The silky shark, Carcharhinus falciformis is one of the most heavily exploited sharks, being the main by-catch species in both tuna longline and purse-seine fisheries in tropical waters worldwide. Despite this severe exploitation, little is known about the species’ life history and population status. Silky sharks, like many other sharks, exhibit slow growth and low fecundity, indicating the urgency of developing assessment studies to aid in the implementation of conservation plans for their stocks. Because information on the catch and effort of this species is scarce, some length-based data-limited methods were applied in the present study to provide estimates of the status of the tropical Pacific silky shark population. As evident from the LBSPR analysis, the current spawning potential ratio (SPR) was found to be below the target reference point of SPR 40% and slightly above the limit reference point of SPR 20%. In addition, the LBB model also confirmed that this stock’s status is overfished with relatively low biomass levels. Furthermore, both models showed estimates of size selectivity at 50% and 95% that were lower than the estimated size at sexual maturity. In conclusion, the data-limited models developed in this study indicated that the silky shark stock in the tropical Pacific Ocean may be at risk of further decline. Additionally, the results show that growth and recruitment overfishing may be occurring in the silky shark’s population calling for immediate intensification of monitoring programs for these sharks as a pre-requisite to develop efficient management and conservation plans in the Pacific Ocean.

CMSY++, an improved version of the CMSY approach developed from Catch-MSY which uses a Bayesian implementation of a modified Schaefer model and can predict stock status and exploitation, was used in the present study. Evaluating relative performance is vital in situations when dealing with fisheries with different catch time series start years and biological prior information. To identify the influences of data inputs on CMSY++ outputs, this paper evaluated the use of a nominal reported catch and a reconstructed catch dataset of the South Atlantic blue shark alongside different priors of the blue shark’s productivity/resilience (r) coupled with different indices of abundance. Results from the present study showed that different catch time series start years did not have a significant influence on the estimation of the biomass and fishing reference points reported by CMSY++. However, uninformative priors of r affected the output results of the model. The developed model runs with varying and joint abundance indices showed conflicting results, as classification rates in the final year changed with respect to the type of index used. However, the model runs indicated that South Atlantic blue shark stock could be overfished (B2020/Bmsy = 0.623 to 1.15) and that overfishing could be occurring (F2020/Fmsy = 0.818 to 1.78). This result is consistent with the results from a previous assessment using a state-space surplus production model applied for the same stock in 2015. Though some potential could be observed when using CMSY++, the results from this model ought to be taken with caution. Additionally, the continuous development of prior information useful for this model would help strengthen its performance.

The pelagic stingray (Pteroplatytrygon violacea), perhaps the only stingray to inhabit open ocean waters, is highly interactive with longline and purse seine fisheries. The threat to P. violacea posed by high bycatch mortality has received widespread attention. To date, the environmental preference of P. violacea, which is important in designing conservation and management measures, has not been well studied. Based on data collected during a 2016–2019 survey in the Pacific Ocean by national observers of tuna longline fisheries, the relationship between the presence of P. violacea and spatiotemporal and environmental variables was first analyzed using the Generalized Additive Model. The results showed that geographic location (latitude and longitude) was the most influential variable. Monthly, P. violacea is frequently present in the Pacific high sea from December to May. The El Niño–Southern Oscillation had a significant impact on the presence of P. violacea in the Pacific high sea, with both the cold (Ocean Nino Index <−0.5) and warm (Ocean Nino Index >1) phases leading to a decrease in its presence. Regarding the environmental factors, we found that high presence was associated with low salinity (33.0~34.5 psu), a relatively high concentration of chlorophyll (0.2–0.35 mg/m³), and warm water (>20 °C). P. violacea was most likely observed in the waters offshore, closer to seamounts, and with water depths between 4000 and 5000 m. Four areas, including those east of the Solomon Islands and east of Kiribati, areas west of the Galapagos Islands, and areas near the coastal upwelling of northern Peru, related to upwelling systems or seamounts, were identified as the potential key habitats of P. violacea. Predicted distribution maps showed a significant seasonal variation in the presence of P. violacea. Moreover, the yearly change in the presence of P. violacea in the Pacific high sea indicated a possible decreasing trend in recent years. The information first provided here is essential for developing conservation and management measures for P. violacea to prevent the unavoidable ecological consequences of bycatch or other anthropogenic factors.

Fisheries researchers have focused on the value of information (VOI) in fisheries management and trade-offs since scientists and managers realized that information from different resources has different contribution in the management process. We picked seven indicators, which are log-normal annual catch observation error (Cobs), annual catch observation bias (Cbias), log-normal annual index observation error (Iobs), maximum length observation bias (Linfbias), observed natural mortality rate bias (Mbias), observed von Bertalanffy growth parameter K bias (Kbias), and catch-at-age sample size (CAA_nsamp), and built operating models (OMs) to simulate fisheries dynamics, and then applied management strategy evaluation (MSE). Relative yield is chosen as the result to evaluate the contribution of the seven indicators. Within the parameter range, there was not much information value reflected from fisheries-dependent parameters including Cobs, Cbias, and Iobs. On the other hand, for fisheries-independent parameters such as Kbias, Mbias, and Linfbias, similar tendency of the information value was showed in the results, in which the relative yield goes down from the upper bound to the lower bound of the interval. CAA_nsamp had no impact on the yield after over 134 individuals. The VOI analysis contributes to the trade-offs in the decision-making process. Information with more value is more worthy to collect in case of waste of time and money so that we could make the best use of scientific effort. But we still need to improve the simulation process such as enhancing the diversity and predictability in an OM. More parameters are on the way to be tested in order to collect optimum information for management and decision-making.