Multi-objective optimization applied to Systematic conservation Planning and spatial conservation priorities under climate change

Biodiversity problems require strategies to accomplish specific conservation goals. An underlying principle of these strategies is known as Systematic Conservation Planning (SCP). SCP is an inherently multi-objective (MO) problem but, in the literature, it has been usually dealt with a monobjective approach. In addition, SCP analysis tend to assume that conserved biodiversity does not change throughout time. In this paper we propose a MO approach to the SCP problem which increases flexibility through the inclusion of more objectives, which whilst increasing the complexity, significantly augments the amount of information used to provide users with an improved decision support system. We employed ensemble forecasting approach, enriching our analysis by taking into account future climate simulations to estimate species occurrence projected to 2080. Our approach is able to identify sites of high priority for conservation, regions with high risk of investment and sites that may become attractive options in the future. As far as we know, this is the first attempt to apply MO algorithms to a SCP problem associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.