You shall know a species by the company it keeps: leveraging co-occurrence data to improve ecological prediction

Abstract

O_LIMaking predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Due to the huge number of species, ecologists seek generalizations based on species traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species ecological attributes not captured by traits or phylogenies. C_LIO_LIWe propose using ordination to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction. We present an efficient method to derive species vectors from co-occurrence data using GloVe, an unsupervised learning algorithm originally designed for language modeling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from a global vegetation dataset and tested their ability to predict elevational range shifts in European montane plant species. C_LIO_LICo-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information. C_LIO_LIGiven the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities, and ecosystems. C_LI