Physics Maths Engineering
Harry Gorfine,
Harry Gorfine
Institution: Victorian Fisheries Authority, Queenscliff, Australia
Email: hgorfine@unimelb.edu.au
Justin Bell,
Justin Bell
Institution: Victorian Fisheries Authority, Queenscliff, Australia
Email: info@rnfinity.com
Michael Cleland,
Khageswor Giri
Peer Reviewed
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].
Show by month | Manuscript | Video Summary |
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2024 November | 38 | 38 |
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 | 354 | 354 |
Show by month | Manuscript | Video Summary |
---|---|---|
2024 November | 38 | 38 |
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 | 354 | 354 |