Light thresholds to prevent dredging impacts on the great barrier reef seagrass, Zostera muelleri ssp. capricorni

© 2016 Chartrand, Bryant, Carter, Ralph and Rasheed. Coastal seagrass habitats are at risk from a range of anthropogenic activities that modify the natural light environment, including dredging activities associated with coastal and port developments. On Australia's east coast, the tropical seagrass Zostera muelleri ssp. capricorni dominates intertidal mudbanks in sheltered embayments which are also preferred locations for harbors and port facilities. Dredging to establish and maintain shipping channels in these areas can degrade water quality and diminish light conditions that are required for seagrass growth. Based on this potential conflict, we simulated in-situ light attenuation events to measure effects on Z. muelleri ssp. capricorni condition. Semi-annual in situ shading studies conducted over 3 years were used to quantify the impact of prolonged light reduction on seagrass morphometrics (biomass, percent cover, and shoot density). Experimental manipulations were complimented with an assessment of 46 months of light history and concurrent natural seagrass change at the study site in Gladstone Harbour. There was a clear light-dependent effect on seagrass morphometrics during seagrass growing seasons, but no effect during senescent periods. Significant seagrass declines occurred between 4 and 8 weeks after shading during the growing seasons with light maintained in the range of 4-5 mol photons m-2 d-1. Sensitivity to shading declined when applied in 2-week intervals (fortnightly) rather than continuous over the same period. Field observations were correlated to manipulative experiments to derive an applied threshold of 6 mol photons m-2 d-1 which formed the basis of a reactive light-based management strategy which has been successfully implemented to ensure positive ecological outcomes for seagrass during a large-scale dredging program

Off-the-Grid Line Spectrum Denoising and Estimation with Multiple Measurement Vectors

Compressed Sensing suggests that the required number of samples for reconstructing a signal can be greatly reduced if it is sparse in a known discrete basis, yet many real-world signals are sparse in a continuous dictionary. One example is the spectrally-sparse signal, which is composed of a small number of spectral atoms with arbitrary frequencies on the unit interval. In this paper we study the problem of line spectrum denoising and estimation with an ensemble of spectrally-sparse signals composed of the same set of continuous-valued frequencies from their partial and noisy observations. Two approaches are developed based on atomic norm minimization and structured covariance estimation, both of which can be solved efficiently via semidefinite programming. The first approach aims to estimate and denoise the set of signals from their partial and noisy observations via atomic norm minimization, and recover the frequencies via examining the dual polynomial of the convex program. We characterize the optimality condition of the proposed algorithm and derive the expected convergence rate for denoising, demonstrating the benefit of including multiple measurement vectors. The second approach aims to recover the population covariance matrix from the partially observed sample covariance matrix by motivating its low-rank Toeplitz structure without recovering the signal ensemble. Performance guarantee is derived with a finite number of measurement vectors. The frequencies can be recovered via conventional spectrum estimation methods such as MUSIC from the estimated covariance matrix. Finally, numerical examples are provided to validate the favorable performance of the proposed algorithms, with comparisons against several existing approaches.Comment: 14 pages, 10 figure

Simonsenia aveniformis sp nov (Bacillariophyceae), molecular phylogeny and systematics of the genus, and a new type of canal raphe system

The genus Simonsenia is reviewed and S. aveniformis described as new for science by light and electron microscopy. The new species originated from estuarine environments in southern Iberia (Atlantic coast) and was isolated into culture. In LM, Simonsenia resembles Nitzschia, with bridges (fibulae) beneath the raphe, which is marginal. It is only electron microscope (EM) examination that reveals the true structure of the raphe system, which consists of a raphe canal raised on a keel (wing), supported by rib like braces (fenestral bars) and tube-like portulae; between the portulae the keel is perforated by open windows (fenestrae). Based on the presence of portulae and a fenestrated keel, Simonsenia has been proposed to be intermediate between Bacillariaceae and Surirellaceae. However, an rbcL phylogeny revealed that Simonsenia belongs firmly in the Bacillariaceae, with which it shares a similar chloroplast arrangement, rather than in the Surirellaceae. Lack of homology between the surirelloid and simonsenioid keels is reflected in subtle differences in the morphology and ontogeny of the portulae and fenestrae. The diversity of Simonsenia has probably been underestimated, particularly in the marine environment.Polish National Science Centre in Cracow within the Maestro program [N 2012/04/A/ST10/00544]; Sciences and Technologies Foundation-FCT (Portugal) [SFRH/BD/62405/2009]info:eu-repo/semantics/publishedVersio

New biocontrol opportunities for prickly acacia: exploration in India

Prickly acacia (Vachellia nilotica ssp. indica), a multipurpose tree native to the Indian subcontinent, is a Weed of National Significance and is widespread throughout the grazing areas of northern Australia. Biological control of V. nilotica ssp. indica has been in progress since the early 1980s, but with limited success to date. Based on genetic and climate matching studies, native surveys for potential biological control agents were conducted in India during 2008-2011. A total of 72 sites were surveyed in southern India and 60 sites in north-western India. Surveys yielded 33 species of phytophagous insects and two rust fungi. Based on host records, 20 insect species that are crop pests or polyphagous, and all plant pathogens other than the two rust fungi, were excluded from the list of potential biological control agents. Using field host range, geographic range, seasonal incidence, damage potential, and preliminary host-specificity test results in India, as filters, the following agents were prioritised for detailed host specificity tests: a scale insect (Anomalococcus indicus), two leaf-webbers (Phycita sp. A and Phycita sp. B), a leaf weevil (Dereodus denticollis), a leaf beetle (Pachnephorus sp.), one gall-inducing rust (Ravenelia acacia-arabicae) and a leaf rust (Ravenelia evansii). The two rusts were sent to CABI-UK for preliminary host-specificity testing. Import permits for the brown leaf-webber (Phycita sp. A), the green leaf-webber (Phycita sp. B), the scale insect (A. indicus), the leaf-weevil (D. denticollis) and the leaf-beetle (Pachnephorus sp.) have been obtained from relevant regulatory authorities in Australia. So far, 11 importations, containing several thousands of insects in total have been exported from India into a quarantine facility in Brisbane, Australia. Based on these importations, host specificity tests for the brown leaf-webber (Phycita sp. A) have been completed, and the tests for the scale insect (A. indicus) and the green leaf-webber (Phycita sp., B) are in progress. Additional importations of the leaf-weevil (D. denticollis) and the leaf-beetle (Pachnephorus sp.) are planned for later in the year, when conditions are more conducive for field collections

Impact of fast-converging PEVD algorithms on broadband AoA estimation

Polynomial matrix eigenvalue decomposition (PEVD) algorithms have been shown to enable a solution to the broadband angle of arrival (AoA) estimation problem. A parahermitian cross-spectral density (CSD) matrix can be generated from samples gathered by multiple array elements. The application of the PEVD to this CSD matrix leads to a paraunitary matrix which can be used within the spatio-spectral polynomial multiple signal classification (SSP-MUSIC) AoA estimation algorithm. Here, we demonstrate that the recent low-complexity divide-and-conquer sequential matrix diagonalisation (DC-SMD) algorithm, when paired with SSP-MUSIC, is able to provide superior AoA estimation versus traditional PEVD methods for the same algorithm execution time. We also provide results that quantify the performance trade-offs that DC-SMD offers for various algorithm parameters, and show that algorithm convergence speed can be increased at the expense of increased decomposition error and poorer AoA estimation performance