Demographic and harvest analysis for blue shark (Prionace glauca) in the Indian Ocean
Introduction
Blue shark (Prionace glauca; BSH) is one of the most common bycatch species in pelagic tuna and swordfish longline fisheries (Nakano and Stevens, 2008, Bustamante and Bennett, 2013). It was globally assessed as “Near Threatened” in the last IUCN (International Union for Conservation of Nature) assessment. The catch of blue shark decreased during the early 1990s due to the worldwide ban of high-seas drift gillnet fisheries, and there has been no significant increase in catches in the Pacific or Atlantic Oceans thereafter (ICCAT, 2015, ISC, 2017). Blue sharks were found to be neither overfished nor subject to overfishing in previous stock assessments in the Pacific (Takeuchi et al., 2016, ISC, 2017) and Atlantic Oceans (ICCAT, 2015).
Blue shark in the Indian Ocean was not being overfished but may be experiencing overfishing based on the last stock assessment by the Indian Ocean Tuna Commission (IOTC) in 2017 (Rice, 2017, IOTC, 2017). However, the Indian Ocean blue shark (IO BSH) has experienced high fishing pressure in recent years, and current catch risk might deplete the stock to overfished status relatively quickly (IOTC, 2017). Catches of IO BSH have been recorded since 1950”- in the IOTC database (https://www.iotc.org/node/4108) and increased steadily from 1950–2019.
The IOTC scientific committee (SC) has suggested using multiple assessment methods (e.g., biomass dynamics models and age-structured assessment models) to compare estimates of stock status for IO BSH and provide more comprehensive management advice. Biomass dynamics models (e.g., Bayesian biomass dynamics model) rely on a prior distribution for the intrinsic rate of population increase (), which needs to be estimated outside the assessment model. Age structured assessment models (e.g., Stock Synthesis) often require values for the steepness (h) of the stock-recruitment relationship (the expected proportion of unfished recruitment for a stock depleted to 20% of its unfished spawning biomass) as an input parameter, which is also difficult to estimate inside a stock assessment model (Zhu et al., 2012). Geng et al. (2020) have incorporated life history information into Bayesian surplus production model to describe the performance and consequence for generating the informative prior from a basic demographic analysis where some biological parameter (e.g. pups survival rate) were collected from operating model of their simulation not life history information. The aim of their research was not to present a detailed demographic analysis, and female-only analysis might not reflect size and sexual dimorphism for IO BSH. The parameters of and h have not been estimated for the IO BSH. Therefore, it is important to estimate these parameters to improve the assessment of IO BSH and develop management advice.
Steepness is a biological parameter defining the productivity of a population when the spawning size decreases. Therefore, theoretically, it can be estimated from life-history information, including maximum recruitment per spawning biomass and the slope of the unfished stock-recruitment relationship curve (Myers et al., 1999). Demographic analysis tends to perform better for long-lived and slow-growing shark species (Tribuzio and Kruse, 2011). Demographic analysis with only life history information (growth curve, fecundity, and survival at age) was used to estimate the intrinsic rate of population increase and steepness of elasmobranch species in the Pacific and the Atlantic Oceans (e.g., Takeuchi et al., 2005, Chen and Yuan, 2006, Tsai et al., 2010, Cortés, 2016). However, no demographic model has been developed for IO BSH to estimate and related parameters.
The objectives of this study were to (1) estimate and quantify the uncertainty of the estimate, (2) estimate steepness; and (3) investigate the influence of harvesting scenarios on achieving a stationary population trajectory for IO BSH. Uncertainty about the estimate of natural motility (M), a parameter of the demographic model, is a major source of uncertainty in this study. Therefore, we used several empirical methods to estimate M and accounted for the uncertainty in the resulting estimates in the analyses.
Section snippets
Demographic method
Demographic analysis can be conducted using age- or size-structured population dynamics models and can be either female-only or for both sexes (Tsai et al., 2014). A two-sex Leslie population projection matrix (Caswell, 2006, Yokoi et al., 2017) was used to represent the demography of the IO BSH: where is the vector of numbers at each age in year t, and H is the harvest (or exploitation) matrix. For demographic analysis, H equal to 1 means unfished status; And for harvest analysis,
Natural mortality estimate
The natural mortality estimates from different methods are shown in Fig. 3. The methods of Chen and Watanabe lead to higher estimates of natural mortality for younger individuals. The range of the mean (overage) of M from various methods was 0.11–0.28 y and 0.12–0.32 y for female and male respectively (animals older than five years). The natural mortality rate for females of age 0 () estimated using the “Chen and Watanabe” and “ALL methods” were 0.80 y−1 and 0.45 y−1, and for male were
Natural mortality
As for many aquatic species, estimation of natural mortality for blue shark relies on empirical methods. The estimates of M (across age and maturity status) for adult female IO BSH were 0.11–0.28 y and for adult male were 0.12–0.32 y−1, close to the estimates in other areas. For example, Nakano (1994) estimated the M for blue shark in the North Pacific Ocean to be 0.17–0.21 y−1, while, the M was estimated at 0.20 y−1 by Takeuchi et al. (2005) and 0.24 y by Chen and Yuan (2006) for blue
CRediT authorship contribution statement
Zhe Geng: Conception and design of study, Acquisition of data, Analysis and/or interpretation of data, Writing - original draft. Yang Wang: Acquisition of data. Richard Kindong: Writing - review & editing. Jiangfeng Zhu: Conception and design of study. Xiaojie Dai: Acquisition of data, Writing - original draft.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We are grateful to Dr Carolina Minte-Vera and Alexandre Aires-da-Silva (IATTC) for assistance with coding and analysis of the demographic analysis. This work was supported by the National Natural Science Foundation of China (41676120) and Key Laboratory of Oceanic Fisheries Exploration (Ministry of Agriculture, China) at Shanghai Ocean University. The majority of work in this study was conducted when the senior author Z. Geng Visited the IATTC headquarters. We thank two anonymous reviewers and
References (46)
- et al.
Age and growth of the blue shark (Prionace glauca) in the Indian Ocean
Fish. Res.
(2019) - et al.
Insights into the reproductive biology and fisheries of two commercially exploited species, shortfin mako (Isurus oxyrinchus) and blue shark (Prionace glauca), in the south-east Pacific ocean
Fish. Res.
(2013) - et al.
Demographic analysis based on the growth parameter of sharks
Fish. Res.
(2006) - et al.
On the dangers of including demographic analysis in Bayesian surplus production models: A case study for Indian ocean blue shark
Fish. Res.
(2020) - et al.
Stock-recruitment relationships in elasmobranchs: Application to the North Pacific blue shark
Fish. Res.
(2018) - et al.
Implications of uncertainty in the spawner–recruitment relationship for fisheries management: An illustration using bigeye tuna (Thunnus obesus) in the eastern Pacific ocean
Fish. Res.
(2012) - et al.
Demographic and risk analyses applied to management and conservation of the blue shark (Prionace glauca) in the North Atlantic Ocean
Mar. Freshw. Res.
(2007) Early life-history implications of selected carcharhinoid and lamnoid sharks of the northwest atlantic
(1990)- et al.
Inclusion of discard and escape mortality estimates in stock assessment models and its likely impact on fisheries management
ICES CM
(2002) - et al.
Analytical reference points for age-structured models: application to data-poor fisheries
ICES J. Mar. Sci.
(2010)
Reproductive parameters of blue shark, prionace glauca, and other sharks in the gulf of guinea
Mar. Freshw. Res.
Matrix population models. Encyclopedia of environmetrics
Age Dependence of Natural Mortality Coefficient in Fish Population Dynamics
Distribution patterns and population structure of the blue shark (Prionace glauca) in the Atlantic and Indian Oceans
Fish Fish.
Incorporating uncertainty into demographic modeling: application to shark populations and their conservation
Conserv. Biol.
Comparative life history and demography of pelagic sharks
Sharks Open Ocean
Estimates of maximum population growth rate and steepness for blue sharks in the north and south Atlantic Ocean
Collect. Sci. Pap. ICCAT
Reproductive biology of the blue shark (prionace glauca) in the western north pacific ocean
Mar. Freshw. Res.
Reproduction of the blue shark Prionace glauca in the south-western equatorial Atlantic Ocean
Fish. Sci.
Observations on the biology and ecology of the blue shark in the North-east Atlantic
J. Fish Biol.
Empirical use of longevity data to estimate mortality rates
Fish. Bull.
Report of the 2015 ICCAT Blue Shark Stock Assessment Session
Report of the 13th Session of the IOTC Working Party on Ecosystems and Bycatch
Cited by (6)
Is restricting catch to young sharks only more sustainable? Exploring a controversial management strategy for bull, tiger, blue and bonnethead sharks
2022, Fisheries Management and EcologyStock Assessment Using Length-Based Bayesian Evaluation Method for Three Small Pelagic Species in the Northwest Pacific Ocean
2022, Frontiers in Marine ScienceResearch process and prospects of population simulations in fishery stock assessment
2022, Journal of Fishery Sciences of ChinaStock Assessment of Four Dominant Shark Bycatch Species in Bottom Trawl Fisheries in the Northern South China Sea
2022, Sustainability (Switzerland)