The proposed study will produce information about the supply-chain economic contribution of selected small scale fisheries in Western Australia, as well as a method that can be applied to making these estimates for other fisheries.

The proposed approach includes making three separate estimates of the supply-chain contribution of selected fisheries using three different levels of information: (1 – minimal cost) published fishery production data and a regional economic model that describes inter-regional flows using published statistical data, (2 – minimum consultation) the same as 1 but also including a workshop with key stakeholders to inform the assumptions, and (3 – maximum data) the same as 2 but also collecting primary data from businesses along the supply-chain. As such, the estimates produced for the selected fisheries using approach 3 will be of high quality and the recommended method described in the guidelines will be informed by a comparison of the cost and performance of undertaking the analysis using each level of information.

Three approaches to obtain supply chain data will be utilised and compared in this project. Below summarises the set of data sources within each approach.

Minimal cost approach
Fishery production statistics: Published production statistics (State of the Fisheries 2020/21*) or data request to DPIRD
Fishery financials and employment: Published profiles (if available) or matched fisheries (as per 2017-210)
Supply-chain flows: Analysis of regional input-output tables, taken as given
Supply-chain financials and employment: Analysis of regional input-output tables

Minimal consultation approach
Fishery production statistics: Published production statistics (State of the Fisheries 2020/21*) or data request to DPIRD
Fishery financials and employment: Published profiles (if available) or matched fisheries (as per 2017-210)
Supply-chain flows: Estimated by workshop with stakeholders, starting from input-output table estimates
Supply-chain financials and employment: Analysis of regional input-output tables

Maximum data approach
Fishery production statistics: Published production statistics (State of the Fisheries 2020/21*) or data request to DPIRD
Fishery financials and employment: Primary data
Supply-chain flows: Primary data
Supply-chain financials and employment: Primary data

* Newman, S.J., Wise, B.S., Santoro, K.G. and Gaughan, D.J. (eds) 2021, Status Reports of the Fisheries and Aquatic Resources of Western Australia 2020/21: The State of the Fisheries, Department of Primary Industries and Regional Development, Western Australia.

Key to estimating the supply-chain flows for the low-data approaches, we will apply the direct coefficients from input-output tables within our RISE models to the value of each fishery to estimate the value of each node of the supply chain for each fishery. The input-output model tells us for each dollar of sales from the fishing sector there are sales to other sectors (i.e. processing, wholesale trade, retail trade, food services). Subsequently, those sectors also have sales to other sectors. By applying these coefficients in sequence we can estimate the value of each node of the supply chain. Data for input-output models are held by BDO and ultimately sourced from publications by ABS, RBA, ATO and other public organisations.

Under the minimal consultation approach, we will refine the supply-chain map developed under the minimal cost approach by eliciting judgement by key stakeholders. These stakeholders will have an idea of the size of the supply chain nodes and will be able to confirm or adjust the values according to their knowledge.

Under the maximum data approach, we will interview businesses along the supply chain in order to collect data to value each supply chain node. We will elicit the value of each business and the number and size of businesses at each node. Information on the number and approximate size of businesses at each supply chain node will be sought from industry participants at the workshop. Contact details for businesses willing to be involved in the interview process with also be sought at the workshop.

Abundance estimates are used both directly and indirectly in stock assessment processes to support fishery management. Australia’s fisheries research agencies all estimate fish population abundance in some way. These include genetic and conventional tagging, acoustics (active and passive), trawl and egg surveys, as well as using proxies of abundance such as catch. Each of these methods have benefits, biases and caveats linked to the method and to the fish species being assessed. For example, differences between life history and habitat can make an abundance estimation method that has worked for one species unsuitable for another. As the application of each method of estimating abundance is potentially species/scenario specific, potential use by researchers and managers can be fraught.

In developing or proposing an abundance estimate for use in fisheries assessment, researchers must have a clear understanding of the assessment framework in order to make sure that an abundance estimate can be used. Claims such as “this time series can then be used in stock assessment” must be verified by funding agencies (particularly beyond FRDC) and defensible. Proliferation of abundance estimation methods without links to the assessment process will not yield an expected benefit beyond knowledge accumulation.

A project is needed to capture the range of methods of estimating abundance for management purposes, and specify the conditions of use, limitations and readiness level for operational use. A decision tree and methods ‘toolbox’ that describes the techniques, their relative strengths and weaknesses will help researchers and managers identify the best suited abundance estimate approach, and guide research effort to overcome known weaknesses.

The development of a ‘toolbox’ of techniques would be used to inform:
1. techniques available to estimate abundance
2. suitability of them to different conditions such as life history, and data availability
3. requirements of the technique such as methods used, prerequisite expertise, data and cost; and
4. circumstances under which the technique can be used.
This project would also identify potential new approaches and technologies that might complement or replace current ones.