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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd
    Freshwater biology 50 (2005), S. 0 
    ISSN: 1365-2427
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: 1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial.2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS.3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r2, intercept and slope. Second, the two models’ assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites.4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd
    Freshwater biology 42 (1999), S. 0 
    ISSN: 1365-2427
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: 1. When using benthic macroinvertebrate communities for bioassessment, temporal variation may influence judgement as to whether or not a site is degraded.2. In a survey of sixteen reference and sixteen test sites in the upper Thames River catchment area (UTRCA) in south-western Ontario, Canada, consistent differences between summer and winter samples were found for taxon richness (increase; P = 0.06) and the Family Biotic Index (decrease; P = 0.11). A bioassessment based on these results would indicate better water quality in the same streams in winter relative to summer. No consistent pattern of seasonal difference was detected for Simpson’s Diversity and Equitability, or percentage Dominant Taxon.3. The Reference Condition Approach to bioassessment uses predictive modelling to explain variation in reference communities with the environmental conditions at these sites as predictors. The community at a test site is compared with that predicted by the model. Several predictive models were constructed using simple geographic and habitat characteristics (i.e. catchment area, distance to source, stream width, substrate and habitat diversity) as predictors. By including season of sampling in the models, we increased their predictive power and the ability of the bioassessment to detect degradation. The best results were achieved when separate predictive models were built for each sampling season.
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2022-02-18
    Description: Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 4
    Publication Date: 2022-06-09
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Parsons, M., Lin, T.-H., Mooney, T., Erbe, C., Juanes, F., Lammers, M., Li, S., Linke, S., Looby, A., Nedelec, S., Van Opzeeland, I., Radford, C., Rice, A., Sayigh, L., Stanley, J., Urban, E., & Di Iorio, L. Sounding the call for a global library of underwater biological sounds. Frontiers in Ecology and Evolution, 10, (2022): 810156, https://doi.org/10.3389/fevo.2022.810156.
    Description: Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.
    Description: Support for the initial author group to meet, discuss, and build consensus on the issues within this manuscript was provided by the Scientific Committee on Oceanic Research, Monmouth University Urban Coast Institute, and Rockefeller Program for the Human Environment. The U.S. National Science Foundation supported the publication of this article through Grant OCE-1840868 to the Scientific Committee on Oceanic Research.
    Keywords: soundscape ; bioacoustics database ; artificial intelligence ; biodiversity ; passive acoustic monitoring ; ecological informatics
    Repository Name: Woods Hole Open Access Server
    Type: Article
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