In Tasmania, CPUE has been found to provide valid information about stock status under some circumstances, i.e. when the fishery is performing very well and when it is performing very badly (Tarbath et al. 2005). Tasmanian abalone fishery assessment reports have shown that the east and west coasts typically have
different catch rates (Tarbath et al. 2005, 2007). However, because catch and effort data are usually collected at spatial scales much coarser than the spatial scale at which fishing occurs, simple summary catch rates from abalone fisheries do not necessarily represent what is happening at the spatial scale of stock distribution and are flawed as a fishery performance measure. For abalone, the key problem with using catch rate as a performance measure is that it is susceptible to hyperstability. Through behavioural changes, divers may maintain high catch rates while depleting a stock (Karpov et al. 2000). In addition, localised depletions would not be detectable using catch rates aggregated across the current management blocks (Prince 2005). CPUE is currently applied as an index of abalone availability in the Tasmanian abalone fishery at an inappropriate spatial scale.
Spatial measures of effort
In some fisheries, the measurement of effort used in calculating CPUE has a spatial component. In trawl fisheries, for example, effort is calculated as a product of time, speed and gear units (effectively the area fished in a unit of time) (Buckworth 1985, Bishop et al. 2004). There is no similar measure of volume, area or distance used to calculate effort in the Tasmanian abalone dive fishery. Anecdotal evidence suggests that such a measure is needed (Mundy 2006b). For example catch rate, can remain stable while the amount of area searched changes (Figure 10). If a diver needs to search an area almost three times as large as a previous year to maintain the same catch rate in the same location, and in the same number of hours, then although search area has increased, the catch rate (as catch per hour) remains the same and a decline in abalone density is not detected.
A summary of the problems
Some identified problems with the way that CPUE is calculated and applied as an index of abundance in the Tasmanian abalone fishery are:
a. Effort is measured in hours and is reported by a diver at the end of the day. These reports are often based on estimates and can be quite inaccurate. b. Effort and catch are pooled across divers for each day of diving, and
reported within large geographic areas, which obscure fine scale variation and changes in CPUE.
c. Effort is measured only as time and the amount of area searched by a diver is not taken into account, i.e. there is no spatial measure of effort.
Raw CPUE may never be a precise index of abalone abundance. However, in the absence of validated alternative performance measures, CPUE data will continue to be used to inform fishery management advice. Despite some limitations, CPUE is a valuable index to assess the abundance of abalone. Standardised fishery CPUE estimates have been shown to be more informative than non-standardised CPUE (Maunder and Punt 2004). However, where consistent effort is applied to a fishery, unstandardised CPUE may still be a useful measure (Tarbath et al. 2005). Assessments of abalone fisheries in New South Wales, Western Australia and South Australia also use CPUE data as performance indicators. (Worthington et al.
1998, Chick et al. 2008, Hart et al. 2009). If standardised CPUE estimates can be generated at fine spatial and temporal scales this may allow fishery managers to use fishery dependent data with much greater confidence.
3.1.4 Objectives
My objectives in this chapter are to address each of the three problems raised above using fine-scale data on the temporal and spatial distribution of fishing
Figure 10. An example of how changes in fishing behaviour may remain undetected using current CPUE measures, reconstructed from fisher anecdotes. A diver visiting the same location in two successive years (A and B) found that in the second year they had to search almost three times the area of the first year to take the same amount of catch. Because they were swimming faster, catch in Kg/Hr was the same.
activity (collection methods described in Chapter 2). Overall, this chapter aims to test the value of alternative measures of fishing effort developed from fine-scale information about divers’ behaviour 1) to complement traditional time-based estimates of CPUE, and 2) to capture the complexity of individual divers’ behaviour.
• Firstly, I test the value of accurately recording the duration of fishing for improving current estimates of daily CPUE (Catch/hour of fishing) to answer the following questions:
- Is there is a significant difference between estimates of fishing time as reported by divers and the duration of fishing events as recorded by data loggers?
- If so, does this error significantly affect daily time-based CPUE measures?
• Secondly, I use GPS-sampled vessel coordinates to i) record vessel movement, ii) use different GIS techniques to estimate the amount of area potentially searched by a diver while fishing, and iii) develop some spatial daily catch rates (i.e., distance-based rates expressed as catch/km of vessel track, or area-based expressed as catch/ha searched by a diver.
- Can these alternative indices (distance-based or area-based) of fishing effort provide original and useful information about fishing effort in complement to traditional time-based CPUE?
- Additionally, can distance-based and area-based indices of fishing effort capture differences in individual divers’ fishing behaviour?
• To illustrate the value of the proposed techniques, in the last section of this chapter, I apply fine-scale daily CPUE and spatial catch rates to a specific case study.