Assessing the South African sardine resource: two stocks rather than one?

Abstract

Sardine Sardinops sagax distributed off the west and south coasts of South Africa have traditionally been assumed to comprise a single well-mixed stock for assessment and management purposes. New research, however, lends weight to the possibility of two stocks in this region. A precautionary management approach thus needs to consider the impact of management decisions on the hypothesised two individual stocks as well as on the resource as a whole. As a first step in this process, Bayesian assessments of South African sardine are presented, which compare results for the traditional single-stock hypothesis with those that follow from a new two-mixing-stock hypothesis. Recruits from the west stock are assumed to move to and remain part of the south stock in annual pulses of varying size. This movement is estimated to be appreciable, and to take place from a substantially more productive west stock to the south stock. This immigration makes a greater contribution to the south-stock biomass than do years of above-average south-stock recruitment. Importantly, this two-mixing-stock hypothesis is shown to be consistent with the data available. Further alternative sardine stock-structure hypotheses suggested by the most recent data are discussed.

Incorporating parasite data in population structure studies of South African sardine Sardinops sagax

“…2008). Using parasites as biotags has proved to be a powerful tool for population structure studies of South African sardine that has provided convincing and reinforcing support for the sardine multiple stock hypothesis and has contributed to the development of stock-specific assessments and consideration of alternative management strategies for South African sardine (de Moor and Butterworth, 2013). The biotag approach has also recently been applied to Cape horse mackerel ( Trachurus capensis ) in the Benguela ecosystem with parasite communities found to be able to differentiate between fish from the northern and southern sub-systems with a high (>90%) degree of accuracy (Le Roux, 2013).…”

“…In contrast, present management of the beach-seine fishery on the east coast is through effort limitation, with 25 current right-holders of which only 3–7 have been active in recent years (van der Lingen and Lamberth, 2013). Management of the purse-seine fishery has assumed that sardine off the South African west and south coasts comprise a single population, but consideration has recently been given to the hypothesis that western and southern sardine sub-populations, or stocks, may exist (de Moor and Butterworth, 2009). This hypothesis was initially based on observations from research surveys of a consistent break, situated between Cape Agulhas and Mossel Bay, in the distribution of sardine at low and medium biomass levels, with sardine distribution becoming continuous around the coast from Cape Columbine to Port Alfred only observed at high biomass levels (Coetzee et al .…”

Accounting for spatial complexities in the calculation of biological reference points: effects of misdiagnosing population structure for stock status indicators

“…When management (e.g., setting of catch quotas) ignores population structure or connectivity among population units, there is increased potential for overharvesting and system productivity is often incorrectly estimated (Fu and Fanning 2004;de Moor and Butterworth 2015). Even when population structure is recognized and accounted for within the management framework, if the spatial dynamics of the fishery (e.g., gear selectivity or effort) are ignored, the possibility of overharvesting can remain (Fahrig 1993;Mchich et al 2006;Ling and Milner-Gulland 2008;Benson et al 2015;Hoshino et al 2014).…”

Testing spatial heterogeneity with stock assessment models

“…Several authors developed and tested stock assessment models that explicitly included spatial dynamics [ 8 , 10 – 18 ]. While the importance/relevance of understanding spatial dynamics is widely recognized, these models require a large amount of information and are not easy to fit [ 12 , 19 ], with several authors reporting little or no advantage of spatially explicit models over closed population models [ 11 – 14 , 16 ].…”