Assessing Data-poor Fisheries: Training for Indonesian Fisheries

10 Sep 2015
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Data is a necessary component of fisheries management, providing information to managers on what management measures might be required to keep the stocks at a healthy level. In the ideal situation, there would be a long time-series of data, from both fisheries-dependent and -independent sources and covering various parameters, such as length and weight, abundance, maturity, mortality, gonad data, otolith samples, fishing ground, gear, season, trip duration, species composition, catch volume, discard rate, a high sampling coverage for the area of interest, etc. The list is endless! Such data is then used for stock assessments using a statistical assessment model, and the current status of a stock is assessed against predefined and agreed limit and target reference points (limit reference point is a level at or below which the stock should not fall as there may be a serious risk to the reproductive capacity of the stock. A target reference point is a level which the stock should be kept at or above to ensure reproductive capability). However, in the real-world, the availability of data is often limited, especially for small-scale fisheries in developing countries where systematic data reporting frameworks are yet to be established and there may only be a small amount of data collected. For example, the data collection system of MDPI collects length and weight data on all tuna species >10kg from small-scale handline sites across eastern Indonesia, and is continuing to expand. In such cases, when there is a limited availability of data, how to inform management about the status of the stock and make decisions? How do fisheries scientists and managers assess such data-poor fisheries?

One method, developed by scientists at Murdoch University in Australia, is the Length-Based Spawning Potential Ratio Model (LB-SPR). The LB-SPR model uses life history and size composition data to assess a stock. The Spawning Potential Ratio (SPR) is the proportion of unfished reproductive potential that remains after fishing. The SPR will have a value between 0 and 1; the closer the value is to 1, the higher the reproductive capacity of the stock. Life history data includes parameters such as Natural Mortality (M), Length at fist maturity (Lm), etc. The low data requirements for the model are an advantage and size composition (length data) can be easy to collect and at a low cost. There are a number of assumptions associated with the LB-SPR model, for example the length sample represents the population, length data is available for males and for females, etc (for more detailed information on the model and assumptions please refer to Hordyk et al. 2015 and www.whatsthecatch.murdoch.eddu.au).

To support Indonesian capacity on assessing data poor fisheries, a training workshop was held in Bogor, 24-28th August, organised by Murdoch University, CSIRO and IPB. The trainers were Adrian Hordyk, Neil Loneragan, Vanessa Jaiteh, and Craig Proctor. During this training week, 38 participants from various organisations (NGOs, academia, government) were introduced to the LB-SPR model, the assumptions, data requirements, and R statistical software (www.rstudio.com). Initial exercises used datasets for whiting and blue morwong. Later, the participants were split into species groups, each group using the LB-SPR model with participant data (data for lobsters, sharks, tuna, blue swimming crab, etc.). The groups were tasked with investigating how the LB-SPR model performed with the different species data and reporting findings to the wider participants. The training week did not focus only on the use of R software and LB-SPR models. Participants were also invited to practice communicating the results in different formats. Each group presented their data on a three slide presentation, receiving feedback for future improvement. An extended abstract will be submitted to the organising committee from each group, and we were asked to think about what additional data and analysis would be required to upgrade the extended abstract into a full-length journal article.

Participants gained immensely from this training course. Indonesian fisheries are typically data-poor and the LB-SPR model is highly relevant in this context. Specifically for MDPI, the training week highlighted how our data can be analysed and presented to support and inform wider national management activities, such as activities related to Harvest Control Rules. This training workshop is applicable to many Indonesian fisheries, providing valuable tools to support the management of the data-poor fisheries of Indonesia.

Reference:

Hordyk, A., Loneragan, N. and Prince, J. 2015. An evaluation of a harvest strategy for data-poor fisheries using the length-based spawning potential ratio assessment methodology. Fisheries Research

Writer: Deirdre Duggan

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