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1. Response to Comments on “Designing river flows to improve food security futures in the Lower Mekong Basin”

   https://doi.org/10.1126/science.aat1477  

   Holtgrieve, GW ( July 2018 , Science)

   Sabo et al. presented an empirically derived algorithm defining the socioecological response of the Tonle Sap Dai fishery in the Cambodian Mekong to basin-scale variation in hydrologic flow regime. Williams suggests that the analysis leading to the algorithm is flawed because of the large distance between the gauge used to measure water levels (hydrology) and the site of harvest for the fishery. Halls and Moyle argue that Sabo et al.’s findings are well known and contend that the algorithm is not a comprehensive assessment of sustainability. We argue that Williams’ critique stems from a misunderstanding about our analysis; further clarification of the analysis is provided. We regret not citing more of the work indicated by Halls and Moyle, yet we note that our empirical analysis provides additional new insights into Mekong flow-fishery relationships. 

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2. Simulation of REE mobility and evolution of F-NaCl-CO2-bearing fluids in hydrothermal calcite and fluorite ore-forming veins

   https://doi.org/10.7185/gold2021.6309  

   Costa Filho, D. ; Gysi, A.P. ( July 2021 , Goldschmidt)

   Rare earth element (REE) deposits are commonly associated with carbonatites and (per)alkaline rocks where hydrothermal magmatic fluids can play a significant role in REE mobilization and deposition [1]. Thermodynamic modeling permits predicting the evolution of ore-forming fluids and can be used to test different controls on hydrothermal REE mobility including temperature, pressure, the solubility of REE minerals, aqueous REE speciation and pH evolution associated with fluid-rock interaction. Previous modeling studies either focused on REE fluoride/chloride complexation in acidic aqueous fluids [2] or near neutral/alkaline fluids associated with calcite vein formation [3]. Such models were also applied to interpret field observations in REE deposits Bayan Obo in China and Bear Lodge in Wyoming [3,4]. Recent hydrothermal calcite-fluid REE partitioning experiments provide new data to simulate the solubility of REE in calcite, REE carbonates/fluorocarbonates at high temperatures [5, 6]. We studied the competing effects controlling the mobility of REE in hydrothermal fluids between 100 and 400 °C at 500 bar. Speciation calculations were carried out in the Ca-F-CO2-Na-Cl-H2O system using the GEMS code package [7]. The properties of minerals and aqueous species were taken from the MINES thermodynamic database [3,5]. The Gallinas Mountains hydrothermal REE deposit in New Mexico was used as a field analogue to compare our models with the formation of calcite-fluorite veins hosting bastnäsite. Previous fluid inclusion studies hypothesized that the REE were transported as fluoride complexes [8] but more recent modeling studies have shown that fluoride essentially acts as a depositing ligand [2]. Here we show more detailed simulations predicting the stability of fluorite, calcite and REE minerals relevant to ore-forming processes in carbonatites and alkaline systems. [1] Gysi et al. (2016), Econ. Geol. 111, 1241-1276; [2] Migdisov and Williams-Jones (2014), Mineral. Deposita 49, 987-997. [3] Perry and Gysi (2018), Geofluids; [4] Liu et al. (2020), Minerals 10, 495; [5] Perry and Gysi (2020), Geochim. Cosmochim. Acta 286, 177-197; [6] Gysi and Williams-Jones (2015) Chem. Geol. 392, 87-101;[7] Kulik et al. (2013), Computat. Geosci. 17, 1-24; [8] Williams-Jones et al. (2000), Econ. Geol. 95, 327-341 

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3. Geomorphological Map of the Soi Crater Region on Titan

   https://doi.org/10.1029/2022JE007499  

   Schoenfeld, A. M. ; Solomonidou, A. ; Malaska, M. J. ; Lopes, R. M. C. ; Birch, S. P. D. ; Le Mouélic, S. ; Florence, M. ; Verlander, T. ; Wall, S. D. ; Elachi, C. ( January 2023 , Journal of Geophysical Research: Planets)

   We mapped the Soi crater region at 1:800,000 scale and produced a geomorphological map using methodology presented by Malaska, Lopes, Williams, et al. (2016),https://doi.org/10.1016/j.icarus.2016.02.021and Schoenfeld et al. (2021),https://doi.org/10.1016/j.icarus.2021.114516. This region spans longitude 110° to 180°W and latitude 0° to 60°N and is representative of the transition between the equatorial, mid‐latitude, and high‐latitude northern regions of Titan. We used Cassini Synthetic Aperture Radar (SAR) as our primary mapping data set. For areas where SAR was not available, we used lower resolution data from the Imaging Science Subsystem, the Visible and Infrared Mapping Spectrometer, radiometry, and high‐altitude SAR for complete mapping coverage of the region. We identified 22 geomorphological units, 3 of which have been discussed in existing literature but have not yet been incorporated into our mapping investigations. These include sharp‐edged depressions (bse), ramparts (brh), and bright gradational plains (pgh). All six major terrain classes are represented in this region: Craters, Labyrinth, Hummocky/mountainous, Plains, Dunes, and Basin and Lakes. We find that plains dominate the surface of the Soi crater region, comprising ∼73% of the mapped area, followed by dunes (∼14%), mountains/hummocky terrains (∼12%), basin and lakes (∼0.7%), labyrinth terrains (∼0.5%), and crater terrains (∼0.4%). We also observe empty lakes as far south as 40°N. The Soi crater region largely has the same collection and proportion of geomorphological units to other mapped regions on Titan. These results further support the hypothesis that surface processes are, broadly speaking, the same across Titan's middle and equatorial latitudes, with the exception of Xanadu.

    

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4. Behavioral differences: insights, explanations and comparisons of French and US Twitter usage during elections

   https://doi.org/10.1007/s13278-019-0611-9  

   Ian Davidson, Antoine Gourru ( January 2020 , Social network analysis and mining)

   Social networks and social media have played a key role for observing and influencing how the political landscape takes shape and dynamically shifts. It is especially true in events such as national elections as indicated by earlier studies with Facebook (Williams and Gulati, in: Proceedings of the annual meeting of the American Political Science Association, 2009) and Twitter (Larsson and Moe in New Med Soc 14(5):729–747, 2012). Not surprisingly in an attempt to better understand and simplify these networks, community discovery methods have been used, such as the Louvain method (Blondel et al. in J Stat Mechanics Theory Exp 2008(10):P10008, 2008) to understand elections (Gaumont et al. in PLoS ONE 13(9):e0201879, 2018). However, most community-based studies first simplify the complex Twitter data into a single network based on (for example) follower, retweet or friendship properties. This requires ignoring some information or combining many types of information into a graph, which can mask many insights. In this paper, we explore Twitter data as a time-stamped vertex- labeled graph. The graph structure can be given by a structural relation between the users such as retweet, friendship or fol- lower relation, whilst the behavior of the individual is given by their posting behavior which is modeled as a time-evolving vertex labels. We explore leveraging existing community discovery methods to find communities using just the structural data and then describe these communities using behavioral data. We explore two complimentary directions: (1) creating a taxonomy of hashtags based on their community usage and (2) efficiently describing the communities expanding our recently published work. We have created two datasets, one each for the French and US elections from which we compare and contrast insights on the usage of hashtags. 

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5. Florida mangrove saltmarsh reference surface soils

   https://doi.org/10.6073/pasta/0e08cbe07c84488cb7b9dd16669946d0  

   Lewis, David Bruce ( January 2021 , Environmental Data Initiative)

   Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm. 

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