Australia's National Recreational Fishing Conference 2017
Evaluation of a smart-phone application to collect recreational fishing catch estimates, including an assessment against an independent probability based survey, using South Australia as a case study
There is need to collect accurate and robust information on recreational fishing levels to inform fisheries management. Recreational fishing estimates are critical to ensure sustainable harvest of community owned fisheries resources. In South Australia, established Fishery Management Plans are in place and recreational catch must be monitored to ensure that the sector is operating within its allocated shares of the resource. Recent changes to the availability of traditional sampling frames and shifts in the way the people communicate mean that it is becoming increasingly cost-prohibitive to undertake surveys using the established methodology. There is a need to re-evaluate how recreational fishing catch and effort is assessed and to develop revised survey methodologies using the latest survey tools and techniques.
Smartphone applications may provide a cost-effective method to collect information on recreational catch. However, as most apps are self-selected, sampling is non-probability based and it is not possible to calculate confidence intervals or margins of error. There is a need to compare app-based data with traditional phone-diary surveys to compare estimates and evaluate the accuracy of the results measured relative to independent population benchmarks. The outputs from this project will provide valuable information to other jurisdictions who have existing apps or are looking to implement an app.- based survey
Final report
The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.
National Recreational Fishing Conference bursary 2019
Trials of oceanographic data collection on commercial fishing vessels in SE Australia
Australia’s fisheries span a large area of ocean. Australia has the world’s third largest Exclusive Economic Zone (EEZ), with an area of over 8 million km2. This zone contains mainly Commonwealth managed fisheries, with State jurisdictions mainly in coastal waters up to the 3 nautical mile limit. Australia's total wild-catch fisheries gross value of production is $1.6 billion, of which 28% is from Commonwealth fisheries and 72% from the smaller coastal inshore fisheries managed by state jurisdictions. The wildcatch fisheries sector employs about 10,000 people across Australia (https://www.awe.gov.au/abares/research-topics/fisheries/fisheries-and-aquaculture-statistics/employment).
The commercial fishing industry has a network of thousands of vessels working mainly in inshore waters around Australia. They can supply a potential platform for extensive and fine scale spatial and temporal monitoring of the waters of the continental shelf (0-1200m), from the surface to the ocean floor. Given that their livelihoods depend on it, they have a keen understanding of oceanographic conditions with respect to fish behaviour, feeding and spawning and the various oceanographic factors that may influence this. In some fisheries (e.g. surface tuna longlining), fishers eagerly seek and use readily available fine-scale oceanographic data such as sea surface temperature and sea level, to improve their targeting and achieve higher resultant catch rates. For many other fisheries, however, it is the fine-scale sub-surface oceanographic conditions (feed layers, thermoclines, temperature at depth etc) that have a critical influence on their fishing dynamics. Unfortunately, this type of oceanographic data is far less readily available. Although fishers and scientists know these factors are important, the time series of fine scale spatial and temporal data relevant to fishery operations is not available to include in stock assessments. As a result, it is often assumed that variations in catch rates reflect changing stock abundance, when it may simply be a result of changing oceanographic conditions.
Marine scientists collect a vast range of oceanographic data using satellites, subsurface drones, and static and drifting buoys. Sea surface data, however, is much easier and more cost-effective to collect at high spatial and temporal resolutions than sub-surface data. Hence, understanding of sub-surface oceanographic conditions tends to be derived from modelling more than actual measurement. This may be sufficient at a wide-scale global or continental level, but it is not adequate at the fine-scale spatial and temporal resolution required for fisheries management.
The use of commercial fishing gear as a research data platform has been increasing in popularity internationally (https://www.frontiersin.org/articles/10.3389/fmars.2020.485512/full). A number of groups in Europe have been doing this for a decade (e.g Martinelli et al 2016), and New Zealand are also now involved (https://www.moanaproject.org/te-tiro-moana). However, this approach has yet to be implemented in Australia in a coordinated way. In particular, our approach dictates open access data served through the IMOS Australian Ocean Data Network (www.aodn.org.au) that can be collected once and used many times.
In this project we intend to instrument seafood sector assets (e.g Trawl Nets, longlines, pots) with fit-for- purpose quality-controlled (QC'd) temperature/pressure sensors to increase the sub-surface temperature data coverage around Australia’s shelf and upper slope regions (0-800m) at low cost. Not only will this assist in the collection of data at relevant spatial and temporal scales for use by fishers, but it will also provide a far more extensive level of QC’d data to oceanographers in near real time (NRT) for evaluation and ingestion into data-assimilating coastal models that will provide improved analysis and forecasts of oceanic conditions. In turn, this will also be of value to the fishing sector when used to standardise stock assessments.
Martinelli, M., Guicciardi, S., Penna, P., Belardinelli, A., Croci, C., Domenichetti, F., et al. (2016). Evaluation of the oceanographic measurement accuracy of different commercial sensors to be used on fishing gears. Ocean Eng. 111, 22–33. doi: 10.1016/J.OCEANENG.2015.10.037