divraster: an R package to calculate taxonomic, functional and phylogenetic diversity from rasters

Diversity metrics are widely used to better understand biodiversity patterns and help with species conservation. Taxonomic diversity alone may lack valuable information on community ecology, such as the importance of species to ecosystem functioning and evolutionary history. By combining taxonomic, functional, and phylogenetic diversity, we can complement our knowledge, particularly under climate change, by improving conservation strategies and helping to maintain biodiversity while reducing costs in the decision-making process. However, the main tools used for computing these metrics may demand significant processing power because of their reliance on matrices, ultimately restricting their applicability. Here, we present divraster, an R package () to calculate diversity metrics directly from species rasters, with the aim of reducing memory usage and processing power, especially when working with large datasets such as large areas, high resolution, or a high number of species. The main function of divraster can calculate changes in a given community between distinct temporal scenarios, such as present and future climate scenarios, working with taxonomic, functional, and phylogenetic diversity. It includes the partition of temporal beta diversity, making it particularly useful in ecological niche models and other macroecology studies. Additionally, divraster performs spatial calculations for both alpha and beta diversity, which are helpful in studies where time is not an issue. We also conducted a performance comparison with packages that provide similar functions and demonstrated that the divraster package outperforms them in most cases regarding memory allocation and processing time.

Automated summary

Diversity metrics play a vital role in biodiversity research, but the existing tools for calculating these metrics often require significant processing power, limiting their applicability. In this study, the divraster package is introduced as a solution to this problem. By directly calculating diversity metrics from species rasters, divraster reduces memory usage and processing power, outperforming other similar packages in terms of performance.