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Physics Maths Engineering

Fishery independent survey datasets of abalone populations on subtidal coastal reefs in southeastern Australia

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Harry Gorfine,

Harry Gorfine

Victorian Fisheries Authority, Queenscliff, Australia

hgorfine@unimelb.edu.au


Justin Bell,

Justin Bell

Victorian Fisheries Authority, Queenscliff, Australia

info@rnfinity.com


Michael Cleland,

Michael Cleland

RightIntoIT, Tinbeerwah, Australia

info@rnfinity.com


Khageswor Giri

Khageswor Giri

Bundoora AgriBio Centre, Australia

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  Peer Reviewed

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© attribution CC-BY

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811 Views

Added on

2023-04-24

Doi: https://doi.org/10.1016/j.dib.2023.109095

Abstract

Assessing the status or exploited marine fish populations often relies on fishery dependent catch and effort data reported by licensed commercial fishers in compliance with regulations and by recreational anglers voluntarily. This invariably leads to bias towards the fraction of a fish population or community that can be legally fished i.e., the stock as defined by legal minimum lengths and spatial boundaries. Data are restricted to populations which continue to be exploited at the expense of obtaining data on previously exploited and unexploited populations [1,2], so if a fishery is contracting spatially over time, then successively less of the overall fish community is monitored with bias towards where biomass is highest or most accessible [3]. A viable alternative is to conduct population monitoring surveys independently of a fishery to obtain information that is more broadly representative of the abundance, composition and size structure of fish communities and their supporting habitats [4–6]. Whereas catch and effort data often must be de-identified and aggregated to protect the confidentiality of fishers’ commercial and personal interests, this constraint does not exist for independently acquired monitoring data, collected at public expense and hence publicly available at high levels of spatial and temporal resolution. Time series underpins the utility of fishery independent survey (FIS) datasets in terms of the life histories of exploited fish species and the time frames of their responses to various combinations of fishing mortality and environmental fluctuations and trends [7]. One-off surveys can establish a baseline and spatial distribution pattern, but regular surveys conducted consistently over time are necessary to detect trends from which population status can be inferred. We present several unique datasets focused on the commercially valuable blacklip abalone (Haliotis rubra), spanning three decades of annually collected data from up to 204 locations on subtidal rocky reefs along a coastline of almost 2500 km, the State of Victoria, Australia. It is rare for data to be collected consistently at this intensity over such a long period of monitoring [2], especially with surveys conducted by small teams of highly skilled research divers, some of whom up until recently had participated in every year. The data comprises ∼28,000 records from ∼4500 site surveys conducted during 1992 to 2021 [2]. Although the fixed site design remained unchanged, the number of sites surveyed varied over time, mostly increasing in number periodically, and the survey method was refined on several occasions. We defined three different variants in the survey method due to technological advancement for both enumerating abalone abundance and measuring shell size structure [7]. The relative abundance counts were standardized using a Bayesian generalized linear mixed model (GLMM) to test for interannual trends whilst allowing for inherent differences among sites, research divers, and their interactions [8].

Key Questions

What is the purpose of the dataset presented in this study?

The dataset aims to facilitate research in leak detection and localization within water distribution systems by providing a diverse set of sensory measurements under controlled laboratory conditions.

What types of sensors were used to collect the data?

The data were gathered using accelerometers, hydrophones, and dynamic pressure sensors, each capturing different aspects of the water distribution system's behavior during leak and no-leak conditions.

What leak types and network configurations are included in the dataset?

The dataset includes four leak types: orifice leak, longitudinal crack, circumferential crack, and gasket leak. It also covers two network topologies: looped and branched configurations.

How can researchers utilize this dataset?

Researchers can use the dataset to develop and validate algorithms for leak detection and localization, simulate various leak scenarios, and enhance the reliability of water distribution systems by improving monitoring techniques.

What makes this dataset unique compared to previous studies?

This dataset is distinctive due to its comprehensive nature, including multiple sensor types, various leak scenarios, different network configurations, and the inclusion of background noise and demand variations, providing a realistic and versatile resource for research.

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ARTICLE USAGE


Article usage: Apr-2023 to Jun-2025
Show by month Manuscript Video Summary
2025 June 78 78
2025 May 93 93
2025 April 63 63
2025 March 73 73
2025 February 50 50
2025 January 46 46
2024 December 40 40
2024 November 52 52
2024 October 64 64
2024 September 103 103
2024 August 33 33
2024 July 36 36
2024 June 26 26
2024 May 27 27
2024 April 22 22
2024 March 5 5
Total 811 811
Show by month Manuscript Video Summary
2025 June 78 78
2025 May 93 93
2025 April 63 63
2025 March 73 73
2025 February 50 50
2025 January 46 46
2024 December 40 40
2024 November 52 52
2024 October 64 64
2024 September 103 103
2024 August 33 33
2024 July 36 36
2024 June 26 26
2024 May 27 27
2024 April 22 22
2024 March 5 5
Total 811 811
Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology
copyright icon

© attribution CC-BY

  • 0

rating
811 Views

Added on

2023-04-24

Doi: https://doi.org/10.1016/j.dib.2023.109095

Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology

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