Approaches to the reconstrution of catches of Indian Ocea blue shark (prionace glauca)

Catch histories form an important component of stock assessments and so having a reliable and believable catch series is a key part in gauging the level of stock depletion. In data-limited situations, reported nominal catches are often not considered reliable and so reconstruction of catch histories plays an important role. The first Indian Ocean stock assessment of blue shark took place in 2015, however, due to the amount of uncertainty in the assessments, the conclusion regarding stock status remained as uncertain. The historic catch series was considered to be one of the key sources of uncertainty and so the Working Party requested that participants develop new approaches to reconstructing historic catches to be used as alternate series for assessment. This paper uses the available nominal catch data currently held in the IOTC database and explores the use of a disaggregation method followed by a ratio based method and a GAM statistical approach to reconstructing historic blue shark catches in the Indian Ocean.
The methods described in this paper attempt to account for two key sources of error in reported catches: (i) not reporting to species, and (ii) not reporting at all. A rule-based method to identify proxy fleets was used to disaggregate reports of ‘sharks NEI’ to address the limited reporting to species level, while ratio and GAM based models using target catches were used to predict the expected catches where there are zero reported catches. The ratio based method was based on the disaggregated catches while the GAM method was based on the IOTC nominal catches. The two resulting estimated catch series were very similar with catches increasing over the time period of the fishery, reaching approximately 50-60,000 t in recent years. However the GAM series produced higher estimated catches in early years and was still increasing at the end of the time period (2015) while the ratio estimates based on the disaggregated catches followed the disaggregated catch trend more closely and peaked in 2011. While a range of approaches have been explored, if a preferred catch series is to be used as an alternative series for the assessment, then it is recommended that the GAM estimated catch is used.