A comprehensive framework for the study of species co‐occurrences, nestedness and turnover

Abstract

Binary presence-absence matrices (rows  species, columns  sites) are often used to quantify patterns of species co-occurrence, and to infer possible biotic interactions from these patterns. Previous classifications of co-occurrence patterns as nested, segregated, or modular have led to contradictory results and conclusions. These analyses usually do not incorporate the functional traits of the species or the environmental characteristics of the sites, even though the outcomes of species interactions often depend on trait expression and site quality. Here we address this shortcoming by developing a method that incorporates realized functional and environmental niches, and relates them to species co-occurrence patterns. These niches are defined from n-dimensional ellipsoids, and calculated from the n eigenvectors and eigenvalues of the variancecovariance matrix of measured environmental or trait variables. Average niche overlap among species and the spatial distribution of niches define a triangle plot with vertices of species segregation (low niche overlap), nestedness (high niche overlap), and modular co-occurrence (clusters of overlapping niches). Applying this framework to temperate understorey plant communities in southwest Poland, we found a consistent modular structure of species occurrences, a pattern not detected by conventional presence-absence analysis. These results suggest that, in our case study, habitat filtering is the most important process structuring understorey plant communities. Furthermore, they demonstrate how incorporating trait and environmental data into co-occurrence analysis improves pattern detection and provides a stronger theoretical framework for understanding community structure.

A new model explaining the origin of different topologies in interaction networks

“…Despite nestedness and modularity being logically different topologies (Ulrich et al. ) and negatively correlated with one another in real‐world networks (Thebault and Fontaine , Pires and Guimaraes , Trøjelsgaard and Olesen ), several networks show combinations of them (Olesen et al. , Flores et al.…”

Species richness correlates of raw and standardized co‐occurrence metrics

“…Three common indices are proportional species turnover (b P 512 a g ), where a is the average local richness and g is the richness pooled over the same local sites (Tuomisto, 2010), the C-score, a normalized count of all 2 3 2 checkerboard submatrices within a species 3 sites presence-absence matrix (Stone & Roberts, 1990), and the b sim measure, which represents the degree of species replacement among sites in a way that is independent of the richness difference between the sites (Simpson, 1943(Simpson, , 1960. The popularity of the last of these measures has increased since Baselga (2010) species among sites, a pattern widely believed to be opposed to species turnover (but see Ulrich, Kryszewski et al, 2017).…”

“…The popularity of the last of these measures has increased since Baselga () partitioned the classical Sørensen index of compositional similarity such that β sim was one of the two components. Finally, we included the NODF (nestedness by overlap and decreasing fill) metric of nestedness (Almeida‐Neto, Guimarães, Guimarães, Loyola, & Ulrich, ), the ordered loss of species among sites, a pattern widely believed to be opposed to species turnover (but see Ulrich, Kryszewski et al, ).…”

Relating beta diversity of birds and butterflies in the Great Basin to spatial resolution, environmental variables and trait‐based groups

“…Variation in species composition may reflect turnover or nestedness (Figure ) (Soininen, Heino, & Wang, ; Ulrich et al, ). Turnover refers to changes in the identities of species independent of changes in species richness.…”