Analyzing floristic inventories with multiple maps
Data visualization, Multi-dimensional scaling, Non-metric similarities, Species occurrence modeling, t-Distributed Stochastic Neighbor Embedding
Spatial observations of plant occurrences contain a wealth of information on relations among species and on the relation between species and environmental conditions. Typically, inventory data of this kind are large co-occurrence matrices, and hence, direct ecological interpretations based on expert knowledge are often very difficult. Hitherto, ordination approaches have been used to construct a virtual ordination space (repre- sented as one or multiple scatter plots) in which species that often co-occur are situated close together, whereas species that hardly co-occur are found far apart. In this study, we investigate a recently proposed or- dination approach, multiple maps t-SNE, that constructs multiple, independent ordination spaces in order to reveal and visualize complementary structure in the data. We compare multiple maps t-SNE to several con- ventional ordination approaches, exploring a large inventory of vascular plant occurrences (FLORKART). Our results reveal that multiple maps t-SNE is well suited for the analysis of floristic inventories. In particular, multiple maps t-SNE uncovers the major dependencies of species co-occurrences on climate and soil biogeo- chemical preconditions.