, evaporation, and latent heat expulsion from the evaporation.
","language":"English","publisher":"Bureau of Reclamation","usgsCitation":"Tosline, D., Norman, L., Greimann, B.P., Cederberg, J., Huang, V., and Ruddell, B., 2020, Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy: Final Report ST-2017-1751-01, iv, 65 p.","productDescription":"iv, 65 p.","ipdsId":"IP-121918","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":382021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382013,"type":{"id":15,"text":"Index Page"},"url":"https://data.usbr.gov/catalog/4414/item/6298"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.09899902343749,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.293803558346596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tosline, Deborah","contributorId":247510,"corporation":false,"usgs":false,"family":"Tosline","given":"Deborah","affiliations":[{"id":49564,"text":"Reclamation, Hydrologist / Program Manager","active":true,"usgs":false}],"preferred":false,"id":807809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":807810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greimann, Blair P.","contributorId":247511,"corporation":false,"usgs":false,"family":"Greimann","given":"Blair","email":"","middleInitial":"P.","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cederberg, Jay 0000-0001-6649-7353","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":219724,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Victor","contributorId":247512,"corporation":false,"usgs":false,"family":"Huang","given":"Victor","email":"","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807813,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruddell, Benjamin L.","contributorId":247513,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin L.","affiliations":[{"id":49567,"text":"Northern Arizona University, Professor","active":true,"usgs":false}],"preferred":false,"id":807814,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227669,"text":"70227669 - 2020 - Assessing the efficacy of protected and multiple-use lands for bird conservation in the U.S.","interactions":[],"lastModifiedDate":"2022-01-26T15:41:46.426505","indexId":"70227669","displayToPublicDate":"2020-09-30T09:36:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the efficacy of protected and multiple-use lands for bird conservation in the U.S.","docAbstract":"Setting land aside has long been a primary approach for protecting biodiversity; however, the efficacy of this approach has been questioned. We examined whether protecting lands positively influences bird species in the U.S., and thus overall biodiversity. We used the North American Breeding Bird Survey and Protected Areas Database of the U.S. to assess effects of protected and multiple-use lands on the prevalence and long-term population trends of imperiled and non-imperiled bird species. We evaluated whether both presence and proportional area of protected and multiple-use lands surrounding survey routes affected prevalence and population trends for imperiled and non-imperiled species. Regarding presence of these lands surrounding these survey routes, our results suggest that imperiled and non-imperiled species are using the combination of protected and multiple-use lands more than undesignated lands. We found no difference between protected and multiple-use lands. Mean population trends were negative for imperiled species in all land categories and did not differ between the land categories. Regarding proportion of protected lands surrounding the survey routes, we found that neither the prevalence nor population trends of imperiled or non-imperiled species was positively associated with any land category. We conclude that, although many species (in both groups) tend to be using these protected and multiple-use lands more frequently than undesignated lands, this protection does not appear to improve population trends. Our results may be influenced by external pressures (e.g., habitat fragmentation), the size of protected lands, the high mobility of birds that allows them to use a combination of all land categories, and management strategies that result in similar habitat between protected and multiple-use lands, or our approach to detect limited relationships. Overall, our results suggest that the combination of protected and multiple-use lands is insufficient, alone, to prevent declines in avian biodiversity at a national scale.
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Lynnette","contributorId":272176,"corporation":false,"usgs":false,"family":"Dornak","given":"L.","email":"","middleInitial":"Lynnette","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":831670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aycrigg, Jocelyn L.","contributorId":272177,"corporation":false,"usgs":false,"family":"Aycrigg","given":"Jocelyn L.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":831671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":831672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831669,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215583,"text":"70215583 - 2020 - Comparative genomics and genomic epidemiology of mycobacterium avium subsp. paratuberculosis strains","interactions":[],"lastModifiedDate":"2020-10-23T14:26:28.658783","indexId":"70215583","displayToPublicDate":"2020-09-30T09:25:35","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"6","title":"Comparative genomics and genomic epidemiology of mycobacterium avium subsp. paratuberculosis strains","docAbstract":"Two phenotypically distinct strains of Mycobacterium avium subsp. paratuberculosis (MAP) were recognized in the 1930s but it was not until the introduction of restriction endonuclease analysis (REA) in the mid-1980s that these two strains, MAP-C and MAP-S, could be distinguished genetically. Since then, a plethora of molecular typing techniques has been applied to MAP isolates (reviewed by Li et al. 2016; Fawzy et al., 2018) and a complex nomenclature for MAP strains has evolved. Currently, the most widely used genotyping method is Mycobacterial Interspersed Repetitive Units – Variable-Number Tandem Repeats (MIRU-VNTR). However, it has limited discriminatory power within the major lineages and does not always accurately reflect genetic relatedness since the repeat sequences are subject to homoplasy (Ahlstrom et al., 2015; Bryant et al., 2016). Whole genome sequencing (WGS) supplies the ultimate resolution and has revolutionized MAP research. It has enabled determination of single nucleotide polymorphism (SNP) level diversity, clarified phylogenetic relationships between divergent lineages and closely related strains and spawned the development of novel genotyping methods based on informative canonical SNPs (Leão et al. 2016; Ahlstrom et al. 2016b). This chapter presents an overview of comparative genomics and epidemiology of MAP strains and also highlights the role that WGS has played in increasing our understanding of MAP strain diversity.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Paratuberculosis: Organism, disease, control","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CAB International","usgsCitation":"Stevenson, K., and Ahlstrom, C., 2020, Comparative genomics and genomic epidemiology of mycobacterium avium subsp. paratuberculosis strains, chap. 6 of Paratuberculosis: Organism, disease, control.","ipdsId":"IP-109779","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":379691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379677,"type":{"id":15,"text":"Index Page"},"url":"https://www.cabi.org/bookshop/book/9781789243413/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevenson, Karen","contributorId":243646,"corporation":false,"usgs":false,"family":"Stevenson","given":"Karen","email":"","affiliations":[{"id":48767,"text":"Moredun Research Institute, Penicuik, Scotland","active":true,"usgs":false}],"preferred":false,"id":802840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":802841,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222425,"text":"70222425 - 2020 - Mississippi Alluvial Valley Forest-breeding landbird population & quantitative habitat objectives","interactions":[],"lastModifiedDate":"2021-09-10T11:37:31.771137","indexId":"70222425","displayToPublicDate":"2020-09-30T09:04:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Mississippi Alluvial Valley Forest-breeding landbird population & quantitative habitat objectives","docAbstract":"The Mississippi Alluvial Valley (MAV) is a 9 million ha (22-million-acre) floodplain that supports a diverse and ecologically rich bottomland hardwood forest ecosystem – one of the most productive in North America. It extends from roughly Cape Girardeau, Missouri, to the Gulf of Mexico and features a mosaic of ridges, swales, meander belts, and backswamps. Small changes in elevation (<1 foot) in the MAV are associated with large shifts in hydrology, which in turn, strongly affect plant and animal community composition and structure. The resultant diversity contributes to a fertile and productive floodplain. General forest types in the MAV include: Oak-gum-cypress (41%), elm-ash-cottonwood (29%), oakhickory (17%), and the remainder is other forest types (Oswalt 2013). Within the oak-gum-cypress and elm-ash-cottonwood categories, sugarberry-hackberry-elm-green ash and sweetgum-Nuttall oak-willow oak forest types account for close to one-half of MAV bottomland forest acreage, while baldcypress-tupelo forests are about 16 percent (Oswalt 2013). Although we emphasize bottomland hardwood habitat and associated bird species, this planning effort includes analyses based upon all forest types within the MAV. Hence, the term ‘forest’ refers to all forest types in the MAV.
","language":"English","publisher":"Lower Mississippi Valley Joint Venture","usgsCitation":"Demarest, D.W., Elliott, B., Ford, R., Hanni, D., McKnight, S.K., Mini, A.E., Twedt, D.J., and Wilson, R., 2020, Mississippi Alluvial Valley Forest-breeding landbird population & quantitative habitat objectives, 14 p.","productDescription":"14 p.","ipdsId":"IP-120883","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389000,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lmvjv.org/landbird-plans"}],"country":"United States","state":"Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"lower Mississippi Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.615478515625,\n 37.38761749978395\n ],\n [\n -92.2412109375,\n 34.813803317113155\n ],\n [\n -92.35107421874999,\n 34.642247047768535\n ],\n [\n -92.48291015625,\n 34.225429015241396\n ],\n [\n -91.571044921875,\n 33.578014746143985\n ],\n [\n -91.790771484375,\n 31.952162238024975\n ],\n [\n -91.95556640625,\n 31.109388560814963\n ],\n [\n -91.966552734375,\n 30.600093873550072\n ],\n [\n -90.72509765625,\n 29.7453016622136\n ],\n [\n -89.725341796875,\n 29.439597566602902\n ],\n [\n -89.45068359374999,\n 29.81205076752506\n ],\n [\n -90.4833984375,\n 30.192618218499273\n ],\n [\n -91.219482421875,\n 31.12819929911196\n ],\n [\n -90.626220703125,\n 32.37996146435729\n ],\n [\n -90.50537109375,\n 33.8521697014074\n ],\n [\n -89.84619140625,\n 35.146862906756304\n ],\n [\n -89.05517578125,\n 36.48314061639213\n ],\n [\n -88.9892578125,\n 37.204081555898526\n ],\n [\n -89.615478515625,\n 37.38761749978395\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Demarest, Dean W.","contributorId":175184,"corporation":false,"usgs":false,"family":"Demarest","given":"Dean","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":820004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Blaine","contributorId":261424,"corporation":false,"usgs":false,"family":"Elliott","given":"Blaine","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ford, Robert","contributorId":214858,"corporation":false,"usgs":false,"family":"Ford","given":"Robert","email":"","affiliations":[{"id":37063,"text":"U.S. Environmental Protection Agency, Cincinnati, OH","active":true,"usgs":false}],"preferred":false,"id":820006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanni, David","contributorId":261426,"corporation":false,"usgs":false,"family":"Hanni","given":"David","email":"","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":820007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKnight, S. Keith","contributorId":221729,"corporation":false,"usgs":false,"family":"McKnight","given":"S.","email":"","middleInitial":"Keith","affiliations":[{"id":40410,"text":"Lower Mississippi Valley Joint Venture","active":true,"usgs":false}],"preferred":false,"id":820008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mini, Anne E.","contributorId":261428,"corporation":false,"usgs":false,"family":"Mini","given":"Anne","email":"","middleInitial":"E.","affiliations":[{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":820009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":820010,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, R. Randy","contributorId":259210,"corporation":false,"usgs":false,"family":"Wilson","given":"R. Randy","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820011,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228928,"text":"70228928 - 2020 - Using video survey to examine the effect of habitat on gag grouper encounter","interactions":[],"lastModifiedDate":"2022-03-08T14:43:20.629834","indexId":"70228928","displayToPublicDate":"2020-09-30T08:32:37","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using video survey to examine the effect of habitat on gag grouper encounter","docAbstract":"Gag is a reef fish that was declared overfished in the Gulf of Mexico (GOM) in 2009. Although Gag are no longer listed as overfished, fisheries managers are concerned that stocks may not be recovering. Our objective was to identify habitat characteristics important to Gag, and their effect on the probability of Gag occurrence. We obtained data from three separate fisheries-independent video surveys that sampled in the eastern GOM from 2010-2017: the National Atmospheric and Oceanic Administration (NOAA) Panama City, FL Office, the NOAA Southeast Area Monitoring and Assessment Program, and the Florida Fish and Wildlife Research Institute. We ran a separate mixed effects logistic regression for each survey, and used Akaike’s Information Criteria to determine the best fitting models. Some variables - percent rock coverage, vertical relief, latitude, and depth - were present in all confidence models. Depth did not have the same relationship with Gag across all surveys, suggesting that shallower habitats (<50 m) might be more suitable for juveniles, whereas deeper habitats (>50 m) might be more suitable for adults. Managers may be able to help Gag and encourage their recovery by using these data to establish or expand protected areas throughout shallower waters.
","conferenceTitle":"Annual Meeting of the American Fisheries Society. Virtual","conferenceDate":"Aug 28 - Sep 3, 2020","language":"English","usgsCitation":"Alvarez, G., Gandy, D., Irwin, B., Jennings, C.A., and Fox, A., 2020, Using video survey to examine the effect of habitat on gag grouper encounter, Annual Meeting of the American Fisheries Society. Virtual, Aug 28 - Sep 3, 2020, 3 p.","productDescription":"3 p.","ipdsId":"IP-119340","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.26416015625,\n 24.8\n ],\n [\n -81.2548828125,\n 24.8\n ],\n [\n -81.2548828125,\n 30.637912028341123\n ],\n [\n -88.26416015625,\n 30.637912028341123\n ],\n [\n -88.26416015625,\n 24.8\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alvarez, G.","contributorId":280041,"corporation":false,"usgs":false,"family":"Alvarez","given":"G.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gandy, D.","contributorId":280042,"corporation":false,"usgs":false,"family":"Gandy","given":"D.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Adam","contributorId":288127,"corporation":false,"usgs":false,"family":"Fox","given":"Adam","affiliations":[],"preferred":false,"id":835935,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223111,"text":"70223111 - 2020 - Shallow basin structure and attenuation are key to predicting long shaking duration in Los Angeles Basin","interactions":[],"lastModifiedDate":"2021-08-11T13:04:12.661022","indexId":"70223111","displayToPublicDate":"2020-09-30T08:01:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Shallow basin structure and attenuation are key to predicting long shaking duration in Los Angeles Basin","docAbstract":"Ground motions in the Los Angeles Basin during large earthquakes are modulated by earthquake ruptures, path effects into the basin, basin effects, and local site response. We analyzed the direct effect of shallow basin structures on shaking duration at a period of 2–10 s in the Los Angeles region through modeling small magnitude, shallow, and deep earthquake pairs. The source depth modulates the basin response, particularly the shaking duration, and these features are a function of path effect and not site condition. Three-dimensional simulations using the CVM-S4.26.M01 velocity model show good fitting to the initial portion of the waveforms at periods of 5 s and longer but fail to predict the long shaking duration during shallow events, especially at periods less than 5 s. Simulations using CVM-H do not match the timing of the initial arrivals as well as CVM-S4.26.M01, and the strong late arrivals in the CVM-H simulation travel with an apparent velocity slower than observed. A higher-quality factor than traditionally assumed may produce synthetics with longer durations but is unable to accurately match the amplitude and phase. Beamforming analysis using dense array data further reveals the long duration surface waves have the same back azimuth as the direct arrivals and are generated at the basin edges, while the later coda waves are scattered from off-azimuth directions, potentially due to strong, sharp boundaries offshore. Improving the description of these shallow basin structures and attenuation model will enhance our capability to predict long-period ground motions in basins.
Land use in Panama has changed dramatically with ongoing deforestation and conversion to cropland and cattle pastures, potentially altering the soil properties that drive the hydrological processes of infiltration and overland flow. We compared plot-scale overland flow generation between hillslopes in forested and actively cattle-grazed watersheds in Central Panama. Soil physical and hydraulic properties, soil moisture and overland flow data were measured along hillslopes of each land-use type. Soil characteristics and rainfall data were input into a simple, 1-D representative model, HYDRUS-1D, to simulate overland flow that we used to make inferences about overland flow response at forest and pasture sites. Runoff ratios (overland flow/rainfall) were generally higher at the pasture site, although no overall trends were observed between rainfall characteristics and runoff ratios across the two land uses at the plot scale. Saturated hydraulic conductivity (Ks) and bulk density were different between the forest and pasture sites (p < 10−4). Simulating overland flow in HYDRUS-1D produced more outputs similar to the overland flow recorded at the pasture site than the forest site. Results from our study indicate that, at the plot scale, Hortonian overland flow is the main driver for overland flow generation at the pasture site during storms with high-rainfall totals. We infer that the combination of a leaf litter layer and the activation of shallow preferential flow paths resulting in shallow saturation-excess overland flow are likely the main drivers for plot scale overland flow generation at the forest site. Results from this study contribute to the broader understanding of the delivery of freshwater to streams, which will become increasingly important in the tropics considering freshwater resource scarcity and changing storm intensities.
The prediction of wave runup, as well as its components, time-averaged setup and the time-varying swash, is a key element of coastal storm hazard assessments, as wave runup controls the transitions between morphodynamic response types such as dune erosion and overwash, and the potential for flooding by wave overtopping. While theoretically able to simulate the dominant low-frequency swash, previous studies using the infragravity-wave–resolving model XBeach (XBSB) have shown an underestimation of the observed swash variance and wave runup, which was in part related to the absence of incident-band swash motions in the model. Here, we use an incident-band wave-resolving, non-hydrostatic version of the XBeach model (XBNH) to simulate wave runup observed during the SandyDuck '97 experiment on an intermediate–reflective sandy beach. The results show that the XBNH model describes wave runup and the individual setup and swash components well. We subsequently examine differences in wave runup prediction between the XBSB and XBNH models and find that the XBNH model is a better predictor of wave runup than XBSB for this beach, which is due to better predictions of both the incident-band and infragravity-band swash. For a range of beach states from reflective to dissipative it is shown that incident-band swash is underestimated by XBSB relative to XBNH, in particular for reflective conditions. Infragravity-band swash is shown to be lower in XBSB than XBNH for most conditions, including dissipative conditions for which the mean difference is 16% of the deep water wave height. The difference in infragravity-band swash in XBNH relative to XBSB is shown to mainly be the result of processes occurring outside the swash zone, but approximately 15% of the difference is caused by explicitly resolving incident-band wave motions within the swash zone, such as swash-swash interactions, which inherently cannot be simulated by wave-averaged models.
Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ−1). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ−1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.
The 2019 Ridgecrest, California earthquake sequence included Mw 6.4 and Mw 7.1 earthquakes that occurred on successive days beginning on 4 July 2019. These two largest earthquakes of the sequence occurred on orthogonal faults that ruptured the Earth’s surface. To better evaluate the 3D subsurface fault structure, (P- and S-wave) velocity, 3D and temporal variations in seismicity, and other important aspects of the earthquake sequence, we recorded aftershocks and ambient noise using up to 461 three-component nodal seismographs for about two months, beginning about one day after the Mw 7.1 mainshock. The ~ 30,000 Mw≥1 earthquakes that were recorded on the dense arrays provide an unusually large volume of data with which to evaluate the earthquake sequence. This report describes the recording arrays and is intended to provide metadata for researchers interested in evaluating various aspects of the 2019 Ridgecrest earthquake sequence using the nodal data set.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200203","usgsCitation":"Catchings, R.D., Goldman, M., Steidl, J.H., Chan, J., Allam, A., Criley, C., Ma, Z., Langermann, D., Huddleston, G., McEvilly, A., Mongovin, D., and Ben-Zion, Y., 2020, Nodal seismograph recordings of the 2019 Ridgecrest Earthquake Sequence: Seismological Research Letters, v. 91, no. 6, p. 3622-3633, https://doi.org/10.1785/0220200203.","productDescription":"12 p.","startPage":"3622","endPage":"3633","ipdsId":"IP-116990","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Staets","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.84463534796981,\n 35.703354079218656\n ],\n [\n -117.84463534796981,\n 35.534977065306975\n ],\n [\n -117.55394107005387,\n 35.534977065306975\n ],\n [\n -117.55394107005387,\n 35.703354079218656\n ],\n [\n -117.84463534796981,\n 35.703354079218656\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chan, Joanne 0000-0002-2065-2423","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":205864,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allam, Amir A. 0000-0002-6447-0779","orcid":"https://orcid.org/0000-0002-6447-0779","contributorId":350962,"corporation":false,"usgs":false,"family":"Allam","given":"Amir A.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":927518,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Criley, Coyn 0000-0002-0227-0165","orcid":"https://orcid.org/0000-0002-0227-0165","contributorId":223113,"corporation":false,"usgs":true,"family":"Criley","given":"Coyn","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ma, Zhenning","contributorId":350963,"corporation":false,"usgs":false,"family":"Ma","given":"Zhenning","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":927520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Langermann, Daniel S.","contributorId":351005,"corporation":false,"usgs":false,"family":"Langermann","given":"Daniel S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":927677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huddleston, Garet Jax 0000-0002-2837-3947","orcid":"https://orcid.org/0000-0002-2837-3947","contributorId":350964,"corporation":false,"usgs":true,"family":"Huddleston","given":"Garet Jax","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927522,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McEvilly, Andrian T.","contributorId":351006,"corporation":false,"usgs":false,"family":"McEvilly","given":"Andrian T.","affiliations":[],"preferred":false,"id":927678,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mongovin, Daniel David Thomas 0000-0002-1623-2637","orcid":"https://orcid.org/0000-0002-1623-2637","contributorId":350965,"corporation":false,"usgs":true,"family":"Mongovin","given":"Daniel David Thomas","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927523,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ben-Zion, Yehuda 0000-0002-9602-2014","orcid":"https://orcid.org/0000-0002-9602-2014","contributorId":350966,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":927525,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70214515,"text":"sir20205083 - 2020 - The Everglades Depth Estimation Network (EDEN) surface-water interpolation model, version 3","interactions":[],"lastModifiedDate":"2020-09-30T12:35:17.865835","indexId":"sir20205083","displayToPublicDate":"2020-09-29T12:47:07","publicationYear":"2020","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":"2020-5083","displayTitle":"The Everglades Depth Estimation Network (EDEN) Surface-Water Interpolation Model, Version 3","title":"The Everglades Depth Estimation Network (EDEN) surface-water interpolation model, version 3","docAbstract":"The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that estimate daily water-level data at ungaged locations, and applications that generate derived hydrologic data across the freshwater part of the Greater Everglades landscape. Version 3 (V3) of the EDEN interpolation surface-water model is the most recent update, replacing the version 2 (V2) model released in 2011.
The primary revision for the V3 model is the switch to the R programming language to create a more efficient and portable EDEN code relative to V2, without reliance on proprietary software. Using R, the interpolation script runs over 10 times faster and is more easily updated, for example, to accommodate changes in the gage network or to incorporate R software updates. Additional revisions made for the V3 model include updates to the interpolation model, the gage network, and groundwater-level estimations. The EDEN model domain in the Greater Everglades and Big Cypress National Preserve is divided into subdomains that are based on hydrologic boundaries. In the V3 model, the number of subdomains was increased from five to eight, which allows hydrologic boundaries, such as levees and canals, to be better represented in the interpolation scheme. Five pseudogages were added to constrain the water-level surface at subdomain boundaries. Changes made to the water-level gage network between the implementation of the V2 and V3 models are incorporated, and groundwater-level estimations are added, which are important information for hydrologic and ecological studies.
Summary model performance statistics indicate similar accuracy in water-level surfaces generated by the V3 and V2 models, with a root mean square error of 4.78 centimeters for both interpolation models against independent water-level measurements. Providing stability and continuity for the EDEN user community, the V3 model closely replicates the V2 model, with a root mean square difference of 3.87 centimeters for interpolated surfaces from April 1, 2014, to March 31, 2018. The additional groundwater levels provide a realistic estimate of the saturated groundwater surface continuous with the surface-water surface for Water Conservation Areas 2A and 2B from 2000 to 2011. This continuous surface is a more accurate estimation of the spatial distribution of water in the hydrologic system than before, providing needed information for ecological studies in areas where depth to water table affects habitats. Development of the EDEN V3 model advances the tools available to scientists and resource managers for guiding large-scale field operations, describing hydrologic changes, and supporting biological and ecological assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205083","collaboration":"USGS Greater Everglades Priority Ecosystems Science ProgramDirector, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
We measured food availability and diet composition of juvenile salmonids over multiple years and seasons before and during the world’s largest dam removal on the Elwha River, Washington State. We conducted these measurements over three sediment-impacted sections (the estuary and two sections of the river downstream of each dam) and compared these to data collected from mainstem tributaries not directly affected by the massive amount of sediment released from the reservoirs. We found that sediment impacts from dam removal significantly reduced invertebrate prey availability, but juvenile salmon adjusted their foraging so that the amount of energy in diets was similar before and during dam removal. This general pattern was seen in both river and estuary habitats, although the mechanisms driving the change and the response differed between habitats. In the estuary, the dietary shifts were related to changes in invertebrate assemblages following a hydrological transition from brackish to freshwater caused by sediment deposition at the river’s mouth. The loss of brackish invertebrate species caused fish to increase piscivory and rely on new prey sources such as plankton. In the river, energy provided to fish by Ephemeroptera, Plecoptera, and Trichoptera taxa before dam removal was replaced first by terrestrial invertebrates, and then by sediment-tolerant taxa such as Chironomidae. The results of our study are consistent with many others that have shown sharp declines in invertebrate density during dam removal. Our study further shows how those changes can move through the food web and affect fish diet composition, selectivity, and energy availability. As we move further along the dam removal response trajectory, we hypothesize that food web complexity will continue to increase as annual sediment load now approaches natural background levels, anadromous fish have recolonized the majority of the watershed between and above the former dams, and revegetation and microhabitats continue to develop in the estuary.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0239198","usgsCitation":"Morley, S.A., Foley, M.M., Duda, J.J., Beirne, M.M., Paradis, R.L., Johnson, R.C., McHenry, M.L., Elofson, M., Sampson, E.M., McCoy, R.E., Stapleton, J., and Pess, G.R., 2020, Shifting food web structure during dam removal—Disturbance and recovery during a major restoration action: PLoS ONE, v. 15, no. 9, e0239198, 34 p., https://doi.org/10.1371/journal.pone.0239198.","productDescription":"e0239198, 34 p.","ipdsId":"IP-117389","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":455196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0239198","text":"Publisher Index Page"},{"id":378966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River, Olympic Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64974975585938,\n 47.81960975604292\n ],\n [\n -123.38882446289061,\n 47.81960975604292\n ],\n [\n -123.38882446289061,\n 48.16333749877855\n ],\n [\n -123.64974975585938,\n 48.16333749877855\n ],\n [\n -123.64974975585938,\n 47.81960975604292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Morley, Sarah A.","contributorId":148956,"corporation":false,"usgs":false,"family":"Morley","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":17601,"text":"NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":800243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Melissa M 0000-0002-5832-6404","orcid":"https://orcid.org/0000-0002-5832-6404","contributorId":238117,"corporation":false,"usgs":false,"family":"Foley","given":"Melissa","email":"","middleInitial":"M","affiliations":[{"id":47699,"text":"San Francisco Estuary Institute, Richmond, CA","active":true,"usgs":false}],"preferred":false,"id":800244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":800245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beirne, Mathew M","contributorId":241958,"corporation":false,"usgs":false,"family":"Beirne","given":"Mathew","email":"","middleInitial":"M","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paradis, Rebecca L","contributorId":241960,"corporation":false,"usgs":false,"family":"Paradis","given":"Rebecca","email":"","middleInitial":"L","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":800248,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McHenry, Michael L.","contributorId":39672,"corporation":false,"usgs":false,"family":"McHenry","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":800249,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Elofson, Mel","contributorId":241966,"corporation":false,"usgs":false,"family":"Elofson","given":"Mel","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800250,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sampson, Earnest M","contributorId":241968,"corporation":false,"usgs":false,"family":"Sampson","given":"Earnest","email":"","middleInitial":"M","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800251,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McCoy, Randall E","contributorId":241971,"corporation":false,"usgs":false,"family":"McCoy","given":"Randall","email":"","middleInitial":"E","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800252,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stapleton, Justin","contributorId":241974,"corporation":false,"usgs":false,"family":"Stapleton","given":"Justin","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800253,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pess, George R.","contributorId":13501,"corporation":false,"usgs":false,"family":"Pess","given":"George","email":"","middleInitial":"R.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":800254,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70214968,"text":"70214968 - 2020 - The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA","interactions":[],"lastModifiedDate":"2020-10-03T15:26:50.097038","indexId":"70214968","displayToPublicDate":"2020-09-29T10:24:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA","docAbstract":"The effects of climate on plant species ranges are well appreciated, but the effects of other processes, such as fire, on plant species distribution are less well understood. We used a dataset of 561 plots 0.1 ha in size located throughout Yosemite National Park, in the Sierra Nevada of California, USA, to determine the joint effects of fire and climate on woody plant species. We analyzed the effect of climate (annual actual evapotranspiration [AET], climatic water deficit [Deficit]) and fire characteristics (occurrence [BURN] for all plots, fire return interval departure [FRID] for unburned plots, and severity of the most severe fire [dNBR]) on the distribution of woody plant species.
Of 43 species that were present on at least two plots, 38 species occurred on five or more plots. Of those 38 species, models for the distribution of 13 species (34%) were significantly improved by including the variable for fire occurrence (BURN). Models for the distribution of 10 species (26%) were significantly improved by including FRID, and two species (5%) were improved by including dNBR. Species for which distribution models were improved by inclusion of fire variables included some of the most areally extensive woody plants. Species and ecological zones were aligned along an AET-Deficit gradient from cool and moist to hot and dry conditions.
In fire-frequent ecosystems, such as those in most of western North America, species distribution models were improved by including variables related to fire. Models for changing species distributions would also be improved by considering potential changes to the fire regime.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-020-00079-9","usgsCitation":"van Wagtendonk, J., Moore, P., Yee, J.L., and Lutz, J.A., 2020, The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA: Fire Ecology, v. 16, 22, 23 p., https://doi.org/10.1186/s42408-020-00079-9.","productDescription":"22, 23 p.","ipdsId":"IP-117438","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00079-9","text":"Publisher Index Page"},{"id":379026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.13549804687501,\n 36.94989178681327\n ],\n [\n -118.24584960937499,\n 36.94989178681327\n ],\n [\n -118.24584960937499,\n 38.272688535980976\n ],\n [\n -120.13549804687501,\n 38.272688535980976\n ],\n [\n -120.13549804687501,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"van Wagtendonk, Jan W.","contributorId":189573,"corporation":false,"usgs":false,"family":"van Wagtendonk","given":"Jan W.","affiliations":[],"preferred":false,"id":800466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Peggy E","contributorId":242603,"corporation":false,"usgs":false,"family":"Moore","given":"Peggy E","affiliations":[{"id":48478,"text":"retired USGS WERC employee","active":true,"usgs":false}],"preferred":false,"id":800467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":800469,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215697,"text":"70215697 - 2020 - Differences in rhizosphere microbial communities between native and non‐native Phragmites australis may depend on stand density","interactions":[],"lastModifiedDate":"2020-10-29T15:20:49.581163","indexId":"70215697","displayToPublicDate":"2020-09-29T08:35:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Differences in rhizosphere microbial communities between native and non‐native Phragmites australis may depend on stand density","title":"Differences in rhizosphere microbial communities between native and non‐native Phragmites australis may depend on stand density","docAbstract":"Microorganisms surrounding plant roots may benefit invasive species through enhanced mutualism or decreased antagonism, when compared to surrounding native species. We surveyed the rhizosphere soil microbiome of a prominent invasive plant, Phragmites australis, and its co‐occurring native subspecies for evidence of microbial drivers of invasiveness. If the rhizosphere microbial community is important in driving plant invasions, we hypothesized that non‐native Phragmites would cultivate a different microbiome from native Phragmites, containing fewer pathogens, more mutualists, or both. We surveyed populations of native and non‐native Phragmites across Michigan and Ohio USA, and we described rhizosphere microbial communities using culture‐independent next‐generation sequencing. We found little evidence that native and non‐native Phragmites cultivate distinct bacterial, fungal, or oomycete rhizosphere communities. Microbial community differences in our Michigan survey were not associated with plant lineage but were mainly driven by environmental factors, such as soil saturation and nutrient concentrations. Intensive sampling along transects consisting of dense monocultures of each lineage and mixed zones revealed bacterial community differences between lineages in dense monoculture, but not in mixture. We found no evidence of functional differences in the microbial communities surrounding each lineage. We extrapolate that the invasiveness of non‐native Phragmites, when compared to its native congener, does not result from the differential cultivation of beneficial or antagonistic rhizosphere microorganisms.