Using larval connectivity to inform conservation management of the endemic and threatened Atlantic mud-piddock (Barnea truncata) in the Minas Basin Canada - Krumhansl, Kira A.; Gentleman, Wendy C.; Wu, Yongsheng; Wang, Xiaoyi; DiBacco, Claudio (Frontiers in Marine Science, 2022)
Understanding metapopulation dynamics is critical for effective species conservation, but they are not always considered for marine species due to difficulties in assessing connectivity in marine environments. This is particularly true for species that are rare or threatened, as demographic and life history data are usually sparse. We employ Lagrangian Particle Tracking (LPT) to assess metapopulation dynamics and inform spatial management measures for the Atlantic Mud Piddock (AMP; Barnea truncata), a poorly studied and threatened marine bivalve mollusk in Canada, whose distribution in the country is limited to a single population in the Minas Basin, Nova Scotia. In a series of simulations designed to account for uncertainty in biological attributes of AMP, we identified that sub-populations along the southern coastline of the Minas Basin were the most strongly connected to other sub-populations by acting as the greatest sources and sinks of simulated larvae. Propagules released from the Minas Basin dispersed as far as the US coast of the Gulf of Maine, which harbors the closest known population of AMP outside of the Minas Basin. However, there was no exchange of larvae in the opposite direction, from the US population of AMP in the Gulf of Maine to the Minas Basin. These results suggest that sub-populations in the Minas Basin are self-sustaining (i.e., sub-populations that exchange larvae and ultimately act as a meta-population), supporting the need to protect critical source sites along the southern coastline for the regional persistence of this species. More generally, these results show how LPT outputs can be directly applied to conservation planning, and used to identify key knowledge gaps to address with future work to reduce uncertainty in model predictions.