Integrating multi-objective optimization and ecological connectivity to strengthen Peru's protected area system towards the 30*2030 target

Abstract

The Kunming-Montreal Global Biodiversity Framework (GBF) of the Convention on Biological Diversity has set the target of protecting 30 % of the world's land and sea by 2030. Previous conservation planning approaches have been based primarily on biodiversity elements, particularly for Peru, a mega-biodiverse country whose protected areas network need to be expanded. However, achieving this ambitious 30 % target requires careful consideration of numerous ecological and social aspects. To cover these aspects, we present a terrestrial conservation planning approach that integrates biodiversity, ecosystem services, human impact, ecological connectivity and ecoregional representativeness. Our approach has been co-produced with national organisations and NGOs and includes advanced Artificial Intelligence (AI) methods. Our results identify areas of high ecological value to supplement the 17.88 % of areas already protected, to reach 30 %. The integration of these areas could close gaps in the current system, particularly those vital for water related ecosystem services, ecoregional representativity and ecological connectivity. Integrated AI-based optimization methods (i.e., integer linear programming, constraint programming, reference point method) enabled us to obtain optimal, constraint-satisfying and balanced protected areas selected on the basis of integrated variables, and constitute a robust alternative compared with heuristic methods (e.g., Marxan, Zonation) commonly used. This work can be used as a fundamental component of Peru's territorial planning, and paves the way on future research on conservation planning, which should integrate advanced spatial conservation planning methods, ecological and social factors in an even more comprehensive way.