A novel decision-support system for augmenting sustainable lobster production through comparison of mariculture systems at increasing stages of technological development

Abstract

In the face of depleting wild stocks of lobster globally, mariculture is widely viewed as a technology for enhancing production, resource efficiency, economics, and effective management of dwindling stocks while supporting livelihood security. We used a novel multi-stage experimental strategy followed by robust non-linear growth model simulations for establishing a Decision-Support System (DSS) to foster sustainable and cleaner production practices in lobster mariculture. The strategies explored in the first stage, which dealt with rearing lobsters in sea cages at increasing levels of technological development, significantly enhanced lobster production and thus, culture system productivity 5.9 times over unsustainable conventional systems. Growth performance, feed utilization, and survivability of lobsters were found to be significantly ameliorated in culture setup-3 (P ≤ 0.01), which was the most technologically advanced setup. In the second stage, non-linear models were fit to growth curves, and the best-fit models were used for the prediction of lobster biological metrics in the mariculture systems. These predictions were then validated using in-situ allometric traits. The DSS thus developed can effectively aid decision-making to optimize culture duration, stock condition, feed optimization, and harvest size to maximise economic returns from lobster mariculture while ensuring sustainability.