Length-Based Assessment Methods for the Conservation of a Pelagic Shark, Carcharhinus falciformis from the Tropical Pacific Ocean

Abstract

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.

This is what we know: Assessing the stock status of the data-poor skipjack tuna (Katsuwonus pelamis) fishery in the South China Sea

“…This visualization of R n scores across discrete combinations of both variables may also aid in narrowing possible variable ranges in subsequent, more refined searches (Mildenberger et al, 2017). While our R n score (0.515) is higher than that reported in other recent studies (Alam et al, 2022;Kindong et al, 2022), the parameters estimated by "TropFishR" match those of other studies (Table 2). Therefore, the priors that we used in LBB and LBSPR are sound and informative.…”

“…Length-frequency data were grouped into monthly catches, assuming that samples represented the monthly length distribution of the overall catch (Kindong et al, 2022). We used ELEFAN in the "TropFishR" package (Mildenberger et al, 2017) to estimate the asymptotic length (L inf ) and growth coefficient (K) of skipjack tuna.…”

“…We use the LBSPR method to estimate the SPR of skipjack tuna in the SCS. This method has been widely used in stock assessments for data-poor fisheries (Prince et al, 2015;Prince et al, 2020;Han et al, 2021;Alam et al, 2022); Kindong et al, 2022, and it has proven to be a consistent and accurate model (Chong et al, 2020;Pons et al, 2020). LBSPR assumes that SPR of an exploited fish stock is a function of relative fishing mortality (F/M) and two life-history ratios (M/K and L 50 /L inf ).…”

“…The LBSPR model applies maximum likelihood estimation to evaluate the selectivity ogive, which is expected to be a logistic curve described by the selection parameters (SL 50 and SL 95 ), and F/M, which are then utilized to estimate the SPR (Prince et al, 2015;Hordyk et al, 2016). Note that SPR estimates of 20% represent a limit reference point; SPR estimates< 20% suggest a level likely to reduce recruitment and a stock verging on collapse; and SPR estimates range 0.35-0.4 are generally associated with a stock at MSY levels (Hordyk et al, 2015;Prince et al, 2015;Kindong et al, 2022). All LBSPR analyses were performed using R code, available at https://cran.r-project.org/web/ packages/LBSPR (access date: 11 September 2022).…”

“…Stock assessment is becoming increasingly necessary to implement effective fishery management and sustainability. Nevertheless, only one fifth of the global landings comes from assessed species, and less than one eighth of the world's fisheries are sufficiently well managed or have detailed stock assessments for management purposes (Costello et al, 2012;FAO, 2022;Kindong et al, 2022). The severity of this problem is increasingly recognized, and various types of data-poor stock assessment methods have been put forward over the past 20 years as part of a solution (Zhang et al, 2021a;Liao et al, 2022).…”