Using Marxan to optimize the geographical and environmental representativeness of biodiversity sampling sites

Biodiversity sampling with sufficient geographical and environmental representativeness is essential for understanding biodiversity patterns and processes and designing effective conservation and management strategies. Yet, environmental representativeness is generally overlooked in existing sampling designs, and there are no user-friendly tools available for grassroots investigators that account for transportation accessibility and phased funding. We developed a novel sampling site selection approach using Marxan, by simply modifying default input datasets to include geographical and environmental data and transportation accessibility. Using the Nu-Salween River as a case study, we compiled an occurrence database of freshwater fish and then designed phased sampling strategies aiming at optimizing the coverage of species through geographical and environmental gradients, improving cost-effectiveness, and ensuring sampling feasibility compared to a random approach. Our approach was highly flexible in determining the optimal number of sampling units based on the number of surveying features (i.e., geographical units and environmental clusters). The output and visualization of the geographical locations of newly added sampling sites were adaptable to sampling strategies with phased funding. Compared to the random approach, the Marxan approach showed a two-fold increase in the mean species richness for selected sampling units and improved sampling effectiveness by 81.18 % (± 0.83 %) and cost efficiency by 81.75 % (± 0.02 %). We provide an efficient and customized sampling design that yields high geographical and environmental representativeness. It is accessible to grassroots investigators and applicable to various taxa, ecosystems, and regions, giving it the potential to significantly contribute to biodiversity inventory and conservation efforts.