A fundamental goal of population ecologists is to identify drivers responsible for temporal variation in abundance. Understanding whether variation is associated with environmental stochasticity or anthropogenic disturbances, which are more amenable to management action, is crucial yet difficult to achieve. Here, we present a hierarchical monitoring framework that models rates of change in abundance from spatially structured populations and identifies when local declines fall out of synchrony with trends at larger spatial scales. Importantly, the framework provides signals that alert managers to the categorical significance of observed declines while avoiding signals where declines result from drivers operating at larger spatial scales (e.g., periodic reductions in primary productivity owing to drought). We demonstrate utility through application to a rapidly declining sagebrush (Artemisia spp.) indicator species (greater sage-grouse; Centrocercus urophasianus) using 30 years (1990–2019) of count data collected from greater than 4,400 leks (habitual breeding sites) distributed across the western United States. Results revealed population declines, immediately preceding triggers (2–4-year period), ranging between 58 and 68%. Conversely, population trends unassociated with triggers showed little-to-no sign of decline. Retrospective application of the monitoring framework indicated an average annual rate of 1.7% of leks or 1.3% of neighborhood clusters (lek aggregations) would have required management intervention to reverse range-wide declines and stabilize the U.S. population as a whole.
There is inconsistent evidence that stream restoration projects lead to recovery of ecosystem attributes, especially stream biota. While some assessments have documented desired changes in fish community metrics in the first years following restoration, longer-term studies have not always corroborated these findings. In this study, we used data and monitoring reports submitted to federal regulators by stream mitigation consultants to examine whether in-stream restoration activities led to changes in fish community attributes at 23 compensatory mitigation projects representing 53 sampling sites in Georgia, United States over 7 years of post-restoration monitoring. Modeling results indicated that abundance and species richness of fishes generally increased in the first years after restoration before decreasing to baseline levels by the seventh year. This pattern was consistent for models considering sensitive fish taxa, as well as at sites across a range of agricultural and forested land cover percentages. However, the effect of restoration on species richness was dampened in larger streams and at more urbanized locations. A community trajectory analysis supported the findings that fish community change was transitory at most sites. Remote estimation of canopy cover change at restoration sites suggested that the hump-shaped response may be driven by increased light availability during the immediate-post restoration period, followed by subsequent re-shading of stream channels by riparian plantings. Our analysis indicates that reach-level manipulation of streams should not be expected to induce long-term changes in fish communities, and that publicly available monitoring reports may be leveraged to address questions of stream restoration efficacy.
Coastal habitats can play an important role in climate change mitigation. As Louisiana implements its climate action plan and the restoration and risk-reduction projects outlined in its 2017 Louisiana Coastal Master Plan, it is critical to consider potential greenhouse gas (GHG) fluxes in coastal habitats. This study estimated the potential climate mitigation role of existing, converted, and restored coastal habitats for years 2005, 2020, 2025, 2030, and 2050, which align with the Governor of Louisiana's GHG reduction targets. An analytical framework was developed that considered (1) available scientific data on net ecosystem carbon balance fluxes per habitat and (2) habitat areas projected from modeling efforts used for the 2017 Louisiana Coastal Master Plan to estimate the net GHG flux of coastal area. The coastal area was estimated as net GHG sinks of −38.4 ± 10.6 and −43.2 ± 12.0 Tg CO2 equivalents (CO2e) in 2005 and 2020, respectively. The coastal area was projected to remain a net GHG sink in 2025 and 2030, both with and without the implementation of Coastal Master Plan projects (means ranged from −25.3 to −34.2 Tg CO2e). By 2050, with model-projected wetland loss and conversion of coastal habitats to open water due to coastal erosion and relative sea level rise, Louisiana's coastal area was projected to become a net source of GHG emissions both with and without the Coastal Master Plan projects. However, in the year 2050, the Louisiana Coastal Master Plan project implementation was projected to avoid the release of +8.8 ± 1.3 Tg CO2e compared with an alternative with no action. Reduction in current and future stressors to coastal habitats, including impacts from sea level rise, as well as the implementation of restoration projects could help to ensure coastal areas remain a natural climate solution.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2847","usgsCitation":"Baustian, M.M., Liu, B., Moss, L.C., Dausman, A., and Pahl, J.W., 2023, Climate change mitigation potential of Louisiana's coastal area: Current estimates and future projections: Ecological Applications, v. 23, no. 4, e2847, 22 p.; Data Release, https://doi.org/10.1002/eap.2847.","productDescription":"e2847, 22 p.; Data Release","ipdsId":"IP-147080","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444174,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2847","text":"Publisher Index Page"},{"id":415413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419359,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Z2MZV","text":"A subset of 2017 Louisiana Coastal Master Plan model output to estimate climate change mitigation potential of Louisiana’s coastal area"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.59801925944635,\n 30.689846184930914\n ],\n [\n -93.59801925944635,\n 28.629249419941743\n ],\n [\n -89.0431895005861,\n 28.629249419941743\n ],\n [\n -89.0431895005861,\n 30.689846184930914\n ],\n [\n -93.59801925944635,\n 30.689846184930914\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moss, Leland C.","contributorId":272644,"corporation":false,"usgs":false,"family":"Moss","given":"Leland","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868939,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pahl, James W.","contributorId":304017,"corporation":false,"usgs":false,"family":"Pahl","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":868940,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243534,"text":"70243534 - 2023 - Uses of epistemic uncertainties in the USGS National Seismic Hazard Models","interactions":[],"lastModifiedDate":"2023-05-16T18:21:23.693721","indexId":"70243534","displayToPublicDate":"2023-03-18T06:34:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Uses of epistemic uncertainties in the USGS National Seismic Hazard Models","docAbstract":"Coseismic temperature rise activates fault dynamic weakening that promotes earthquake rupture propagation. The spatial scales over which peak temperatures vary on slip surfaces are challenging to identify in the rock record. We present microstructural observations and electron backscatter diffraction data from three small-displacement hematite-coated fault mirrors (FMs) in the Wasatch fault damage zone, Utah, to evaluate relations between fault properties, strain localization, temperature rise, and weakening mechanisms during FM development. Millimeter- to cm-thick, matrix-supported, hematite-cemented breccia is cut by ∼25–200 μm-thick, texturally heterogeneous veins that form the hematite FM volume (FMV). Grain morphologies and textures vary with FMV thickness over μm to mm lengthscales. Cataclasite grades to ultracataclasite where FMV thickness is greatest. Thinner FMVs and geometric asperities are characterized by particles with subgrains, serrated grain boundaries, and(or) low-strain polygonal grains that increase in size with proximity to the FM surface. Comparison to prior hematite deformation experiments suggests FM temperatures broadly range from ≥400°C to ≥800–1100°C, compatible with observed coeval brittle and plastic deformation mechanisms, over sub-mm scales on individual slip surfaces during seismic slip. We present a model of FM development by episodic hematite precipitation, fault reactivation, and strain localization, where the thickness of hematite veins controls the width of the deforming zones during subsequent fault slip, facilitating temperature rise and thermally activated weakening. Our data document intrasample coseismic temperatures, resultant deformation and dynamic weakening mechanisms, and the length scales over which these vary on slip surfaces.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB025069","usgsCitation":"McDermott, R.G., Ault, A.K., Wetzel, K.F., Evans, J.P., and Shen, F., 2023, Microscale spatial variations in coseismic temperature rise on hematite fault mirrors in the Wasatch fault damage zone: JGR Solid Earth, v. 128, no. 3, e2022JB025069, 20 p., https://doi.org/10.1029/2022JB025069.","productDescription":"e2022JB025069, 20 p.","ipdsId":"IP-142375","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch fault damage zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.0333,\n 41.425\n ],\n [\n -112.0333,\n 41.366667\n ],\n [\n -111.966667,\n 41.366667\n ],\n [\n -111.966667,\n 41.425\n ],\n [\n -112.0333,\n 41.425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McDermott, Robert Gregory 0000-0002-2550-0322","orcid":"https://orcid.org/0000-0002-2550-0322","contributorId":360810,"corporation":false,"usgs":true,"family":"McDermott","given":"Robert","middleInitial":"Gregory","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ault, Alexis K.","contributorId":365163,"corporation":false,"usgs":false,"family":"Ault","given":"Alexis","middleInitial":"K.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":953857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetzel, Kelsey F.","contributorId":365164,"corporation":false,"usgs":false,"family":"Wetzel","given":"Kelsey","middleInitial":"F.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":953858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, James P.","contributorId":365165,"corporation":false,"usgs":false,"family":"Evans","given":"James","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":953859,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Fen-Ann","contributorId":365166,"corporation":false,"usgs":false,"family":"Shen","given":"Fen-Ann","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":953860,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248415,"text":"70248415 - 2023 - Increasing hypoxia on global coral reefs under ocean warming","interactions":[],"lastModifiedDate":"2023-09-12T14:09:20.916709","indexId":"70248415","displayToPublicDate":"2023-03-16T09:02:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Increasing hypoxia on global coral reefs under ocean warming","docAbstract":"Ocean deoxygenation is predicted to threaten marine ecosystems globally. However, current and future oxygen concentrations and the occurrence of hypoxic events on coral reefs remain underexplored. Here, using autonomous sensor data to explore oxygen variability and hypoxia exposure at 32 representative reef sites, we reveal that hypoxia is already pervasive on many reefs. Eighty-four percent of reefs experienced weak to moderate (≤153 µmol O2 kg−1 to ≤92 µmol O2 kg−1) hypoxia and 13% experienced severe (≤61 µmol O2 kg−1) hypoxia. Under different climate change scenarios based on four Shared Socioeconomic Pathways (SSPs), we show that projected ocean warming and deoxygenation will increase the duration, intensity and severity of hypoxia, with more than 94% and 31% of reefs experiencing weak to moderate and severe hypoxia, respectively, by 2100 under SSP5-8.5. This projected oxygen loss could have negative consequences for coral reef taxa due to the key role of oxygen in organism functioning and fitness.
","language":"English","publisher":"Nature","doi":"10.1038/s41558-023-01619-2","usgsCitation":"Pezner, A.K., Courtney, T.A., Barkley, H., Chou, W., Chu, H., Clements, S.M., Cyronak, T., DeGrandpre, M.D., Kekuewa, S.A., Kline, D.I., Liang, Y., Martz, T.R., Mitarai, S., Page, H.N., Rintoul, M.S., Smith, J.E., Soong, K., Takeshita, Y., Tresguerres, M., Wei, Y., Yates, K.K., and Andersson, A.J., 2023, Increasing hypoxia on global coral reefs under ocean warming: Nature Climate Change, v. 13, p. 403-409, https://doi.org/10.1038/s41558-023-01619-2.","productDescription":"7 p.","startPage":"403","endPage":"409","ipdsId":"IP-138099","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467117,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://oist.repo.nii.ac.jp/record/2000571/files/Pezner_MS_edits_clean_coauthors.pdf","text":"External 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Hannah","contributorId":329648,"corporation":false,"usgs":false,"family":"Barkley","given":"Hannah","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":882826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chou, Wen-Chen 0000-0002-5107-7978","orcid":"https://orcid.org/0000-0002-5107-7978","contributorId":329650,"corporation":false,"usgs":false,"family":"Chou","given":"Wen-Chen","email":"","affiliations":[{"id":78679,"text":"National Taiwan Ocean University","active":true,"usgs":false}],"preferred":false,"id":882827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chu, Hui-Chuan","contributorId":329651,"corporation":false,"usgs":false,"family":"Chu","given":"Hui-Chuan","email":"","affiliations":[{"id":78679,"text":"National Taiwan Ocean University","active":true,"usgs":false}],"preferred":false,"id":882828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clements, Samanth M. 0000-0003-3094-2173","orcid":"https://orcid.org/0000-0003-3094-2173","contributorId":329652,"corporation":false,"usgs":false,"family":"Clements","given":"Samanth","email":"","middleInitial":"M.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cyronak, Tyler 0000-0003-3556-7616","orcid":"https://orcid.org/0000-0003-3556-7616","contributorId":329653,"corporation":false,"usgs":false,"family":"Cyronak","given":"Tyler","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":882830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeGrandpre, Michael D.","contributorId":187412,"corporation":false,"usgs":false,"family":"DeGrandpre","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":882831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kekuewa, Samuel A.H. 0000-0003-0395-2261","orcid":"https://orcid.org/0000-0003-0395-2261","contributorId":329654,"corporation":false,"usgs":false,"family":"Kekuewa","given":"Samuel","email":"","middleInitial":"A.H.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882832,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kline, David I","contributorId":141143,"corporation":false,"usgs":false,"family":"Kline","given":"David","email":"","middleInitial":"I","affiliations":[{"id":12888,"text":"Scripps Institution of Oceanography, Univ of California","active":true,"usgs":false}],"preferred":false,"id":882833,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Liang, Yi-Bei 0000-0002-8622-8596","orcid":"https://orcid.org/0000-0002-8622-8596","contributorId":329656,"corporation":false,"usgs":false,"family":"Liang","given":"Yi-Bei","email":"","affiliations":[{"id":78681,"text":"National Sun Yat-sen University Taiwan","active":true,"usgs":false}],"preferred":false,"id":882834,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Martz, Todd R.","contributorId":199920,"corporation":false,"usgs":false,"family":"Martz","given":"Todd","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":882835,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mitarai, Satoshi","contributorId":218228,"corporation":false,"usgs":false,"family":"Mitarai","given":"Satoshi","email":"","affiliations":[],"preferred":false,"id":882836,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Page, Heather N. 0000-0001-9997-0004","orcid":"https://orcid.org/0000-0001-9997-0004","contributorId":329657,"corporation":false,"usgs":false,"family":"Page","given":"Heather","email":"","middleInitial":"N.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882837,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rintoul, Max S. 0000-0001-9141-9779","orcid":"https://orcid.org/0000-0001-9141-9779","contributorId":329658,"corporation":false,"usgs":false,"family":"Rintoul","given":"Max","email":"","middleInitial":"S.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882838,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, Jennifer E. 0000-0002-4516-6931","orcid":"https://orcid.org/0000-0002-4516-6931","contributorId":329659,"corporation":false,"usgs":false,"family":"Smith","given":"Jennifer","email":"","middleInitial":"E.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882839,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Soong, Keryea 0000-0002-4371-7103","orcid":"https://orcid.org/0000-0002-4371-7103","contributorId":329660,"corporation":false,"usgs":false,"family":"Soong","given":"Keryea","email":"","affiliations":[{"id":78681,"text":"National Sun Yat-sen University Taiwan","active":true,"usgs":false}],"preferred":false,"id":882840,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Takeshita, Yuichiro","contributorId":329673,"corporation":false,"usgs":false,"family":"Takeshita","given":"Yuichiro","email":"","affiliations":[],"preferred":false,"id":882841,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tresguerres, Martin 0000-0002-7090-9266","orcid":"https://orcid.org/0000-0002-7090-9266","contributorId":329662,"corporation":false,"usgs":false,"family":"Tresguerres","given":"Martin","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":882842,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Wei, Yi","contributorId":329663,"corporation":false,"usgs":false,"family":"Wei","given":"Yi","email":"","affiliations":[{"id":30216,"text":"National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":882843,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Yates, Kimberly K. 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":214349,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882844,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Andersson, Andreas J","contributorId":141142,"corporation":false,"usgs":false,"family":"Andersson","given":"Andreas","email":"","middleInitial":"J","affiliations":[{"id":12888,"text":"Scripps Institution of Oceanography, Univ of California","active":true,"usgs":false}],"preferred":false,"id":882845,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70251433,"text":"70251433 - 2023 - A 600-kyr reconstruction of deep Arctic seawater δ18O from benthic foraminiferal oxygen isotopes and ostracode Mg/Ca paleothermometry","interactions":[],"lastModifiedDate":"2024-02-10T13:50:03.510105","indexId":"70251433","displayToPublicDate":"2023-03-16T07:43:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"A 600-kyr reconstruction of deep Arctic seawater δ18O from benthic foraminiferal oxygen isotopes and ostracode Mg/Ca paleothermometry","docAbstract":"The oxygen isotopic composition of benthic foraminiferal tests (δ18Ob) is one of the pre-eminent tools for correlating marine sediments and interpreting past terrestrial ice volume and deep-ocean temperatures. Despite the prevalence of δ18Ob applications to marine sediment cores over the Quaternary, its use is limited in the Arctic Ocean because of low benthic foraminiferal abundances, challenges with constructing independent sediment core age models, and an apparent muted amplitude of Arctic δ18Ob variability compared to open-ocean records. Here we evaluate the controls on Arctic δ18Ob by using ostracode
On 20 December 2020, after more than 2 years of quiescence at Kīlauea Volcano, Hawaiʻi, renewed volcanic activity in the summit crater caused boiling of the water lake over a period of ∼90 min. The resulting water-rich, electrified plume rose to 11–13 km above sea level, which is among the highest plumes on record for Kīlauea. Although conventional models would infer a high mass flux from explosive magma-water interaction, the plume was not associated with an infrasound signal indicative of “explosive” activity, nor did it produce a measurable ash-fall deposit. We use multisensor data to characterize lava-water interaction and plume generation during this opening phase of the 2020–21 eruption. Satellite, weather radar, and eyewitness observations revealed that the plume was rich in water vapor and hydrometeors but transported less ash than expected from its maximum height. Volcanic lightning flashes detected by ground-based cameras were confined to freezing altitudes of the upper cloud, suggesting that the ice formation drove the electrification of this plume. The low acoustic energy from lava-water interaction points to a weakly explosive style of hydrovolcanism. Heat transfer calculations show that the lava to water heat flux was sufficient to boil the lake within 90 min. Limited mixing of lava and water inhibited major steam explosions and fine fragmentation. Results from one-dimensional plume modeling suggest that the models may underpredict plume height due to overestimation of crosswind air-entrainment. Our findings shed light on an unusual style of volcanism in which weakly explosive lava-water interaction generated an outsized plume.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010718","usgsCitation":"Cahalan, R.C., Mastin, L.G., Van Eaton, A.R., Hurwitz, S., Smith, A., Dufek, J., Solovitz, S.A., Patrick, M.R., Schmith, J., Parcheta, C., Thelen, W., and Downs, D.T., 2023, Dynamics of the December 2020 ash-poor plume formed by lava-water interaction at the summit of Kilauea Volcano, Hawaii: Geochemistry, Geophysics, Geosystems, v. 24, no. 3, e2022GC010718, 23 p., https://doi.org/10.1029/2022GC010718.","productDescription":"e2022GC010718, 23 p.","ipdsId":"IP-145500","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010718","text":"Publisher Index Page"},{"id":481272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2803669612127,\n 19.458319847666203\n ],\n [\n -155.2803669612127,\n 19.37101672587596\n ],\n [\n -155.17107998542878,\n 19.37101672587596\n ],\n [\n -155.17107998542878,\n 19.458319847666203\n ],\n [\n -155.2803669612127,\n 19.458319847666203\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Cahalan, Ryan Cain 0000-0002-3322-0654","orcid":"https://orcid.org/0000-0002-3322-0654","contributorId":302355,"corporation":false,"usgs":true,"family":"Cahalan","given":"Ryan","email":"","middleInitial":"Cain","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":925179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":925182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Adam B.","contributorId":328715,"corporation":false,"usgs":false,"family":"Smith","given":"Adam B.","affiliations":[{"id":38790,"text":"Missouri Botanical Garden","active":true,"usgs":false}],"preferred":false,"id":925183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dufek, Josef","contributorId":194001,"corporation":false,"usgs":false,"family":"Dufek","given":"Josef","email":"","affiliations":[],"preferred":false,"id":925184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solovitz, Stephen A. 0000-0001-7019-2958","orcid":"https://orcid.org/0000-0001-7019-2958","contributorId":257659,"corporation":false,"usgs":false,"family":"Solovitz","given":"Stephen","email":"","middleInitial":"A.","affiliations":[{"id":52077,"text":"Washington State University, Vancouver","active":true,"usgs":false}],"preferred":false,"id":925185,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmith, Jo 0000-0002-0912-7441","orcid":"https://orcid.org/0000-0002-0912-7441","contributorId":304399,"corporation":false,"usgs":true,"family":"Schmith","given":"Jo","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Parcheta, Carolyn 0000-0001-6556-4630 cparcheta@usgs.gov","orcid":"https://orcid.org/0000-0001-6556-4630","contributorId":215617,"corporation":false,"usgs":true,"family":"Parcheta","given":"Carolyn","email":"cparcheta@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925188,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925189,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925190,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70250816,"text":"70250816 - 2023 - Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands","interactions":[],"lastModifiedDate":"2024-01-08T17:12:25.046632","indexId":"70250816","displayToPublicDate":"2023-03-15T11:07:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands","docAbstract":"Forest removal for livestock grazing is a striking example of human-caused state change leading to a stable, undesirable invasive grass system that is resistant to restoration efforts. Understanding which factors lead to resilience to the alternative grass state can greatly benefit managers when planning forest restoration. We address how thresholds of grass cover and seed rain might influence forest recovery in a restoration project on Hawaiʻi Island, USA. Since the 1980s, over 400,000 Acacia koa (koa) trees have been planted across degraded pasture, and invasive grasses still dominate the understory with no native woody-plant recruitment. Between this koa/grass matrix are remnant native Metrosideros polymorpha (ʻōhiʻa) trees beneath which native woody plants naturally recruit. We tested whether there were threshold levels of native woody understory that accelerate recruitment under both tree species by monitoring seed rain at 40 trees (20 koa and ʻōhiʻa) with a range of native woody understory basal area (BA). We found a positive relationship between total seed rain (but not bird-dispersed seed rain) and native woody BA and a negative relationship between native woody BA and grass cover, with no indication of threshold dynamics. We also experimentally combined grass removal levels with seed rain density (six levels) of two common understory species in plots under koa (n = 9) and remnant ʻōhiʻa (n = 9). Few seedlings emerged when no grass was removed despite adding seeds at densities two to 75 times higher than naturally occurring. However, seedling recruitment increased two to three times once at least 50% of grass was removed. Existing survey data of naturally occurring seedlings also supported a threshold of grass cover below which seedlings were able to establish. Thus, removal of all grasses is not necessary to achieve system responses: Even moderate reductions (~50%) can increase rates of native woody recruitment. The nonlinear thresholds found here highlight how incremental changes to an inhibitory factor lead to limited restoration success until a threshold is crossed. The resources needed to fully eradicate an invasive species may be unwarranted for state change, making understanding where thresholds lie of the utmost importance to prioritize resources.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2841","usgsCitation":"Rehm, E.M., D'Antonio, C., and Yelenik, S.G., 2023, Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands: Ecological Applications, v. 33, no. 4, e2841, 16 p., https://doi.org/10.1002/eap.2841.","productDescription":"e2841, 16 p.","ipdsId":"IP-146381","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":444204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2841","text":"Publisher Index Page"},{"id":435410,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OUTPW","text":"USGS data release","linkHelpText":"Hakalau Forest NWR seedling and substrate data, 2015"},{"id":424193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.33984563763397,\n 19.888564964992852\n ],\n [\n -155.33984563763397,\n 19.7594436528868\n ],\n [\n -155.2208910771554,\n 19.7594436528868\n ],\n [\n -155.20855397426797,\n 19.931097090422853\n ],\n [\n -155.33984563763397,\n 19.888564964992852\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":891660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D'Antonio, Carla M.","contributorId":27992,"corporation":false,"usgs":false,"family":"D'Antonio","given":"Carla M.","affiliations":[],"preferred":false,"id":891661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":891662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252452,"text":"70252452 - 2023 - Ecological harm and economic damages of chemical contamination to linked aquatic-terrestrial food webs: A study-design tool for practitioners","interactions":[],"lastModifiedDate":"2024-09-18T16:03:02.473007","indexId":"70252452","displayToPublicDate":"2023-03-15T09:51:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Ecological harm and economic damages of chemical contamination to linked aquatic-terrestrial food webs: A study-design tool for practitioners","docAbstract":"Contamination of aquatic ecosystems can have cascading effects on terrestrial consumers by altering the availability and quality of aquatic insect prey. Comprehensive assessment of these indirect food-web effects of contaminants on natural resources and their associated services necessitates using both ecological and economic tools. In the present study we present an aquatic-terrestrial assessment tool (AT2), including ecological and economic decision trees, to aid practitioners and researchers in designing contaminant effect studies for linked aquatic-terrestrial insect-based food webs. The tool is tailored to address the development of legal claims by the US Department of the Interior's Natural Resource Damage Assessment and Restoration Program, which aims to restore natural resources injured by oil spills and hazardous substance releases into the environment. Such cases require establishing, through scientific inquiry, the existence of natural resource injury as well as the determination of the monetary or in-kind project-based damages required to restore this injury. However, this tool is also useful to researchers interested in questions involving the effects of contaminants on linked aquatic-terrestrial food webs. Stylized cases exemplify how application of AT2 can help practitioners and researchers design studies when the contaminants present at a site are likely to lead to injury of terrestrial aerial insectivores through loss of aquatic insect prey and/or dietary contaminant exposure. Designing such studies with ecological endpoints and economic modeling inputs in mind will increase the relevance and cost-effectiveness of studies, which can in turn improve the outcomes of cases and studies involving the ecological effects of contaminants on food webs.
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The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.
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However, managers must periodically remove free-roaming horses from the landscape to reduce the risk of habitat degradation. We developed an individual-based population simulation model to evaluate the expected change in genetic diversity after 100 years under each of four removal strategies for the wild horse population in the Pryor Mountain management area with removals occurring every 5 years, and assuming that no additional horses are introduced to the herd. We found that long generations and high survival rates of this wild horse population guard against rapid loss of genetic diversity for all scenarios in general. However, scenarios that included a removal strategy prioritizing individuals for removal based on relatedness initially increased mean genetic diversity that was subsequently maintained at a higher level than strategies that randomly selected individuals for removal.","language":"English","publisher":"Bureau of Land Management","usgsCitation":"Zimmerman, S.J., Fike, J., and Oyler-McCance, S.J., 2023, Simulation of genetic change under four removal strategies for a wild horse population, 49 p.","productDescription":"49 p.","ipdsId":"IP-142753","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":415052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415035,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eplanning.blm.gov/eplanning-ui/project/1502632/570"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Shawna J 0000-0003-3394-6102 szimmerman@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-6102","contributorId":238076,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Shawna","email":"szimmerman@usgs.gov","middleInitial":"J","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868376,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241200,"text":"fs20233006 - 2023 - Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022","interactions":[],"lastModifiedDate":"2026-02-04T20:37:47.514644","indexId":"fs20233006","displayToPublicDate":"2023-03-14T16:03:33","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3006","displayTitle":"Application of Geophysical Methods to Enhance Aquifer Characterization and Groundwater-Flow Model Development, Des Moines River Alluvial Aquifer, Des Moines, Iowa, 2022","title":"Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022","docAbstract":"Des Moines Water Works (DMWW) is one of the largest water providers in Iowa and as population growth continues, demand for drinking water is increasing. DMWW uses groundwater and surface water as raw water sources to supply the City of Des Moines and surrounding communities. In response to current and future demands, DMWW is in need of a thorough understanding of local groundwater resources, specifically the Des Moines River alluvial aquifer. The Des Moines River alluvial aquifer is hydraulically connected to the Des Moines River and consists of alluvial deposits and glacial outwash sands and gravels. To ensure a sustainable groundwater supply, additional information to better understand and manage groundwater availability within the Des Moines River alluvial aquifer would be beneficial. Beginning in 2018, DMWW partnered with the U.S. Geological Survey to construct a groundwater-flow model to increase understanding of the hydrologic system in the Des Moines area. The model hydrogeologic framework will be enhanced by using multiple geophysical methods of data collection in the Des Moines River, Beaver Creek, and the Des Moines River alluvial aquifer that could provide a better understanding of the geology in the model area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233006","usgsCitation":"Thomas, J.C., Spring, M.A., Gruhn, L.R., and Bristow, E.L., 2023, Application of geophysical methods to enhance aquifer characterization and groundwater-flow model development, Des Moines River alluvial aquifer, Des Moines, Iowa, 2022: U.S. Geological Survey Fact Sheet 2023–3006, 4 p., https://doi.org/10.3133/fs20233006.","productDescription":"Report: 4 p.; 2 Data Releases; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136349","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":414104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3006"},{"id":414105,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3006/fs20233006.XML","description":"FS 2023–3006"},{"id":414103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3006/coverthb2.jpg"},{"id":414112,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS dataset","linkHelpText":"—National Hydrography Dataset— USGS National Hydrography Dataset Best Resolution for Hydrologic Unit 4 – 2001"},{"id":414109,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":414110,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499566,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114475.htm","linkFileType":{"id":5,"text":"html"}},{"id":414138,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":414113,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3006/images"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Des Moines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.69895287733283,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.52388190639587\n ],\n [\n -93.51363729010005,\n 41.66093681949087\n ],\n [\n -93.69895287733283,\n 41.66093681949087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. Finally, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.
Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process-guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.
Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions.
An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km2).
First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy.
Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover.
We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01621-1","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D., Rongsong, L., Requena-Mullor, J.M., and Caughlin, T., 2023, Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching: Landscape Ecology, v. 38, https://doi.org/10.1007/s10980-023-01621-1.","productDescription":"16 p.","startPage":"1306","ipdsId":"IP-144778","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":414693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.76040258225669,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 42.96440805189778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","edition":"1291","noUsgsAuthors":false,"publicationDate":"2023-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii","contributorId":257073,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":867435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":867436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rongsong, Liu","contributorId":303384,"corporation":false,"usgs":false,"family":"Rongsong","given":"Liu","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":867437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867439,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241418,"text":"70241418 - 2023 - Climate-driven tradeoffs between landscape connectivity and the maintenance of the coastal carbon sink","interactions":[],"lastModifiedDate":"2023-03-31T15:21:13.569405","indexId":"70241418","displayToPublicDate":"2023-03-13T06:57:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Climate-driven tradeoffs between landscape connectivity and the maintenance of the coastal carbon sink","docAbstract":"Ecosystem connectivity tends to increase the resilience and function of ecosystems responding to stressors. Coastal ecosystems sequester disproportionately large amounts of carbon, but rapid exchange of water, nutrients, and sediment makes them vulnerable to sea level rise and coastal erosion. Individual components of the coastal landscape (i.e., marsh, forest, bay) have contrasting responses to sea level rise, making it difficult to forecast the response of the integrated coastal carbon sink. Here we couple a spatially-explicit geomorphic model with a point-based carbon accumulation model, and show that landscape connectivity, in-situ carbon accumulation rates, and the size of the landscape-scale coastal carbon stock all peak at intermediate sea level rise rates despite divergent responses of individual components. Progressive loss of forest biomass under increasing sea level rise leads to a shift from a system dominated by forest biomass carbon towards one dominated by marsh soil carbon that is maintained by substantial recycling of organic carbon between marshes and bays. These results suggest that climate change strengthens connectivity between adjacent coastal ecosystems, but with tradeoffs that include a shift towards more labile carbon, smaller marsh and forest extents, and the accumulation of carbon in portions of the landscape more vulnerable to sea level rise and erosion.
Wildfires pose a risk to water supplies in the western U.S. and many other parts of the world, due to the potential for degradation of water quality. However, a lack of adequate data hinders prediction and assessment of post-wildfire impacts and recovery. The dearth of such data is related to lack of funding for monitoring extreme events and the challenge of measuring the outsized hydrologic and erosive response after wildfire. Assessment and prediction of post-wildfire surface water quality would be strengthened by the strategic monitoring of key parameters, and the selection of sampling locations based on the following criteria: (1) streamgage with pre-wildfire data; (2) ability to install equipment that can measure water quality at high temporal resolution, with a focus on storm sampling; (3) minimum of 10% drainage area burned at moderate to high severity; (4) lack of major water management; (5) high-frequency precipitation; and (6) availability of pre-wildfire water-quality data and (or) water-quality data from a comparable unburned basin. Water-quality data focused on parameters that are critical to human and (or) ecosystem health, relevant to water-treatment processes and drinking-water quality, and (or) inform the role of precipitation and discharge on flow paths and water quality are most useful. We discuss strategic post-wildfire water-quality monitoring and identify opportunities for advancing assessment and prediction. Improved estimates of the magnitude, timing, and duration of post-wildfire effects on water quality would aid the water resources community prepare for and mitigate against impacts to water supplies.
The reproductive ecology of geese that breed in the Arctic and subarctic is likely susceptible to the effects of climate change, which is projected to alter the environmental conditions of northern latitudes. Nest survival is an important component of productivity in geese; however, the effects of regional environmental conditions on nest survival are not well understood for some species, including the Emperor Goose (Anser canagicus), a species of conservation concern that is endemic to the Bering Sea region. We estimated nest survival and examined how indices of regional environmental conditions, nest traits (nest age, initiation date, and maximum number of eggs in the nest), and researcher disturbance influenced daily survival probability (DSP) of Emperor Goose nests using hierarchical models and 24 years of nest monitoring data (1994–2017) from the Yukon–Kuskokwim Delta (Y–K Delta) in western Alaska. Our results indicate that overall nest survival was generally high (µ = 0.766, 95% CRI: 0.655–0.849) and ranged from 0.327 (95% CRI: 0.176–0.482) in 2013 to 0.905 (95% CRI: 0.839–0.953) in 1995. We found that DSPs of nests were influenced by nest traits, negatively influenced by major tidal flooding events and by researcher disturbance, but were not influenced by regional indices of spring timing, temperature and precipitation during nesting, or fox and vole abundance on the Y–K Delta. However, the number of nests found each year was negatively related to our index of fox abundance, suggesting nests that failed as a result of fox predation may have never been discovered due to our limited nest-searching efforts during egg laying. Our results suggest that regional environmental variation had minimal influence on the nest survival of Emperor Geese, although major flooding events were important. Nevertheless, we suspect that within-year variation in local weather conditions and local abundance of predators and alternative prey may be important and should be considered in future studies.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duad008","usgsCitation":"Thompson, J.M., Uher-Koch, B.D., Daniels, B.L., Schmutz, J.A., and Sedinger, B.S., 2023, Nest traits and major flooding events influence nest survival of Emperor Geese while regional environmental variation linked to climate does not: Ornithological Applications, v. 125, no. 2, duad008, 14 p., https://doi.org/10.1093/ornithapp/duad008.","productDescription":"duad008, 14 p.","ipdsId":"IP-144477","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":444247,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1093/ornithapp/duad008","text":"Publisher Index Page"},{"id":435417,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9891BFB","text":"USGS data release","linkHelpText":"Emperor Goose (Anser canagicus) Nest Survival Encounter History from the Yukon-Kuskokwim Delta, Alaska, 1994-2017"},{"id":416853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.0853296316241,\n 61.527680543295105\n ],\n [\n -166.0853296316241,\n 60.419454074425744\n ],\n [\n -163.581422052621,\n 60.419454074425744\n ],\n [\n -163.581422052621,\n 61.527680543295105\n ],\n [\n -166.0853296316241,\n 61.527680543295105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Jordan M.","contributorId":303133,"corporation":false,"usgs":false,"family":"Thompson","given":"Jordan","email":"","middleInitial":"M.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":872081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":872082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, Bryan L.","contributorId":304964,"corporation":false,"usgs":false,"family":"Daniels","given":"Bryan","email":"","middleInitial":"L.","affiliations":[{"id":66195,"text":"Yukon Delta National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":872083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmutz, Joel A.","contributorId":304965,"corporation":false,"usgs":false,"family":"Schmutz","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":66196,"text":"Alaska Science Center WTEB (retired)","active":true,"usgs":false}],"preferred":false,"id":872084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sedinger, Benjamin S.","contributorId":304966,"corporation":false,"usgs":false,"family":"Sedinger","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":872085,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241155,"text":"70241155 - 2023 - Wastewater reuse and predicted ecological risk posed by contaminant mixtures in Potomac River watershed streams","interactions":[],"lastModifiedDate":"2023-08-07T17:00:33.651676","indexId":"70241155","displayToPublicDate":"2023-03-10T06:58:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Wastewater reuse and predicted ecological risk posed by contaminant mixtures in Potomac River watershed streams","docAbstract":"A wastewater model was applied to the Potomac River watershed to provide (i) a means to identify streams with a high likelihood of carrying elevated effluent-derived contaminants and (ii) risk assessments to aquatic life and drinking water. The model linked effluent discharges along stream networks, accumulated wastewater, and predicted contaminant loads of municipal wastewater constituents while accounting for instream dilution and attenuation. Simulations using 2016 data suggested that nearly 30% (8281 km) of streams were wastewater impacted. Low- to medium-order streams had the largest range of accumulated wastewater (ACCWW%) values. ACCWW% exceeded a 1% threshold at >39% of drinking-water intakes (varied by temporal condition). Risk assessments of municipal wastewater-contaminant mixtures indicated that 22% (1479 km) of streams impacted by municipal wastewater (5.5% of all reaches modeled) may pose high risk to aquatic organisms under mean-annual conditions, with fish more susceptible to chronic-exposure effects relative to other taxa. Risk varied temporally and by stream order, with the greatest risk occurring in the summer in small streams. These findings suggest that wastewater may be an important factor contributing to environmental degradation in the Potomac River watershed.
This three-dimensional (3D) geologic map displays the subsurface geology in the upper ~4 kilometers of the Earth’s crust in the southeastern Gabbs Valley geothermal area of west-central Nevada. The 3D map was constructed by integrating the results from detailed geologic mapping, 3D gravity inversion modeling, and potential-field-geophysical studies. This effort was undertaken as part of the Nevada Play Fairway Project, a regional effort to characterize new geothermal resources in the United States. Local data collection and analysis in southeastern Gabbs Valley, Nevada, including the construction of this 3D map, led to the drilling of six temperature gradient wells and identification of previously unknown hydrothermal fluids at 150 meters depth. The measured temperatures, which are as high as 124.9 degrees Celsius, indicate that the southeastern Gabbs Valley hydrothermal system has temperatures that are comparable to geothermal fields that have been developed for electricity generation in the region. We describe the geologic units and structures displayed by the map and discuss the methods used to integrate the geologic and geophysical information into the 3D geologic interpretation. The accompanying map provides horizontal and vertical section views and oblique perspective views from several angles. The digital data for elements of the map, the individual 3D fault surfaces, and stratigraphic surfaces are available from Siler (2022). The accompanying map sheet and video displaying the 3D map are available at https://doi.org/10.3133/sim3498.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3498","usgsCitation":"Siler, D.L., Witter, J.B., Craig, J.W., Earney, T.E., Schermerhorn, W.D., Fournier, D., Faulds, J.E., Glen, J.M.G., and Peacock, J.R., 2023, Three-dimensional geologic map the southeastern Gabbs Valley geothermal area, Nevada: U.S. Geological Survey Scientific Investigations Map 3498, 23 p., 1 sheet, https://doi.org/10.3133/sim3498.","productDescription":"Pamphlet: v, 23 p.; 1 Sheet: 46.00 × 39.00 inches; Video","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122870","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500204,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114474.htm","linkFileType":{"id":5,"text":"html"}},{"id":413934,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3498/sim3498_video.mp4","text":"Video","size":"45 MB MP4"},{"id":413933,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3498/sim3498_pamphlet.pdf","text":"Pamphlet","size":"20 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":413932,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3498/sim3498_sheet.pdf","text":"Sheet","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":413931,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3498/covrthb.jpg"},{"id":413930,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BR3681","text":"Stratigraphic and fault surfaces from the three-dimensional geologic map of the southeastern Gabbs Valley geothermal area","description":"Siler, D.L., 2022, Stratigraphic and fault surfaces from the three-dimensional geologic map of the southeastern Gabbs Valley geothermal area: U.S. Geological Survey data release, https://doi.org/10.5066/P9BR3681."}],"country":"United States","state":"Nevada","otherGeospatial":"Gabbs Valley geothermal area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.3,\n 38.3\n ],\n [\n -117.3,\n 39.0\n ],\n [\n -119.0,\n 39.0\n ],\n [\n -119.0,\n 38.3\n ],\n [\n -117.3,\n 38.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center Moffett Field
U.S. Geological Survey
350 N. Akron Rd. P.O. Box 158
Moffett Field, CA 94035
Pumas (Puma concolor) are solitary large carnivores that exhibit high energetic investments while hunting prey that often take multiple days to consume. Therefore, pumas should behave in a way to maximize their energetic gains, including using caching, which is a behavior used by many mammal species to preserve and store food or to conceal it from conspecifics and scavengers to limit their losses. Yet pumas do not always cache their kills. In order to understand caching behavior, we used variables associated with the kills such as prey mass, search time, climate, and habitat to test 20 ecological models (representing four a priori hypotheses: food perishability, resource pulse, consumption time, and kleptoparasitism deterrence) in an information-theoretic approach of model selection to explore factors related to the caching behavior. Models were run with information from tracked radio-collared pumas in California over a 2.5-year period and identified a total of 352 kills. Overall, we documented pumas caching 61.5% of their kills, including 71.6% of Black-tailed Deer (Odocoileus hemionus columbianus), their primary prey in the study area. The model with a quadratic effect of adjusted mass of prey on puma caching probability had all of the empirical support (w = 1.00). Specifically, pumas were most likely to cache intermediate-sized prey, such as yearling and adult female deer, and also fed from cached kills for longer periods of time. Larger prey may be too large to easily cache, making it less energetically efficient—while small prey can often be consumed quickly enough to not require caching. This suggests that intermediate-sized prey may be the optimal size for caching, allowing a puma to feed for multiple days while not greatly increasing energetic output. The hypotheses we tested were not mutually exclusive and pumas caching their prey may occur for several reasons; nevertheless, our study demonstrated that pumas use caching to extend their foraging time and maximize energetic gains when preying on intermediate-sized prey.
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