Developing Recommendations for Undertaking Cpue Standardisation Using Observer Program Data

Abstract

Abundance indices based on nominal CPUE do not take into account confounding factors such as fishing strategy and environmental conditions, that can decouple any underlying abundance signal in the catch rate. As such, the assumption that CPUE is proportional to abundance is frequently violated. CPUE standardisation is one of the common analyses applied. The aims of this paper were to provide a statistical modelling framework for conducting CPUE standardisations using the Observer Program data for bigeye tuna, yellowfin tuna, albacore and southern bluefin tuna, and provide a comparison in the trends between the nominal CPUEs and their standardised indices obtained. The CPUE standardisations were conducted on the Observer Program collected between 2005 and 2007, by applying GLM analysis using the Tweedie distribution. The results suggested that year, area, HBF and bait factors significantly influenced the nominal CPUEs for the four tuna species of interest. Some extreme peaks and troughs in the nominal time series were smoothed in the standardised CPUE time series. The high degree of temporal variability that is still shown in the standardised CPUE trends suggests that the data are too sparse to give any meaningful indication of proxy abundance. Nevertheless, this may also suggest that variables used in the GLMs do not sufficiently account for all of the confounding factors, or abundance may indeed be truly variable.

STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

“…The result is expected to support ALB management study in Eastern Indian Ocean and update the information which was previously reported by Sadiyah et al (2012).…”

“…Temporal trends of nominal CPUEs were much influenced by different factors which associated with fishing practice and environmental condition (Sadiyah et al, 2012). The different factor such as time of fishing (year), season, fishing area and hook between float (HBF) can cause an extreme value in nominal CPUE time series.…”

“…Su et al (2008) was used GLM, GAM and delta approachment to analysis bigeye tuna CPUE standardization for Taiwan tuna longline fisheries. Sadiyah et al (2012) applied the GLM method to develop recommendation of CPUE standardization based on Indonesian tuna longline observer data.…”

“…The result from Sadiyah et al (2012) suggested that some significant confounding factor for CPUE standardization with GLM model were year, fishing area, and HBF. In this study, we added another confounding factor i. e the period of season (west monsoon and east monsoon).…”

The Effect of Depth of Hooks, Set and Soak Time to the Catch Per Unit of Effort of Tuna in the Eastern Indian Ocean

“…Aside of operational factors used in this analysis, Sadiyah et al (2012) reported that year, area and bait factors significantly influenced the nominal CPUEs of tuna known as technological creep. However, this study did not discuss about those factors and mainly focused on operational aspects related to the CPUE.…”