We analysed the patterns of variation that
characterize 33 catch time series of large pelagic fishes exploited by the
Japanese and Taiwanese longline fisheries in the Indian Ocean from 1968 to
2003. We selected four species, the yellowfin (Thunnus albacares), the bigeye (T. obesus), the
albacore (T. alalunga), and the swordfish (Xiphias gladius) and aggregated data into five
biogeographic provinces of Longhurst (2001). We carried out wavelet
analyses, an efficient method to study non-stationary time series, in order
to get the time-scale patterns of each signals. We then compared and grouped
the different wavelet spectra using a multivariate analysis to identify the
factors (species, province or fleet) that may influence their clustering. We
also investigated the associations between catch time series and a
large-scale climatic index, the Dipole Mode Index (DMI), using cross wavelet
analyses. Our results evidenced that the geographical province is more
important than the species level when analyzing the 33 catch time series in
the tropical Indian Ocean. The DMI further impacted the variability of tuna
and swordfish catch time series at several periodic bands and at different
temporal locations, and we demonstrated that the geographic locations
modulated its impact. We discussed the consistency of time series
fluctuations that reflect embedded information and complex interactions
between biological processes, fishing strategies and environmental
variability at different scales.