Oceanographic flow regime and fish recruitment: reversed circulation in the North Sea coincides with unusually strong sandeel recruitment

Abstract

The search for environmental descriptors of fish stock production is ongoing. Although numerous correlations between environmental variables such as food abundance or sea surface temperature have been proposed in the past, few are operational in a fisheries management context today. Reasons for this may include many -but spurious -correlations, the use of higher-level climate-change indicators, and misconceived perception of causal relationships. In the present study, we demonstrate how modelled oceanographic data describing local conditions, combined with a simple probabilistic risk assessment, can be used to forecast fish recruitment. We used the lesser sandeel Ammodytes marinus in the North Sea as an example, and focussed on the circulation patterns experienced by the first-feeding larvae on the Dogger Bank. A strong link between the net direction of water transport at the surface and unusually strong year-classes of sandeel was found. For example, the most extreme recruitments only took place in years with a particular type of flow regime in February, which may be associated with the occasional reversals of the North Sea circulation. Using risk ratios, we show the potential for using flow regime in probabilistic short-term forecasts of unusually strong year-classes. Lastly, we propose a hypothesis for recruitment in sandeel which could be extended to other species, and thereby contribute to future studies of predictors in recruitment forecasting.

Influence of larval transport and temperature on recruitment dynamics of North Sea cod (Gadus morhua) across spatial scales of observation

“…High SST can influence recruitment through faster development and thus increased retention and survival to settlement (Heath, Kunzlik, Gallego, Holmes, & Wright, 2008). Additionally, both SST and NAO could be proxies for other phenomena acting at local scales, such as food availability (Capuzzo et al., 2018;Nicolas et al., 2014) and flow regimes (Henriksen et al., 2018). NAO and SST can furthermore be correlated to connectivity and retention (Table 4).…”

“…The retention and connectivity patterns estimated here broadly reflect known patterns for the area. The southern North Sea is characterised by a generally retentive system (Henriksen et al., 2018), while in the northern area there is a strong flow to the Skagerrak and the Norwegian Sea (Huserbråten et al., 2018). Consequently, the South and North populations are generally isolated, with limited connectivity (Heath et al., 2008).…”

“…Punt, 2019), and there is increasing interest in including spatial structure in recruitment dynamics in stock assessment (Cadrin et al., 2019;Hidalgo et al., 2019;Punt, 2019). Although reliable ocean current forecasts are not available in advance, estimates of larval drift can be useful to inform short‐term forecasts (Henriksen et al., 2018;Hidalgo et al., 2019). This effort is however constrained by the availability and rapid applicability of ocean models in the context of operational fisheries oceanography (Hidalgo et al., 2019).…”

“…A number of factors affect the ELS survival, including temperature, food availability and predation (Folkvord, 2005;Peck & Hufnagl, 2012). In addition to these factors, the interannual variability in recruitment can be influenced by advective transport of eggs and larvae from spawning to nursery areas (Bailey, 1981;Henriksen et al., 2018). Recent physical–biological modelling studies have related larval transport and success to recruitment using various approaches (Peck & Hufnagl, 2012), including comparisons between modelled larval survival and observed recruitment (Daewel, Schrum, & Gupta, 2015) and between modelled and observed juvenile distributions (Huwer, Hinrichsen, Hussy, & Eero, 2016).…”

“…Moreover, transport can influence connectivity among populations (e.g. through interannual variability in oceanographic current patterns, Huwer et al., 2016;Kvile, Romagnoni, Dagestad, Langangen, & Kristiansen, 2018) and recruitment dynamics across large geographic scales (Cadrin, Goethel, Morse, Fay, & Kerr, 2019;Henriksen et al., 2018;Hinrichsen, Von Dewitz, & Dierking, 2018) and thereby population management (Fogarty & Botsford, 2007;Hidalgo et al., 2019;Ramesh, Rising, & Oremus, 2019). Critically, the spatial scale of observation can affect the stock–recruitment relationship, providing contrasting results across scales (Chang, Chen, Halteman, & Wilson, 2016).…”