Spatial Phylogenetics with Continuous Data: an Application to California Bryophytes

Abstract

Spatial phylogenetics is premised on the idea that species are not discrete categorical entities but instead lie on a hierarchical evolutionary continuum that contains rich biological information valuable for quantifying spatial biodiversity patterns. Yet while spatial phylogenetic approaches use quantitative information to represent phylogenetic patterns, most have continued to rely on methods that discard valuable information about spatial patterns by converting continuous variables into binary categories. This includes representing geographic ranges using binary presence-absence data, classifying statistical significance into categories, and quantifying biogeographic gradients into discrete regions. In this paper we show how a full suite of spatial phylogenetic analyses, including analyses of alpha and beta diversity, neo- and paleo-endemism, biogeographic hypothesis testing, and spatial conservation prioritization, can be implemented with "smooth" methods that never remove information content by categorizing continuous data. Our analysis focuses on the bryophytes of California, an understudied group in a global plant biodiversity hotspot. Using a time-calibrated phylogeny and species distribution models for 548 species of mosses and liverworts, we profile the evolutionary diversity, compositional turnover, and conservation value of bryophyte communities across the state. Our results highlight important patterns in the diversity of this key plant group, while our methods can serve as a model for future studies seeking to maximize the information content of spatial phylogenetic analyses.