Using machine learning to understand the implications of meteorological conditions for fish kills Chen, You‑Jia, Nicholson, Emily and Cheng, You‑Jia 2020, Using machine learning to understand the implications of meteorological conditions for fish kills, Scientific Reports, vol. 10, no. 1, pp. 1-13, doi: 10.1038/s41598-020-73922-3. Attached Files Name Description MIMEType Size Downloads Title Using machine learning to understand the implications of meteorological conditions for fish kills Author(s) Chen, You‑Jia Nicholson, Emily orcid.org/0000-0003-2199-3446 Cheng, You‑Jia Journal name Scientific Reports Volume number 10 Issue number 1 Article ID 17003 Start page 1 End page 13 Total pages 13 Publisher Springer Science and Business Media LLC Place of publication London, Eng. Publication date 2020 ISSN 2045-2322 2045-2322 Summary Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet Mugil cephalus using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2–3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems Language eng DOI 10.1038/s41598-020-73922-3 Indigenous content off HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30144437 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Life and Environmental Sciences Open Access Collection Centre for Integrative Ecology Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 26 Oct 2020, 07:14:51 EST Tue, 27 Oct 2020, 04:01:39 EST Tue, 27 Oct 2020, 07:04:11 EST Wed, 04 Nov 2020, 08:53:35 EST Thu, 05 Nov 2020, 05:03:55 EST Thu, 05 Nov 2020, 11:07:40 EST Thu, 05 Nov 2020, 11:10:02 EST Thu, 05 Nov 2020, 13:28:25 EST Sat, 14 Nov 2020, 01:32:51 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 36 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 04 Nov 2020, 08:53:35 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.