Cold-water conditions have excluded durophagous (skeleton-breaking) predators from the Antarctic seafloor for millions of years. Rapidly warming seas off the western Antarctic Peninsula could now facilitate their return to the continental shelf, with profound consequences for the endemic fauna. Among the likely first arrivals are king crabs (Lithodidae), which were discovered recently on the adjacent continental slope. During the austral summer of 2010‒2011, we used underwater imagery to survey a slope-dwelling population of the lithodid Paralomis birsteini off Marguerite Bay, western Antarctic Peninsula for environmental or trophic impediments to shoreward expansion. The population density averaged ∼4.5 individuals × 1,000 m−2 within a depth range of 1,100‒1,500 m (overall observed depth range 841–2,266 m). Images of juveniles, discarded molts, and precopulatory behavior, as well as gravid females in a trapping study, suggested a reproductively viable population on the slope. At the time of the survey, there was no thermal barrier to prevent the lithodids from expanding upward and emerging on the outer shelf (400- to 550-m depth); however, near-surface temperatures remained too cold for them to survive in inner-shelf and coastal environments (<200 m). Ambient salinity, composition of the substrate, and the depth distribution of potential predators likewise indicated no barriers to expansion of lithodids onto the outer shelf. Primary food resources for lithodids—echinoderms and mollusks—were abundant on the upper slope (550–800 m) and outer shelf. As sea temperatures continue to rise, lithodids will likely play an increasingly important role in the trophic structure of subtidal communities closer to shore.

Abstract. Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions using proxies for iodide such as nitrate and sea-surface temperature. This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (random forest regression) to generate a high-resolution (0.125∘×0.125∘, ∼12.5km×12.5km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (1967–2018) that has a 45 % larger sample size than has been used previously as the dependent variable and co-located ancillary parameters (temperature, nitrate, phosphate, salinity, shortwave radiation, topographic depth, mixed layer depth, and chlorophyll a) from global climatologies as the independent variables. We investigate the regression models generated using different combinations of ancillary parameters and select the 10 best-performing models to be included in an ensemble prediction. We then use this ensemble of models, combined with global fields of the ancillary parameters, to predict new high-resolution monthly global sea-surface iodide fields representing the present day. Sea-surface temperature is the most important variable in all 10 models. We estimate a global average sea-surface iodide concentration of 106 nM (with an uncertainty of ∼20 %), which is within the range of previous estimates (60–130 nM). Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40 % on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. We envisage these fields could be used to represent present-day sea-surface iodide concentrations, in applications such as climate and air-quality modelling. The global iodide dataset is made freely available to the community (https://doi.org/10/gfv5v3, Sherwen et al., 2019), and as new observations are made, we will update the global dataset through a “living data” model.

Rural Aging in America: Proceedings of the 2017 Connectivity SummitAlexis Skoufalos, EdD, Janice L. Clarke, RN, BBA, Dana Rose Ellis, BA, Vicki L. Shepard, MSW, MPA, and Elizabeth Y. Rula, PhDEditorial: Creating a Movement to Transform Rural Aging David B. Nash, MD, MBA, with Donato J. Tramuto, and Joseph F. Coughlin, PhD   S-3Introduction   S-4Summit Proceedings   S-5  Roundtable 1: The Power of Community – Enabling Social Connections and Access to Health Resources Through Community-Based Programs   S-5  Roundtable 2: Technology and Rural Health: Innovative Solutions to Bridge the Distance, Improve Care, and Deliver Programs   S-7  Roundtable 3: An Integrated Experience: The Exponential Potential of a Collaborative Approach to Rural Aging   S-8General Discussion and Recommendations   S-8Post-Summit Debriefing   S-9  Strategy and objectives   S-9  6–12 month action plan   S-9Conclusion   S-9

18Whale falls provide a substantial, nutrient-rich resource for species in areas of the ocean that may 19 otherwise be largely devoid of food. We report the discovery of a natural whale fall at 1430 m 20 depth in the cold waters of the continental slope off the western Antarctic Peninsula. This is the 21 highest latitude whale fall reported to date. The section of the carcass we observed-the tail 22 fluke-was more complete than any previously reported natural whale fall from the deep sea and

In this paper we present a new speaker-separation algorithm for separating signals with known statistical characteristics from mixed multi-channel recordings. Speaker separation has conventionally been treated as a problem of Blind Source Separation (BSS). This approach does not utilize any knowledge ofthe statistical characteristics of the signals to be separated, relying mainly on the independence between the various signals to separate them. The algorithm presented in this paper, on the other hand, utilizer detailed statistical information about the signals to he separated, represented in the form of hidden Markov mndels (HMM). We treat the signal separation problem as one of beam-forming, where each signal is extracted using a filter-and-sum may. The filters are estimated to maximize the likelihood of the summed output, measured on the HMM for the desired signal. This is done by iteratively estimating the best state sequence through the HMM from a factorial HMM (FHMM), that is the cross-product of the HMMs for the multiple signals, using the current output of the array, and estimating the filters to maximize the likelihood of that state sequence. Experiments show that the proposed method can cleanly extract a background speaker who is 20dB below the foreground speaker in a two-speaker mixture, when the HMMs for the signals are constructed from knowledge of the utterance transcriptions.