Using biophysical modelling and population genetics for conservation and management of an exploited species, Pecten maximus L.

Abstract

Connectivity between populations is important when considering conservation or the management of exploitation of vulnerable species. We investigated how populations of a broadcast‐spawning marine species (scallop, Pecten maximus) that occur in discrete geographic locations were connected to each other. Population genetic insights were related to the outputs from a three‐dimensional hydrodynamic model implemented with scallop larval behaviour to understand the extent to which these areas were linked by oceanographic processes and how this was altered by season and two contrasting years that had strongly different average temperature records (warm vs cold) to provide contrasting oceanographic conditions. Our results span from regional to shelf scale. Connectivity was high at a regional level (e.g. northern Irish Sea), but lower at scales >100 km between sites. Some localities were possibly isolated thus dependent on self‐recruitment to sustain local populations. Seasonal timing of spawning and inter‐annual fluctuations in seawater temperature influenced connectivity patterns, and hence will affect spatial recruitment. Summer rather than spring spawning increased connectivity among some populations, due to the seasonal strengthening of temperature‐driven currents. Furthermore, the warm year resulted in higher levels of modelled connectivity than the cold year. The combination of genetic and oceanographic approaches provided valuable insights into the structure and connectivity at a continental shelf scale. This insight provides a powerful basis for defining conservation management units and the appropriate scale for spatial management. Temporal fluctuations in temperature impact upon variability in connectivity, suggesting that future work should account for ocean warming when investigating population resilience.

Connectivity between populations of the scallop Pecten maximus in the Irish Sea and the implications for fisheries management

“…Our results are based on mesoscale (2-10 km) and interannualmean perspectives of the Celtic and Irish Seas circulation. The simulated velocities from the ocean model were generally in good agreement with other similar scale models developed for this region (Xing and Davies, 2001;Horsburgh and Hill, 2003;Hartnett et al, 2007;Hold et al, 2021). We incorporated the general seasonal development of the hydrodynamics with some realistic natural variability; however, several dispersive processes have not been fully characterised, such as circulation patterns associated with different synoptic storm tracks (Bricheno et al, 2023) and fine-scale coastal circulation associated with The effect of changing the growth rate, depth of the diel vertical migration (DVM), size at settlement, delay period and release date upon the pelagic larval duration (PLD) in days.…”

“…This could provide information about when recruitment may be high or low, which could be proven by a coordinated stock assessment that would yield observed densities and distributions that would substantially improve the modelling. The implementation combined with microchemistry or genetic methods, could help validate patterns from mathematical models, particularly from external sources (Nicolle et al, 2013;Hold et al, 2021). For instance, in 1985 large numbers of larvae of P. maximus were trapped in an area with unsuitable settlement substrate by a whirlpool located in St. Brieuc Bay, France, which was caused by strong winds blowing in the opposite direction of the tidal currents.…”

“…Spatial variability in scallop population sizes, commercial catch rates, biological characteristics, and oceanographic conditions pose difficulties for management. For example, assessing recruitment has been overlooked as a management tool but is now being considered (Nicolle et al, 2017;Handal et al, 2020;Hold et al, 2021). As a result, there may be little relationship between the spawning population and self-recruitment, which makes it difficult to use survey data, or commercial catch and effort data, to forecast the effects of management measures such as catch limitations, minimum landing sizes and effort restrictions.…”

“…Previous work examining connectivity networks through modelling approaches in P. maximus populations have focused on using a fixed PLD (Nicolle et al, 2013;Le Goff et al, 2017;Nicolle et al, 2017;Hold et al, 2021). In this study, we developed a 3-dimensional ocean model coupled to a Lagrangian particle tracking model (PTM) that used the modelled water temperatures to determine the growth rate, thus the time taken to reach each larval stage; hence providing detailed representation of the biological phenomenon in the simulation of the behavioural characteristics.…”

Genetic and particle modelling approaches to assessing population connectivity in a deep sea lobster

“…This complementary approach illustrates how oceanographic drivers have the potential to influence genetic exchange among populations, including those separated by long distances. Many particle modelling studies have focused on the larval dispersal of shallow-water, coastal marine invertebrates (e.g., mussels 46,47 ; scallops 35,48,49 ; and lobsters 36,50 ), which are typically influenced by more physically complex coastline and associated hydrodynamic processes compared with generally less complex bathymetry of deep water on continental shelf margins. In contrast, in deep sea species, which frequently have enormous distribution ranges, larval dispersal relies on broad scale hydrographic processes.…”