phylospatial: An R package for spatial phylogenetic analysis with quantitative community data

Abstract Spatial phylogenetic approaches enhance biodiversity analyses by incorporating information about species' evolutionary similarity. However, existing software packages for analysing spatial phylogenetic diversity (PD) patterns only provide full support for binary (presence–absence) community data. A major gap exists in support for occurrence probabilities (such as from species distribution models), a common and valuable data type that captures spatial uncertainty and habitat suitability gradients. While abundance data are partially supported by existing PD tools, these lack features such as geospatial data integration and flexible null model analysis. These gaps limit PD research on quantitative features of species distributions and can introduce imprecision and bias if continuous biodiversity data are thresholded to work with existing tools. Here I present phylospatial , a new R package that fully supports probability, abundance, and binary community data across a range of spatial PD analyses. The package processes all three data types in a common framework, while handling them in distinct ways at key points in the analysis pipeline. It also integrates with raster and vector data formats, providing efficient workflows for geospatial data. phylospatial provides an integrated framework for performing various types of analyses, including calculating alpha PD and endemism, testing significance using community null models, identifying beta‐PD patterns including biogeographic regions, and conducting spatial conservation prioritization. I illustrate the package's functionality with worked examples using a dataset comprising a phylogeny and modelled occurrence probabilities for 5200 species of California plants. By facilitating spatial phylogenetic analysis of quantitative data types that more accurately represent the reality of species distributions, these methods and tools help broaden the range of questions and applications that can be addressed, and can help to increase the detail and statistical rigour of these studies.