We have identified and provided ecological interpretations of 30 taxa recovered at two shellmounds at the Dominican University of California archaeology site in Marin County, California (CA-MRN-254). A Q-mode cluster analysis was used to group the samples according to their faunal similarity. The clusters ranged from a diverse grouping of 100 samples with 27 taxa (Cluster A) to those with a more restricted assemblage (4–9 taxa in Clusters B to E). The Q-mode clusters were then used to interpret the variability in food resources utilized through the 1800 years of site occupation. During the Intermediate Middle Period (A.D.100-300), the inhabitants appeared to be selective in the marine taxa they used, evident by the presence of Cluster B and E assemblages. A diverse (Cluster A) assemblage was then utilized at the site at one or both of the shellmounds through the remainder of the occupancy period, including the Middle/Late Period Transition (A.D. 700–900) and Late Period Phase 1C (A.D. 900–1300), coincident with the extensive drought conditions of the Medieval Climatic Anomaly (MCA) in the San Francisco Bay area. These findings suggest the marine invertebrate resources utilized by the site occupants were not significantly affected by the persistent aridity associated with the MCA.
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2088651","usgsCitation":"Gumma, M., Thenkabail, P., Panjala, P., Teluguntla, P., Yamano, T., and Mohammad, I., 2022, Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security: GIScience & Remote Sensing, v. 59, no. 1, p. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.","productDescription":"30 p.","startPage":"1048","endPage":"1077","ipdsId":"IP-135578","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2088651","text":"Publisher Index 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Murali Krishna","contributorId":294754,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali Krishna","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":848826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panjala, Pranay","contributorId":294756,"corporation":false,"usgs":false,"family":"Panjala","given":"Pranay","email":"","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi","contributorId":294758,"corporation":false,"usgs":false,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":63639,"text":"Bay Area Environmental Research Institute (BAERI) @ USGS","active":true,"usgs":false}],"preferred":false,"id":848828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamano, Takashi","contributorId":294759,"corporation":false,"usgs":false,"family":"Yamano","given":"Takashi","email":"","affiliations":[{"id":63641,"text":"Asian Development Bank (ADB)","active":true,"usgs":false}],"preferred":false,"id":848829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mohammad, Ismail","contributorId":294760,"corporation":false,"usgs":false,"family":"Mohammad","given":"Ismail","email":"","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233233,"text":"70233233 - 2022 - Martian gully activity and the gully sediment transport system","interactions":[],"lastModifiedDate":"2022-07-19T12:01:29.241941","indexId":"70233233","displayToPublicDate":"2022-07-13T06:55:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Martian gully activity and the gully sediment transport system","docAbstract":"The formation process for Martian gullies is a critical unknown for understanding recent climate conditions. Leading hypotheses include formation by snowmelt in a past climate, or formation via currently active CO2 frost processes. This paper presents an expanded catalog of >300 recent flows in gullies. The results indicate that sediment transport in current gully flows moves the full range of materials needed for gully formation. New flows are more likely to transport boulders in gullies that have pre-existing boulder-covered aprons, indicating that current flows are transporting the same materials required for gully formation overall. The distribution of gully activity frequencies can be described by a power law and indicates that the recurrence intervals for flows in individual gullies are commonly tens to hundreds of Mars years. Over the last ~300 kyr, climate variations have been modest but individual gullies have had tens to thousands of flow events. This could be sufficient to account for the entirety of gully formation in some cases, although the same processes are likely to have occurred further in the past. For any gullies that may have initiated under higher-obliquity conditions, this level of recent activity indicates that the observable morphology has been shaped by CO2-driven flows. These observations of sediment transport and the tempo of gully activity are consistent with gully formation entirely by CO2 frost processes, likely with spatial and temporal variability, but with no role required for liquid water.
The Upper Geyser Basin at Yellowstone National Park (Wyoming, USA) harbors the greatest concentration of geysers worldwide. Research suggests that individual geysers are not isolated but rather are hydraulically connected in the subsurface with other geysers and thermal springs. To quantify such connections, we combined techniques from machine learning, causal inference, and dynamical systems to characterize the collective eruptive behavior of a set of 10 geysers over 18 months (April 2007 – September 2008) focusing on geyser-geyser interactions. Model predictions were up to 15 times more accurate when we sought to predict a geyser's eruption time series based on outflow channel temperatures from the network than based on its own time series alone, suggesting the existence of a complex interconnected subsurface groundwater system. On average, cone-type geysers had larger impacts on other geysers than did fountain-type geysers. Similarly, cone-type geysers were on average more insulated from other geysers. However, substantial unexplained variation remained after considering the cone versus fountain dichotomy. Distance between geysers also affected interactions: nearby geysers had stronger effects on focal geysers than did geysers located farther away. Collectively, results support the hypothesis of geyser interdependence at timescales of 5 min–10 days. Our analyses highlight the existence of quantifiable geyser-to-geyser interactions that can be resolved through pairwise and system-level analyses. These findings emphasize the subsurface interconnectedness of thermal features, provide information relevant to visitor experiences in Yellowstone National Park, and suggest strategies for exploring patterns of interdependence that may exist among other episodic geological phenomena.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023749","usgsCitation":"Fagan, W., Swain, A., Banerjee, A., Ranade, H., Thompson, P., Staniczenko, P.P., Flynn, B., Hungerford, J., and Hurwitz, S., 2022, Quantifying interdependencies in geyser eruptions at the Upper Geyser Basin, Yellowstone National Park: Journal of Geophysical Research, v. 127, no. 8, e2021JB023749, 23 p., https://doi.org/10.1029/2021JB023749.","productDescription":"e2021JB023749, 23 p.","ipdsId":"IP-137582","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":405590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Upper Geyser Basin, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.839,\n 44.459\n ],\n [\n -110.823,\n 44.459\n ],\n [\n -110.823,\n 44.467\n ],\n [\n -110.839,\n 44.467\n ],\n [\n -110.839,\n 44.459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":849649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swain, Anshuman","contributorId":295531,"corporation":false,"usgs":false,"family":"Swain","given":"Anshuman","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banerjee, Amitava","contributorId":295532,"corporation":false,"usgs":false,"family":"Banerjee","given":"Amitava","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ranade, Hamir","contributorId":295533,"corporation":false,"usgs":false,"family":"Ranade","given":"Hamir","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Peter","contributorId":295535,"corporation":false,"usgs":false,"family":"Thompson","given":"Peter","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staniczenko, Phillip P. A.","contributorId":295537,"corporation":false,"usgs":false,"family":"Staniczenko","given":"Phillip","email":"","middleInitial":"P. A.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flynn, Barrett","contributorId":295539,"corporation":false,"usgs":false,"family":"Flynn","given":"Barrett","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849655,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hungerford, Jefferson 0000-0003-2651-2285","orcid":"https://orcid.org/0000-0003-2651-2285","contributorId":229552,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849656,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":849657,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237720,"text":"70237720 - 2022 - Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA","interactions":[],"lastModifiedDate":"2022-10-21T13:37:46.04698","indexId":"70237720","displayToPublicDate":"2022-07-12T08:30:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA","docAbstract":"The Kodiak Islands lie near the southern terminus of the 1964 Great Alaska earthquake rupture area and within the Kodiak subduction zone segment. Both local and trans-Pacific tsunamis were generated during this devastating megathrust event, but the local tsunami source region and the causative faults are poorly understood. We provide an updated view of the tsunami and earthquake hazard for the Kodiak Islands region through tsunami modeling and geophysical data analysis. Using seismic and bathymetric data, we characterize a regionally extensive seafloor lineament related to the Kodiak shelf fault zone, with focused uplift along a 50-km-long portion of the newly named Ugak fault as the most likely source of the local Kodiak Islands tsunami in 1964. We present evidence of Holocene motion along the Albatross Banks fault zone, but we suggest that this fault did not produce a tsunami in 1964. We relate major structural boundaries to active forearc splay faults, where tectonic uplift is collocated with gravity lineations. Differences in interseismic locking, seismicity rates, and potential field signatures argue for different stress conditions at depth near presumed segment boundaries. We find that the Kodiak segment boundaries have a clear geophysical expression and are linked to upper-plate structure and splay faulting. The tsunamigenic fault hazard is higher for the Kodiak shelf fault zone when compared to the nearby Albatross Banks fault zone, suggesting short wave travel paths and little tsunami warning time for nearby communities.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02486.1","usgsCitation":"Ramos, M.D., Liberty, L.M., Haeussler, P., and Humphreys, R.J., 2022, Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA: Geosphere, v. 18, no. 5, p. 1474-1491, https://doi.org/10.1130/GES02486.1.","productDescription":"18 p.","startPage":"1474","endPage":"1491","ipdsId":"IP-135286","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02486.1","text":"Publisher Index Page"},{"id":408601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.4037941171657,\n 60.562811262269065\n ],\n [\n -151.83516963619837,\n 61.55980516417185\n ],\n [\n -156.47488362730599,\n 57.79635099382884\n ],\n [\n -154.29366188512998,\n 55.202746146556194\n ],\n [\n -147.4037941171657,\n 60.562811262269065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos, Marlon D. 0000-0003-4449-8624","orcid":"https://orcid.org/0000-0003-4449-8624","contributorId":293255,"corporation":false,"usgs":false,"family":"Ramos","given":"Marlon","email":"","middleInitial":"D.","affiliations":[{"id":63266,"text":"Air Force Research Lab","active":true,"usgs":false}],"preferred":false,"id":855359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liberty, Lee M","contributorId":194078,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":855360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":855361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphreys, Robert John 0000-0002-6733-6399","orcid":"https://orcid.org/0000-0002-6733-6399","contributorId":298308,"corporation":false,"usgs":true,"family":"Humphreys","given":"Robert","email":"","middleInitial":"John","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855362,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233556,"text":"70233556 - 2022 - Revisiting the 1899 earthquake series using integrative geophysical analysis in Yakutat Bay, Alaska","interactions":[],"lastModifiedDate":"2023-11-08T18:01:12.38369","indexId":"70233556","displayToPublicDate":"2022-07-12T07:16:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting the 1899 earthquake series using integrative geophysical analysis in Yakutat Bay, Alaska","docAbstract":"A series of large earthquakes in 1899 affected southeastern Alaska near Yakutat and Disenchantment Bays. The largest of the series, a MW 8.2 event on 10 September 1899, generated an ~12-m-high tsunami and as much as 14.4 m of coseismic uplift in Yakutat Bay, the largest coseismic uplift ever measured. Several complex fault systems in the area are associated with the Yakutat terrane collision with North America and the termination of the Fairweather strike-slip system, but because faults local to Yakutat Bay have been incompletely or poorly mapped, it is unclear which fault system(s) ruptured during the 10 September 1899 event. Using marine geophysical data collected in August 2012, we provide an improved tectonic framework for the Yakutat area, which advances our understanding of earthquake hazards. We combined 153 line km of 2012 high-resolution multichannel seismic (MCS) reflection data with compressed high-intensity radar pulse (Chirp) profiles, basin-scale MCS data, 2018 seafloor bathymetry, published geodetic models and thermochronology data, and previous measurements of coseismic uplift to better constrain fault geometry and subsurface structure in the Yakutat Bay area. We did not observe any active or concealed faults crossing Yakutat Bay in our high-resolution data, requiring faults to be located entirely onshore or nearshore. We interpreted onshore faults east of Yakutat Bay to be associated with the transpressional termination of the Fairweather fault system, forming a series of splay faults that exhibit a horsetail geometry. Thrust and reverse faults on the west side of the bay are related to Yakutat terrane underthrusting and collision with North America. Our results include an updated fault map, structural model of Yakutat Bay, and quantitative assessment of uncertainties for legacy geologic coseismic uplift measurements. Additionally, our results indicate the 10 September 1899 rupture was possibly related to stress loading from the earlier Yakutat terrane underthrusting event of 4 September 1899, with the majority of 10 September coseismic slip occurring on the Esker Creek system on the northwest side of Yakutat Bay. Limited (~2 m) coseismic or postseismic slip associated with the 1899 events occurred on faults located east of Yakutat Bay.
We present a response to Butterworth and Ross-Gillespie's (2022) comment on our perspectives on how forage fish fisheries are impacting the endangered African penguin (Sphenicus demersus), and corresponding management options. Butterworth and Ross-Gillespie overstate model uncertainties and downplay the clear ecological and conservation significance of the fisheries closure experiment. We demonstrate that their criticism of “pseudo-replication” is weak, and not in line with their own analyses nor with the interpretations of many international scientific review panels commissioned by the government of South Africa to evaluate experimental results. Their comment does not alter our fundamental conclusions that forage fisheries operating near penguin breeding colonies compete with the birds for food resources, are detrimental to the penguin's population health, and are impeding recovery. Given that sardines are depleted (DFFE, 2021) and the African penguin is approaching a conservation crisis, we reiterate our position that continuing the precautionary approach of closures at the local scale of central-place foraging penguins is warranted to facilitate their population growth under fisheries management goals to conserve and maintain ecosystem functions.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsac116","usgsCitation":"Sydeman, B., Hunt, G., Pikitch, E., Parrish, J., Piatt, J., Boersma, D., Kaufman, L., Anderson, D.L., Thompson, S., and Sherley, R.B., 2022, African penguins and localized fisheries management: Response to Butterworth and Ross-Gillespie: ICES Journal of Marine Science, fsac116, 7 p., https://doi.org/10.1093/icesjms/fsac116.","productDescription":"fsac116, 7 p.","ipdsId":"IP-141361","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":403999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Sydeman, Bill","contributorId":293222,"corporation":false,"usgs":false,"family":"Sydeman","given":"Bill","email":"","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":846803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, Gene","contributorId":178704,"corporation":false,"usgs":false,"family":"Hunt","given":"Gene","email":"","affiliations":[],"preferred":false,"id":846804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pikitch, E.K.","contributorId":152152,"corporation":false,"usgs":false,"family":"Pikitch","given":"E.K.","email":"","affiliations":[],"preferred":false,"id":846805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parrish, J.","contributorId":149527,"corporation":false,"usgs":false,"family":"Parrish","given":"J.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":846806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Piatt, John F. 0000-0002-4417-5748","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":244053,"corporation":false,"usgs":true,"family":"Piatt","given":"John F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":846807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boersma, D.","contributorId":293225,"corporation":false,"usgs":false,"family":"Boersma","given":"D.","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":846808,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaufman, L.","contributorId":293227,"corporation":false,"usgs":false,"family":"Kaufman","given":"L.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":846809,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, D. L.","contributorId":274874,"corporation":false,"usgs":false,"family":"Anderson","given":"D.","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":846810,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, S.","contributorId":77103,"corporation":false,"usgs":false,"family":"Thompson","given":"S.","email":"","affiliations":[],"preferred":false,"id":846811,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sherley, Richard B.","contributorId":198407,"corporation":false,"usgs":false,"family":"Sherley","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":846812,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70237712,"text":"70237712 - 2022 - The North American tree-ring fire-scar network","interactions":[],"lastModifiedDate":"2022-10-20T11:49:06.295032","indexId":"70237712","displayToPublicDate":"2022-07-12T06:45:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The North American tree-ring fire-scar network","docAbstract":"Fire regimes in North American forests are diverse and modern fire records are often too short to capture important patterns, trends, feedbacks, and drivers of variability. Tree-ring fire scars provide valuable perspectives on fire regimes, including centuries-long records of fire year, season, frequency, severity, and size. Here, we introduce the newly compiled North American tree-ring fire-scar network (NAFSN), which contains 2562 sites, >37,000 fire-scarred trees, and covers large parts of North America. We investigate the NAFSN in terms of geography, sample depth, vegetation, topography, climate, and human land use. Fire scars are found in most ecoregions, from boreal forests in northern Alaska and Canada to subtropical forests in southern Florida and Mexico. The network includes 91 tree species, but is dominated by gymnosperms in the genus Pinus. Fire scars are found from sea level to >4000-m elevation and across a range of topographic settings that vary by ecoregion. Multiple regions are densely sampled (e.g., >1000 fire-scarred trees), enabling new spatial analyses such as reconstructions of area burned. To demonstrate the potential of the network, we compared the climate space of the NAFSN to those of modern fires and forests; the NAFSN spans a climate space largely representative of the forested areas in North America, with notable gaps in warmer tropical climates. Modern fires are burning in similar climate spaces as historical fires, but disproportionately in warmer regions compared to the historical record, possibly related to under-sampling of warm subtropical forests or supporting observations of changing fire regimes. The historical influence of Indigenous and non-Indigenous human land use on fire regimes varies in space and time. A 20th century fire deficit associated with human activities is evident in many regions, yet fire regimes characterized by frequent surface fires are still active in some areas (e.g., Mexico and the southeastern United States). These analyses provide a foundation and framework for future studies using the hundreds of thousands of annually- to sub-annually-resolved tree-ring records of fire spanning centuries, which will further advance our understanding of the interactions among fire, climate, topography, vegetation, and humans across North America.
The grave threat posed by the Enriquillo‐Plantain Garden fault zone (EPGFZ) and other fault systems on the Tiburon Peninsula in southern Haiti was highlighted by the catastrophic M 7.0 Léogâne earthquake on 12 January 2010 and again by the deadly M 7.2 Nippes earthquakes on 14 August 2021. Early Interferometric Synthetic Aperture Radar observations suggest the 2021 earthquake broke structures associated with this fault system farther west of the 2010 event, but the rupture zones of both events are separated by a ∼50 km gap. This sequence provided the impetus to reconsider a nineteenth century earthquake that may have occurred within this gap. Though previous studies identified a single moderately large event on 8 April 1860, original sources describe a complex and distributed seismic sequence to the west of Port‐au‐Prince. These provide evidence for an initial event to the west of Les Cayes, on the southern coast of the Tiburon Peninsula. This was followed on the morning of 8 April 1860 by a damaging earthquake near l’Anse‐à‐Veau along the northern coast of the peninsula, which was succeeded 14 hr later by a larger mainshock to the east. Although locations cannot be determined precisely from extant macroseismic data, our preferred scenario includes an intensity magnitude
Understanding effects of human water use and subsequent return flows on the availability and suitability of water for downstream uses is critical to efficient and effective watershed management. We compared spatially detailed estimates of stream chemistry within three watersheds in diverse settings to available standards to isolate effects of wastewater and irrigation return flows on the suitability of downstream waters for maintaining healthy aquatic ecosystems and for selected human uses. Mean-annual flow-weighted total and source-specific concentrations of nitrogen and phosphorus in individual stream reaches within the Upper Colorado, Delaware, and Illinois River Basins and of total dissolved solids within stream reaches of the Upper Colorado River Basin were estimated from previously calibrated regional watershed models. Estimated concentrations of both nitrogen and phosphorus in most stream reaches in all three watersheds (at least 78%, by length) exceed recommended standards for the protection of aquatic ecosystems, although concentrations in relatively few streams exceed such standards due to contributions from wastewater return flows, alone. Consequently, efforts to reduce wastewater nutrient effluent may provide important local downstream benefits but would likely have minimal impact on regional ecological conditions. Similarly, estimated mean-annual flow-weighted total dissolved solids concentrations in the Upper Colorado River Basin exceed standards for agricultural water use and (or) the secondary maximum contaminant level (SMCL) for drinking water in 52% of streams (by length), but rarely due to effects of irrigation return flows, alone. Dissolved solids in most tributaries of the Upper Colorado River are attributable primarily to natural sources.
Germanium (Ge) is a metal used in emerging energy technologies, communications, and defense, and has been deemed critical by the United States due to its essential applications and scarce supply. Germanium is recovered as a byproduct of zinc (Zn) sulfides, and mining and processing of these materials lead to waste that could act both as a source of extractable Ge and a source for exposure to humans and ecosystems. Yet the distribution, speciation, and mineral hosts of Ge in mining-impacted areas are poorly understood. The Tar Creek Superfund Site, a former Zn mining area and Ge producer, is a natural laboratory to understand the environmental behavior and economic implications of Ge in mine wastes. We studied the distribution and behavior of Ge in solid wastes at the Tar Creek Superfund Site using bulk and microanalytical techniques. In wastes at this site we find that Ge has been redistributed from its original host, sphalerite (ZnS), to the fine-grained weathering product hemimorphite (Zn4Si2O7(OH)2·H2O), which impacts germanium's mobility, bioaccessibility, and potential for recovery. We provide chemical and mineralogical evidence of this redistribution, along with an evaluation of the oxidation state and molecular-scale substitution of Ge into sphalerite, hemimorphite, and quartz. Geochemical modeling shows that hemimorphite is more stable than sphalerite in waste piles and provides a stable secondary repository for Ge. However, hemimorphite is fine-grained, and if ingested or inhaled is readily soluble, with the potential to release Ge. Lastly, we discuss other sites internationally where similar behavior may be important. This study shows that weathering can have a significant impact on the distribution, speciation, and mineral hosts of Ge in mine wastes; directly influence mobilization from waste piles and subsequent availability to humans and ecosystems; and dictate metallurgical strategies to target Ge for recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2022.105341","usgsCitation":"White, S.J., Piatak, N.M., McAleer, R.J., Hayes, S.M., Seal,, R., Schaider, L.A., and Shine, J.P., 2022, Germanium redistribution during weathering of Zn mine wastes: Implications for environmental mobility and recovery of a critical mineral: Applied Geochemistry, v. 143, 105341, 12 p., https://doi.org/10.1016/j.apgeochem.2022.105341.","productDescription":"105341, 12 p.","ipdsId":"IP-127786","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447162,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2022.105341","text":"Publisher Index Page"},{"id":435781,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM36FG","text":"USGS data release","linkHelpText":"Mineral abundances within bulk and size-fractionated mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435780,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI7VKH","text":"USGS data release","linkHelpText":"Molecular speciation of Ge within sphalerite, hemimorphite, and quartz from mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435779,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HHH5FL","text":"USGS data release","linkHelpText":"Geochemical, mineralogical, and molecular scale speciation characterization of mine wastes from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A. "},{"id":435778,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ALZZ3E","text":"USGS data release","linkHelpText":"Electron microprobe analyses of sphalerite and hemimorphite from mine wastes from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435777,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MXFIQ","text":"USGS data release","linkHelpText":"Elemental concentrations for bulk and size-fractionated mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":404206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Tar Creek Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.40502929687499,\n 36.049098959065645\n ],\n [\n -94.658203125,\n 36.049098959065645\n ],\n [\n -94.658203125,\n 37.00255267215955\n ],\n [\n -96.40502929687499,\n 37.00255267215955\n ],\n [\n -96.40502929687499,\n 36.049098959065645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Sarah Jane 0000-0002-4055-8207","orcid":"https://orcid.org/0000-0002-4055-8207","contributorId":216796,"corporation":false,"usgs":true,"family":"White","given":"Sarah","email":"","middleInitial":"Jane","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatak, Nadine M. 0000-0002-1973-8537 npiatak@usgs.gov","orcid":"https://orcid.org/0000-0002-1973-8537","contributorId":193010,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine","email":"npiatak@usgs.gov","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":847201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Sarah M. 0000-0001-5887-6492","orcid":"https://orcid.org/0000-0001-5887-6492","contributorId":208569,"corporation":false,"usgs":true,"family":"Hayes","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaider, Laurel A.","contributorId":291960,"corporation":false,"usgs":false,"family":"Schaider","given":"Laurel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":847204,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shine, James P.","contributorId":178314,"corporation":false,"usgs":false,"family":"Shine","given":"James","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":847205,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239303,"text":"70239303 - 2022 - Evidence for fluctuating wind in shaping an ancient Martian dune field: The Stimson formation at the Greenheugh pediment, Gale crater","interactions":[],"lastModifiedDate":"2023-01-09T13:14:36.209975","indexId":"70239303","displayToPublicDate":"2022-07-11T07:13:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7353,"text":"Journal of Geophysical Research - Planets","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for fluctuating wind in shaping an ancient Martian dune field: The Stimson formation at the Greenheugh pediment, Gale crater","docAbstract":"Temporal fluctuations of wind strength and direction can influence aeolian bedform morphology and orientation, which can be encoded into the architecture of aeolian deposits. These strata represent a direct record of atmospheric processes and can be used to understand ancient Martian atmospheric processes as well as those on Earth. The strata can: give insight to ancient atmospheric circulation, how the atmosphere evolved in response to global changes in habitability, and how ancient processes differ from modern processes. The Stimson formation at the Greenheugh pediment (Gale crater) records evidence of fluctuating wind across multiple temporal scales. The strata can be subdivided into three intervals–Gleann Beag, Ladder, and Edinburgh intervals. Internally, the intervals record changes of dune morphology and orientation, correlatable to wind fluctuations at multiple temporal scales. The basal Gleann Beag interval comprises compound cross-strata, deposited by oblique compound dunes. These dunes record a bimodal wind regime, resulting in net sediment transport toward the north. The Ladder interval records a reversal of sediment transport to the south, where straight-crested simple-dunes shaped by a seasonally variable winds formed. Finally, the Edinburgh interval records sediment transport to the west, where a unimodal wind formed sinuous-crested simple dunes. These observations demonstrate active and variable atmospheric circulation in Gale crater during the accumulation of the Stimson dune field, at multiple temporal scales from seasonally driven winds to much longer time-frames, during the Hesperian. These observations can be used to further understand ancient atmospheric conditions and processes, at a high temporal resolution on Mars.
Advances in multi-species monitoring have prompted an increase in the use of multi-species occupancy analyses to assess patterns of co-occurrence among species, even when data were collected at scales likely violating the assumption that sites were closed to changes in the occupancy state for the target species. Violating the closure assumption may lead to erroneous conclusions related to patterns of co-occurrence among species. Occurrence for two hypothetical species was simulated under patterns of avoidance, aggregation, or independence, when the closure assumption was either met or not. Simulated populations were sampled at two levels (N = 250 or 100 sites) and two scales of temporal resolution for surveys. Sample data were analyzed with conditional two-species occupancy models, and performance was assessed based on the proportion of simulations recovering the true pattern of co-occurrence. Estimates of occupancy were unbiased when closure was met, but biased when closure violations occurred; bias increased when sample size was small and encounter histories were collapsed to a large-scale temporal resolution. When closure was met and patterns of avoidance and aggregation were simulated, conditional two-species models tended to correctly find support for non-independence, and estimated species interaction factors (SIF) aligned with predicted values. By contrast, when closure was violated, models tended to incorrectly infer a pattern of independence and power to detect simulated patterns of avoidance or aggregation that decreased with smaller sample size. Results suggest that when the closure assumption is violated, co-occurrence models often fail to detect underlying patterns of avoidance or aggregation, and incorrectly identify a pattern of independence among species, which could have negative consequences for our understanding of species interactions and conservation efforts. Thus, when closure is violated, inferred patterns of independence from multi-species occupancy should be interpreted cautiously, and evidence of avoidance or aggregation is likely a conservative estimate of true pattern or interaction.
Climate model projections are commonly used to assess potential impacts of global warming on a breadth of social, economic, and environmental topics. Modeling centers throughout the world coordinate to apply a consistent suite of radiative forcing experiments so that all model outputs can be collectively analyzed and compared. Three generations of model outputs have been produced and made available to the scientific community through the Coupled Model Intercomparison Project (CMIP): CMIP3 disseminated during the mid-2000s, CMIP5 during the early-2010s, and CMIP6 during the late-2010s. Twenty-first century sea-ice projections from CMIP3 and CMIP5 models have been used in Bayesian network assessments of how climate change could impact the future persistence of polar bears (Ursus maritimus) throughout their range. In this report, we compare sea-ice projections by CMIP6 models to those of CMIP5 models in each of four polar bear ecoregions over the 21st century. We evaluate differences between the two CMIP generations with respect to other sources of variability that affect uncertainties of the model projections: (1) variability from different models; (2) variability from different greenhouse gas emissions scenarios; and (3) natural (internal) variability in the earth’s climate system. We found that natural variability as well as that attributable to models dominated uncertainties in sea-ice projections in all months and ecoregions during the first half of the 21st century, while emissions scenarios dominated uncertainties during the late 21st century. By comparison, we found only slight differences between the CMIP6 and CMIP5 model projections of sea ice. Applying CMIP6 instead of CMIP5 sea-ice projections to the polar bear Bayesian network model developed in 2016, therefore, would not qualitatively change the population status outcomes published therein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221062","collaboration":"Prepared in cooperation with the U.S Fish and Wildlife Service","usgsCitation":"Douglas, D.C., and Atwood, T.C., 2022, Comparisons of Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) sea-ice projections in polar bear (Ursus maritimus) ecoregions during the 21st century: U.S. Geological Survey Open-File Report 2022–1062, 27 p., https://doi.org/10.3133/ofr20221062.","productDescription":"vii, 27 p.","onlineOnly":"Y","ipdsId":"IP-139269","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":403336,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221062/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1062"},{"id":403335,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.XML"},{"id":403334,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1062/images"},{"id":403333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1062"},{"id":403332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1062/coverthb.jpg"}],"contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).
","language":"English","publisher":"Copernicus","doi":"10.5194/essd-14-3115-2022","usgsCitation":"McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S., Peters-Lidard, C., and Verdin, J., 2022, A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan: Earth System Science Data, v. 14, no. 7, p. 3115-3135, https://doi.org/10.5194/essd-14-3115-2022.","productDescription":"21 p.","startPage":"3115","endPage":"3135","ipdsId":"IP-134002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-3115-2022","text":"Publisher Index 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mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901829,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kumar, Sujay","contributorId":337039,"corporation":false,"usgs":false,"family":"Kumar","given":"Sujay","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901830,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Peters-Lidard, Christa","contributorId":337041,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901831,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verdin, James","contributorId":337042,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":901832,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70233613,"text":"70233613 - 2022 - Mercury contamination and potential health risks to Arctic seabirds and shorebirds","interactions":[],"lastModifiedDate":"2022-07-27T11:59:12.514773","indexId":"70233613","displayToPublicDate":"2022-07-08T06:54:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mercury contamination and potential health risks to Arctic seabirds and shorebirds","docAbstract":"Since the last Arctic Monitoring and Assessment Programme (AMAP) effort to review biological effects of mercury (Hg) on Arctic biota in 2011 and 2018, there has been a considerable number of new Arctic bird studies. This review article provides contemporary Hg exposure and potential health risk for 36 Arctic seabird and shorebird species, representing a larger portion of the Arctic than during previous AMAP assessments now also including parts of the Russian Arctic. To assess risk to birds, we used Hg toxicity benchmarks established for blood and converted to egg, liver, and feather tissues. Several Arctic seabird populations showed Hg concentrations that exceeded toxicity benchmarks, with 50 % of individual birds exceeding the “no adverse health effect” level. In particular, 5 % of all studied birds were considered to be at moderate or higher risk to Hg toxicity. However, most seabirds (95 %) were generally at lower risk to Hg toxicity. The highest Hg contamination was observed in seabirds breeding in the western Atlantic and Pacific Oceans. Most Arctic shorebirds exhibited low Hg concentrations, with approximately 45 % of individuals categorized at no risk, 2.5 % at high risk category, and no individual at severe risk. Although the majority Arctic-breeding seabirds and shorebirds appeared at lower risk to Hg toxicity, recent studies have reported deleterious effects of Hg on some pituitary hormones, genotoxicity, and reproductive performance. Adult survival appeared unaffected by Hg exposure, although long-term banding studies incorporating Hg are still limited. Although Hg contamination across the Arctic is considered low for most bird species, Hg in combination with other stressors, including other contaminants, diseases, parasites, and climate change, may still cause adverse effects. Future investigations on the global impact of Hg on Arctic birds should be conducted within a multi-stressor framework. This information helps to address Article 22 (Effectiveness Evaluation) of the Minamata Convention on Mercury as a global pollutant.
In early October of 2020, the U.S. Geological Survey (USGS) held a virtual workshop to discuss Gulf and Atlantic Coastal Plains site-response models. Earthquake researchers came together to assess (1) research related to proposed Coastal Plains amplification models and (2) USGS plans for implementing these models. Presentations spanned a broad range of topics from Atlantic and Gulf Coastal Plains geophysical properties including seismic velocity and attenuation, to ground motion amplification models and their impacts on seismic hazard. Interspersed with these presentations were discussions regarding the definition and extent of the Atlantic and Gulf Coastal Plains, potential complexities of wave propagation in the Atlantic and Gulf Coastal Plains, and problems that need to be overcome to implement various proposed site-response models. Based on feedback from this workshop, the USGS working group on Coastal Plain Amplification is considering applying published models that depend on sediment thickness. The working group is also exploring potential application of models that depend on the length of path traversed across the Coastal Plain, including the Gulf Coastal Plain ground-motion model adjustments from the Next Generation Attenuation Relationships for the Eastern United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221006","usgsCitation":"Boyd, O.S., Pratt, T.L., Chapman, M.C., Shumway, A., Rezaeian, S., Moschetti, M.P., and Petersen, M.D., 2022, U.S. Geological Survey coastal plain amplification virtual workshop: U.S. Geological Survey Open-File Report 2022–1006, 25 p., https://doi.org/10.3133/ofr20221006.","productDescription":"vi, 25 p.","onlineOnly":"Y","ipdsId":"IP-128818","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.pdf","text":"Report","size":"849 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1006"},{"id":402498,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.xml"},{"id":402496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1006/coverthb.jpg"},{"id":405344,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1006/images"},{"id":405560,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221006/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1006"}],"country":"United States","otherGeospatial":"Gulf and Atlantic Coast Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.716796875,\n 25.522614647623293\n ],\n [\n -81.03515625,\n 31.541089879585808\n ],\n [\n -75.5419921875,\n 35.35321610123823\n ],\n [\n -73.95996093749999,\n 40.38002840251183\n ],\n [\n -69.5654296875,\n 41.57436130598913\n ],\n [\n -70.48828125,\n 42.19596877629178\n ],\n [\n -70.224609375,\n 43.51668853502906\n ],\n [\n -66.97265625,\n 44.62175409623324\n ],\n [\n -68.2470703125,\n 45.920587344733654\n ],\n [\n -71.630859375,\n 43.45291889355465\n ],\n [\n -76.025390625,\n 40.613952441166596\n ],\n [\n -78.31054687499999,\n 38.54816542304656\n ],\n [\n -81.4306640625,\n 33.578014746143985\n ],\n [\n -83.056640625,\n 30.86451022625836\n ],\n [\n -92.98828125,\n 30.977609093348686\n ],\n [\n -96.328125,\n 30.29701788337205\n ],\n [\n -98.4375,\n 28.57487404744697\n ],\n [\n -98.0859375,\n 26.470573022375085\n ],\n [\n -97.20703125,\n 25.997549919572112\n ],\n [\n -96.9873046875,\n 27.371767300523047\n ],\n [\n -95.5810546875,\n 28.65203063036226\n ],\n [\n -93.8232421875,\n 29.305561325527698\n ],\n [\n -91.40625,\n 29.19053283229458\n ],\n [\n -89.9560546875,\n 28.8831596093235\n ],\n [\n -88.9892578125,\n 29.84064389983441\n ],\n [\n -86.17675781249999,\n 30.031055426540206\n ],\n [\n -84.111328125,\n 29.49698759653577\n ],\n [\n -83.1005859375,\n 28.76765910569123\n ],\n [\n -82.529296875,\n 26.82407078047018\n ],\n [\n -81.123046875,\n 25.045792240303445\n ],\n [\n -80.5078125,\n 24.726874870506972\n ],\n [\n -79.716796875,\n 25.522614647623293\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 966
Denver, CO 80225
The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale.
We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion.
Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%).
Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00299-x","usgsCitation":"Weller, F.G., Beatty, W.S., Webb, E.B., Kesler, D.C., Krementz, D.G., Asante, K., and Naylor, L.W., 2022, Environmental drivers of autumn migration departure decisions in midcontinental mallards: Movement Ecology, v. 10, 1, 13 p., https://doi.org/10.1186/s40462-021-00299-x.","productDescription":"1, 13 p.","ipdsId":"IP-132818","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447212,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00299-x","text":"Publisher Index Page"},{"id":432679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341013,"corporation":false,"usgs":false,"family":"Weller","given":"Florian","email":"","middleInitial":"G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kesler, Dylan C.","contributorId":216845,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","email":"","middleInitial":"C.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":908795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908796,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Asante, Kwasi","contributorId":59632,"corporation":false,"usgs":true,"family":"Asante","given":"Kwasi","email":"","affiliations":[],"preferred":false,"id":908797,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Naylor, Luke W.","contributorId":145840,"corporation":false,"usgs":false,"family":"Naylor","given":"Luke","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":908798,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70241515,"text":"70241515 - 2022 - Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data","interactions":[],"lastModifiedDate":"2023-03-22T12:15:44.376645","indexId":"70241515","displayToPublicDate":"2022-07-05T07:09:52","publicationYear":"2022","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 small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data","docAbstract":"Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.
Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ufug.2022.127649","usgsCitation":"Coville, R.C., Kruegler, J., Selbig, W.R., Hirabayashi, S., Loheid, S., Avery, W., Shuster, W., Haefner, R.J., Scharenbroch, B.C., Endreny, T.A., and Nowak, D., 2022, Loss of street trees predicted to cause 6000 L/tree increase in leaf-on stormwater runoff for Great Lakes urban sewershed: Urban Forestry & Urban Greening, v. 74, 127649, 11 p., https://doi.org/10.1016/j.ufug.2022.127649.","productDescription":"127649, 11 p.","ipdsId":"IP-133935","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447219,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ufug.2022.127649","text":"Publisher Index Page"},{"id":412277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.50874870835374,\n 43.82183481554111\n ],\n [\n -88.50874870835374,\n 43.736154677156634\n ],\n [\n -88.38456095868207,\n 43.736154677156634\n ],\n [\n -88.38456095868207,\n 43.82183481554111\n ],\n [\n -88.50874870835374,\n 43.82183481554111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"74","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coville, Robert C. 0000-0002-6895-2564","orcid":"https://orcid.org/0000-0002-6895-2564","contributorId":269851,"corporation":false,"usgs":false,"family":"Coville","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":862263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kruegler, James 0000-0002-2671-0807","orcid":"https://orcid.org/0000-0002-2671-0807","contributorId":269853,"corporation":false,"usgs":false,"family":"Kruegler","given":"James","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":862264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirabayashi, Satoshi","contributorId":301162,"corporation":false,"usgs":false,"family":"Hirabayashi","given":"Satoshi","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":862266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loheid, Stephen","contributorId":301163,"corporation":false,"usgs":false,"family":"Loheid","given":"Stephen","email":"","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":862267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Avery, William 0000-0002-2651-9906","orcid":"https://orcid.org/0000-0002-2651-9906","contributorId":269858,"corporation":false,"usgs":false,"family":"Avery","given":"William","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - 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Stevens Point","active":true,"usgs":false}],"preferred":false,"id":862271,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Endreny, Theodore A.","contributorId":195489,"corporation":false,"usgs":false,"family":"Endreny","given":"Theodore","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":862272,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nowak, Dave","contributorId":301166,"corporation":false,"usgs":false,"family":"Nowak","given":"Dave","email":"","affiliations":[{"id":35159,"text":"USDS Forest Service","active":true,"usgs":false}],"preferred":false,"id":862273,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70262385,"text":"70262385 - 2022 - Choosing an optimal duck season: Integrating hunter values and duck abundance","interactions":[],"lastModifiedDate":"2025-01-21T16:02:10.870812","indexId":"70262385","displayToPublicDate":"2022-07-05T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Choosing an optimal duck season: Integrating hunter values and duck abundance","docAbstract":"State wildlife agencies have long struggled to identify optimal hunting season dates for migratory game bird species that meet the diverse and often competing interests of stakeholders. Many approaches have been used to ensure the regulated community participates in the decision-making process, including public hearings, hunter season-date preference surveys, and hunter task forces or committees. Although hearings, surveys, and task force approaches include portions of the regulated community (typically the most avid) they may not necessarily reflect the opinions and values of all stakeholders. Additionally, these approaches rely heavily on limited anecdotal observations that may be unduly influenced by hunter avidity (e.g., days spent afield), hunter density, species preferences, and property access. To address the challenges caused by engaging only portions of the stakeholder community, we used a structured decision-making framework that included a 2017 duck hunter survey to elucidate values of a representative sample of the regulated community in each waterfowl hunting zone in the state of New York, USA. Rather than asking duck hunters about their specific duck hunting season date preferences, we asked them to rank 6 objectives describing what they value in their hunting experience (e.g., maximizing the opportunity to see mallards [Anas platyrhynchos] and black ducks [Anas rubripes], maximizing the number of weekend days). Four of the 6 objectives described duck species availability (i.e., abundance or immigration) and the remaining 2 described considerations that affect an individual hunter's opportunity or limitation to going duck hunting (i.e., holidays or conflicting hunting seasons). We used spatiotemporal abundance models derived from eBird citizen science data to estimate abundance and immigration rates of ducks in each waterfowl zone. We evaluated up to 9 unique season date alternatives developed by duck hunter task forces to determine which season date alternative best satisfied the competing objectives of duck hunters in each zone. The approach we developed allowed for selection of optimal duck hunting season dates and successfully involved avid duck hunters in the regulation development stages, while ensuring that the values of a representative sample of all stakeholders were directly considered through a clear and transparent decision-making process.
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