Aims Banksia attenuata is a resprouting species growing in deep sand, while B. sessilis is a fire-killed species occurring in shallow sand over laterite or limestone. We aimed to discover the ecophysiological basis for their different distributions by exploring their investment in deep non-cluster roots and shallow cluster roots, and their cluster-root functioning.Methods Deep-pot (1 m), shallow-pot (400 mm), hydroponic experiments and phosphorus (P)-extraction experiment were carried out. Biomass allocation, clusterroot exudation, plant P and leaf manganese (Mn) concentrations were measured.Results Banksia attenuata allocated more biomass to deep roots and less biomass to cluster roots than B. sessilis did in deep pots. The two Banksias released similar Revised version without track changes Click here to access/download;Revised version without track changes;Banksia manuscript 20190126 revision clean.docx carboxylates in all experiments, with similar carboxylate-exudation rates in hydroponics. The carboxylate amount per unit cluster root of B. sessilis grown in shallow pots was greater than that of B. attenuata, and B, sessilis acquired more P than B. attenuata did in limestone substrate. Conclusions Greater investment in deep roots for water uptake accounts for the presence of B. attenuata in deep sand, and vice versa for the absence of B. sessilis. A larger investment in cluster roots, which released greater amounts of carboxylates, likely accounts for B. sessilis occurring over limestone. Trade-offs in investment and cluster-root functioning support the species' distribution patterns and life histories.Leaf Mn concentration was a good proxy for the plant capacity to acquire P.

Abstract-The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as Automated Rendezvous and Docking, AR&D). The crewed versions may also perform AR&D, possibly with a different level of automation and/or autonomy, and must also provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program.NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the CEV requirements. The relatively low technology readiness of relative navigation sensors for AR&D has been carried as one of the CEV Projects top risks. The AR&D Sensor Technology Project seeks to reduce this risk by increasing technology maturation of selected relative navigation sensor technologies through testing and simulation, and to allow the CEV Project to assess the relative navigation sensors.The first year of this project was focused on a series of "pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes. The second year of the project will use the information and data collected from the "pathfinder" testing to evaluate the Contractor-proposed sensors. Four highly applicable candidate sensor were identified for the "pathfinder" activities: the Johnson Space Center's (JSCs) Automatic Targeting and Reflective Alignment Concept (AutoTRAC) Computer Vision System (ACVS), which is a camera-based system that uses reflectors on the target vehicle; JSCs Natural Feature Image Recognition (NFIR), which is a camera-based system that does not require reflectors; Marshall Space Flight Center's (MSFCs) Advanced Video Guidance Sensor (AVGS), which is a laser-based system that uses reflectors on the target vehicle; and the Optech Lidar, which is a laser-based system that produces range and intensity data, provided by the Jet Propulsion Laboratory (JPL) for this task.Sensor characterization and testing for each of these four sensors was conducted at the MSFC Flight Robotics Laboratory (FRL) using the FRL 6-DOF gantry system, called the Dynamic Overhead Target Simulator (DOTS).The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking systems, with added retroreflectors for the pathfinder sensors and a standoff cross target for visual recognition by the NFIR sensor.The sensors were tested using four categories of open-loop test trajectories: (1) sensor characterization trajectories designed to test a wide range of performance parameters, (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles, (3) lighting tests...

Abstract. Water vapor is an important component in the water and energy cycle of the Arctic. Especially in the light of Arctic amplification, changes of water vapor are of high interest but are difficult to observe due to the data sparsity of the region. The ACLOUD/PASCAL campaign performed in May/June 2017 in the Arctic North Atlantic sector offers the opportunity to investigate the quality of various satellite and reanalysis products. Compared to reference measurements at R/V Polarstern frozen into the ice (around 82° N, 10° E) and at Ny-Ålesund, the Integrated Water Vapor (IWV) from IASI shows the best performance among all satellite products. Using all radiosonde stations within the region indicates some differences that might relate to different radiosonde types used. Though the region is well sampled by polar orbiting satellites daily means can deviate by up to 50 % due to strong spatio-temporal IWV variability associated with atmospheric river events. For monthly mean values, this weather induced variability cancels out but systematic differences dominate which particularly appear over different surface types, e.g. ocean, sea ice. In the data sparse central Arctic above 84° N, strong differences of 30 % in IWV monthly means between satellite products occur in the month of June which likely results from the difficulties to consider the complex and changing surface characteristics of the melting ice within the retrieval algorithms. There is hope that the detailed surface characterization performed as part of the recently finished Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) will foster the improvement of future retrieval algorithms.

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