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In the present study, swordfish (Xiphius gladius) catch in numbers and effort (hooks) data from ‘Catch data sheet of FSI large longline fishing vessels operating in FAO area of 51 and 57 from 2007-2019’ were analyzed together. These survey data comprise daily geo-referenced fishing positions (latitude and longitude), year, quarters, soaking time (time duration), species number and fishing effort (number of hooks) and catch rate of other coexisting species. Nominal hooking rate was calculated as the number of individuals captured per 100 hooks.
Due to the large percentage of zero swordfish catch in the survey data, the hooking rate (HR) of sword, as the number of fish caught per 100 hooks, was standardized using GLM in R approach with a delta lognormal approximation. The presence/absence and abundance (CPUE) of swordfish were modeled separately. The variables used in the model take into account spatial and temporal variations as well as the abundance of coexisting species. In total, 3056 fishing operations were carried out between 2007 and 2019 of which 1274 and 1782 operations in the FAO area of 57 and 51 respectively.
Abundance indices for swordfish (Xiphius gladius) for the period 2007-2019 were estimated using data obtained through oceanic longline surveys conducted by the of FSI large longline fishing vessels. Indices were calculated both for Western Indian Ocean and Eastern Indian Ocean together. Individual longline set catch per unit effort data, collected by scientists, were analyzed to assess effects of factors such as year, quarters, soaking time, latitude, longitude and abundance of coexisting species such as yellow fin tuna, sailfish, marlins and skipjacks besides remotely sensed chlorophyll –a and sea surface temperature derived from satellite imagery.
The main effects considered were temporal (year, quarters), spatial (longitude, latitude) and coexisting species i.e. hooking rate of skipjack tuna, marlins and skipjacks, besides remotely sensed variables chlorophyll a and sea surface temperature in the model. The results suggested that spatial (longitude & latitude), season (year and quarters) and soaking time significantly influenced the nominal hooking rate of swordfish whereas, coexisting species factors and remotely sensed variables particularly surface temperature and chlorophyll turned out to be insignificant and eventually dropped from the model. The high degree of temporal variability that is still shown in the standardized CPUE trends to suggest that the variables used in the GLM in R do not sufficiently account for all of the confounding factors, or the abundance may indeed be truly variable.
The principal goal of the study was to select the best model that could be used for subsequent prediction of swordfish abundance. The nominal hooking rate of swordfish showed a strong inter-annual fluctuation. However, this variability was reduced in the standardized hooking rate series. This indicated that the standardization process removed certain variability attributes to the explanatory variables. In this study, despite the environmental effects not being included in the model for standardization, our study provides useful information for the swordfish because the analysis based on long time series data which cover a considerable range of western Indian Ocean and Eastern Indian Ocean.
