Evaluation of Three Harvest Control Rules for Bigeye Tuna(Thunnus obesus) Fisheries in the Indian Ocean

The stock of Bigeye tuna(Thunnus obesus) in the Indian Ocean supports an important international fishery and is considered to be fully exploited. The responsible management agency, the Indian Ocean Tuna Commission(IOTC), does not have an explicit management decision-making framework in place to prevent over-fishing. In this study, we evaluated three harvest control rules, i) constant fishing mortality(CF), from 0.2 to 0.6, ii) constant catch(CC), from 60000 to 140000 t, and iii) constant escapement(CE), from 0.3 to 0.7. The population dynamics simulated by the operating model was based on the most recent stock assessment using Stock Synthesis version Ⅲ(SS3). Three simulation scenarios(low, medium and high productivity) were designed to cover possible uncertainty in the stock assessment and biological parameters. Performances of three harvest control rules were compared on the basis of three management objectives(over 3, 10 and 25 years): i) the probability of maintaining spawning stock biomass above a level that can sustain maximum sustainable yield(MSY) on average, ii) the probability of achieving average catches between 0.8 MSY and 1.0 MSY, and iii) inter-annual variability in catches. The constant escapement strategy(CE=0.5), constant fishing mortality strategy(F=0.4) and constant catch(CC=80000) were the most rational among the respective management scenarios. It is concluded that the short-term annual catch is suggested at 80000 t, and the potential total allowable catch for a stable yield could be set at 120000 t once the stock had recovered successfully. All the strategies considered in this study to achieve a ‘tolerable' balance between resource conservation and utilization have been based around the management objectives of the IOTC.

The Potential Vertical Distribution of Bigeye Tuna (Thunnus obesus) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean

Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical distribution of this fish while foraging and determine the influences of the distribution on longline efficiency in the tropical Atlantic Ocean, the catch per unit effort(CPUE) data were compiled from the International Commission for the Conservation of Atlantic Tunas and the Argo buoy data were downloaded from the Argo data center. The raw Argo buoy data were processed by data mining methods. The CPUE was standardized by support vector machine before analysis. We assumed the depths with the upper and lower limits of the optimum water temperatures of 15℃ and 9℃ as the preferred swimming depth, while the lower limit of the temperature(12℃) associated with the highest hooking rate as the preferred foraging depth(D12) of bigeye tuna during the daytime in the Atlantic Ocean. The preferred swimming depth and foraging depth range in the daytime were assessed by plotting the isobath based on Argo buoy data. The preferred swimming depth and vertical structure of the water column were identified to investigate the spatial effects on the CPUE by using a generalized additive model(GAM). The empirical cumulative distribution function was used to assess the relationship between the spatial distribution of CPUE and the depth of 12℃ isolines and thermocline. The results indicate that 1) the preferred swimming depth of bigeye tuna in the tropical Atlantic is from 100 m to 400 m and displays spatial variation;2) the preferred foraging depth of bigeye tuna is between 190 and 300 m and below the thermocline;3) the number of CPUEs peaks at a relative depth of 30 –50 m(difference between the 12℃ isolines and the lower boundary of the thermocline);and 4) most CPUEs are within the lower depth boundary of the thermocline levels(LDBT) which is from 160 m to 230 m. GAM analysis indicates that the general relationship between the nominal CPUE and LDBT is characterized by a dome shape and peaks at approximately 190 m. The oceanographic features influence the habitat of tropical pelagic fish and fisheries. Argo buoy data can be an important tool to describe the habitat of oceanic fish. Our results provide new insights into how oceanographic features influence the habitat of tropical pelagic fish and fisheries and how fisheries exploit these fish using a new tool(Argo profile data).

Evaluation of FAD-associated purse seine fishery reduction strategies for bigeye tuna (Thunnus obesus) in the Indian Ocean

In the Indian Ocean, bigeye tuna supports one of the most important fisheries in the world. This fishery mainly consists of two components: longline and purse seine fisheries. Evidence of overfishing and stock depletion of bigeye tuna calls for an evaluation of alternative management strategies. Using an age-structured operating model, parameterize dwith the results derived in a recent stock assessment,we evaluated the effectiveness of applying constant fishing mortality (CF) and quasi-constant fishing mortality (QCF) strategies to reduce fishing effort of purse seining with fish aggregating devices (FADs) at different rates. Three different levels of productivity accounted for the uncertainty in our understanding of stock productivity. The study shows that the results of CF and QCF are similar. Average SSB and catchduring simulation years would be higher if fishing mortality of FAD-associated purse seiningwas reduced rapidly. The banning or rapid reduction of purse seining with FAD resulted in a mean catch, and catch in the last simulation year, higher than that of the base case in which no change was made to the purse seine fishery. This could be caused by growth overfishing by purse seine fisheries with FADs according to the per-recruit analysis. These differences would be more obvious when stock productivity was low. Transferring efforts of FAD-associated purse seining to longline fisheries is also not feasible.Our study suggests that changes are necessary to improve the performance of the current management strategy.