Mathematical Bio-Economics 2.0 for Sustainable Fisheries

Reconciling food security, economic development, and biodiversity conservation in the face of global changes is a major challenge. The sustainable uses of marine biodiversity in the context of climate change, invasive species, water pollution, and demographic growth is an example of this bio-economic challenge. There is a need for quantitative methods, models, scenarios, and indicators to support policies addressing this issue. Although bio-economic models for marine resources date back to the 1950s and are still used in fisheries management and policy design, they need major improvements, extensions, and breakthroughs. This paper proposes to design a Mathematical Bio-Economics 2.0 (MBE2) for Sustainable Fisheries to advance the development of bio-economic models and scenarios for the management of fisheries and marine ecosystems confronted with unprecedented global change. These models and scenarios should make both ecological and socioeconomic sense while being well-posed mathematically and numerically. To achieve this, we propose to base the MBE2 framework for Sustainable Fisheries on four research axes regarding the mathematics and modeling of: (i) ecosystem-based fisheries management; (ii) criteria of sustainability; (iii) criteria of resilience; and (iv) governance and strategic interactions. The associated methodology of MBE2 draws mainly on dynamic systems theory, optimal and viable controls of systems, game theory, and stochastic approaches. Our analysis, which is based on these four axes, allows us to identify the main methodological gaps to fill compared to current models for fisheries management. | IntroductionBalancing biodiversity conservation with food security and the preservation of a broader set of ecosystem services (ESs), in a context of ecological transition and climate change, is one of the greatest challenges of the century. The creation and development of the IPBES (International Platform for Biodiversity and Ecosystem Services) at the interface of decision-support and scientific knowledge is in direct line with these concerns.