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      A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme

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          Abstract

          Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data generated from imagery includes identification, annotation and quantification of biological subjects and environmental features within an image. To be long-lived and useful beyond their project-specific initial purpose, and to maximize their utility across studies and disciplines, marine imagery data should use a standardised vocabulary of defined terms. This would enable the compilation of regional, national and/or global data sets from multiple sources, contributing to broad-scale management studies and development of automated annotation algorithms. The classification scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project provides such a vocabulary. The CATAMI classification scheme introduces Australian-wide acknowledged, standardised terminology for annotating benthic substrates and biota in marine imagery. It combines coarse-level taxonomy and morphology, and is a flexible, hierarchical classification that bridges the gap between habitat/biotope characterisation and taxonomy, acknowledging limitations when describing biological taxa through imagery. It is fully described, documented, and maintained through curated online databases, and can be applied across benthic image collection methods, annotation platforms and scoring methods. Following release in 2013, the CATAMI classification scheme was taken up by a wide variety of users, including government, academia and industry. This rapid acceptance highlights the scheme’s utility and the potential to facilitate broad-scale multidisciplinary studies of marine ecosystems when applied globally. Here we present the CATAMI classification scheme, describe its conception and features, and discuss its utility and the opportunities as well as challenges arising from its use.

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          Most cited references13

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          Ocean acidification and its potential effects on marine ecosystems.

          Ocean acidification is rapidly changing the carbonate system of the world oceans. Past mass extinction events have been linked to ocean acidification, and the current rate of change in seawater chemistry is unprecedented. Evidence suggests that these changes will have significant consequences for marine taxa, particularly those that build skeletons, shells, and tests of biogenic calcium carbonate. Potential changes in species distributions and abundances could propagate through multiple trophic levels of marine food webs, though research into the long-term ecosystem impacts of ocean acidification is in its infancy. This review attempts to provide a general synthesis of known and/or hypothesized biological and ecosystem responses to increasing ocean acidification. Marine taxa covered in this review include tropical reef-building corals, cold-water corals, crustose coralline algae, Halimeda, benthic mollusks, echinoderms, coccolithophores, foraminifera, pteropods, seagrasses, jellyfishes, and fishes. The risk of irreversible ecosystem changes due to ocean acidification should enlighten the ongoing CO(2) emissions debate and make it clear that the human dependence on fossil fuels must end quickly. Political will and significant large-scale investment in clean-energy technologies are essential if we are to avoid the most damaging effects of human-induced climate change, including ocean acidification.
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            Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation

            Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys.
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              Bayesian correlated clustering to integrate multiple datasets

              Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/. Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 October 2015
                2015
                : 10
                : 10
                : e0141039
                Affiliations
                [1 ]CSIRO Oceans & Atmosphere, Hobart, Tas, Australia
                [2 ]Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tas, Australia
                [3 ]School of Biological Sciences & Australian Centre for Field Robotics, The University of Sydney & Sydney Institute of Marine Science, Sydney, NSW, Australia
                [4 ]The Pawsey Supercomputing Centre / WAMSI, Mt Lawley, WA, Australia
                [5 ]Geoscience Australia, National Earth and Marine Observations Group, Canberra, ACT, Australia
                [6 ]The UWA Oceans Institute (M096), Crawley, WA, Australia
                [7 ]Australian Institute of Marine Science, The UWA Oceans Institute (M096), 39 Fairway, Crawley, WA, 6009, Australia
                [8 ]NSW Department of Industries, Nelson Bay, NSW, Australia
                [9 ]Western Australian Museum, Welshpool, Australia
                University of Waikato (National Institute of Water and Atmospheric Research), NEW ZEALAND
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Wrote the paper: FA NH RF LE RP CHLS RSS NB GE JC MT TR. Contributed to design of classification according to their relevant expertise: FA NH RF RP CHLS RSS NB GE JC MT AJ KGH. Incorporated classification into CAAB: TR KGH.

                ‡ These authors made smaller but essential contributions to this work.

                Article
                PONE-D-15-36087
                10.1371/journal.pone.0141039
                4625050
                26509918
                a6c86e78-b49c-4ffe-8701-c70289152d5c
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 17 August 2015
                : 27 August 2015
                Page count
                Figures: 2, Tables: 1, Pages: 18
                Funding
                CATAMI acknowledges funding from the following: the NeCTAR project ( http://www.nectar.org.au), an Australian Government project conducted as part of the Super Science initiative and financed by the Education Investment Fund; the Australian National Data Service (ANDS) ( http://www.ands.org.au/index.html); and the Australia Government’s National Environmental Research Program (NERP), Marine Biodiversity Hub ( http://www.nerpmarine.edu.au/). The Marine Biodiversity Hub is supported through funding from the Australian Government’s National Environmental Research Program (NERP), administered by the Department of Sustainability, Environment, Water, Population and Communities (DSEWPaC).
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                All relevant data are within the paper and its Supporting Information files.

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