ORCID Profile
0000-0002-2463-3414
Current Organisations
NSW Office of Environment and Heritage
,
Macquarie University
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Publisher: Springer Science and Business Media LLC
Date: 21-10-2014
DOI: 10.1038/S41559-019-0999-7
Abstract: Research into the microbiomes of natural environments is changing the way ecologists and evolutionary biologists view the importance of microorganisms in ecosystem function. This is particularly relevant in ocean environments, where microorganisms constitute the majority of biomass and control most of the major biogeochemical cycles, including those that regulate Earth's climate. Coastal marine environments provide goods and services that are imperative to human survival and well-being (for ex le, fisheries and water purification), and emerging evidence indicates that these ecosystem services often depend on complex relationships between communities of microorganisms (the 'microbiome') and the environment or their hosts - termed the 'holobiont'. Understanding of coastal ecosystem function must therefore be framed under the holobiont concept, whereby macroorganisms and their associated microbiomes are considered as a synergistic ecological unit. Here, we evaluate the current state of knowledge on coastal marine microbiome research and identify key questions within this growing research area. Although the list of questions is broad and ambitious, progress in the field is increasing exponentially, and the emergence of large, international collaborative networks and well-executed manipulative experiments are rapidly advancing the field of coastal marine microbiome research.
Publisher: Copernicus GmbH
Date: 02-09-2019
Abstract: Abstract. Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and assumptions that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point- and path-s ling instruments. The Bayesian framework is designed to account for uncertainty in the parameterisations of measurements, the meteorological data, and the atmospheric model itself when performing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. From all the inversions we conducted across the different instrument groups and release-rate periods, our worst-case median emission rate estimate was within 36 % of the true emission rate. Further, in the worst case, the closest limit of the 95 % credible interval to the true emission rate was within 11 % of this true value.
Publisher: Copernicus GmbH
Date: 23-04-2019
DOI: 10.5194/AMT-2019-124
Abstract: Abstract. Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and misspecifications that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point- and path-s ling instruments. The Bayesian framework is designed to account for uncertainty in the parametrisations of measurements, the meteorological data, and the atmospheric model itself when doing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. The worst median emission-rate estimate we obtain from all the inversions is within 36 % of the true value, while the worst posterior 95 % credible interval has a limit within 11 % of the true value.
Publisher: Elsevier BV
Date: 09-2019
No related grants have been discovered for Karita Negandhi.