A family of null models to distinguish between environmental filtering and biotic interactions in functional diversity patterns

Abstract

Questions: Traditional null models used to reveal assembly processes from functional diversity patterns are not tailored for comparing different spatial and evolutionary scales. In this study, we present and explore a family of null models that can help disentangling assembly processes at their appropriate scales and thereby elucidate the ecological drivers of community assembly.

Location: French Alps.

Methods: Our approach gradually constrains null models by: (1) filtering out species not able to survive in the regional conditions in order to reduce the spatial scale, and (2) shuffling species only within lineages of different ages to reduce the evolutionary scale of the analysis. We first tested and validated this approach using simulated communities. We then applied it to study the functional diversity patterns of the leaf-height-seed strategy of plant communities in the French Alps.

Results: Using simulations, we found that reducing the spatial scale correctly detected a signature of competition (functional divergence) even when environmental filtering produced an overlaying signal of functional convergence. However, constraining the evolutionary scale did not change the identified functional diversity patterns. In the case study of alpine plant communities, investigating scale effects revealed that environmental filtering had a strong influence at larger spatial and evolutionary scales and that neutral processes were more important at smaller scales. In contrast to the simulation study results, decreasing the evolutionary scale tended to increase patterns of functional divergence.

Conclusion: We argue that the traditional null model approach can only identify a single main process at a time and suggest to rather use a family of null models to disentangle intertwined assembly processes acting across spatial and evolutionary scales.

Keywords: Assembly rules; Biotic and abiotic filtering; Limiting similarity; Null models; Simulated communities.

Fig. 1. Comparison of the outcomes of the ‘equiprobable randomisation’ (EQ-R) and the ‘suitability-based randomisation’ (SB-R) null models for the simulated community data.

Each subplot presents the distribution of the ranks in a violin plot (Hintze & Nelson 1998) generated for a specific combination of environmental filtering (Benv) and competition (Bcomp). Community assembly is random in the upper-left corner, driven by competition only in the upper-right corner (Bcomp > 0), driven by environmental filtering only in the lower-left corner (Benv > 0), and driven by the interplay of these processes in the lower-right corner). A rank value higher than 0.975 indicates a diversity value higher than expected under the null model, while a rank value lower than 0.025 indicate a diversity value lower than expected under the null model

Fig. 2. Comparisons of the outcomes of the ‘intra-lineages randomisation’ (IL-R) as functions of the evolutionary scale.

Each subplot contains the median of the distribution of ranks of communities generated for a specific combination of environmental filtering (Benv > 0) and competition (Bcomp > 0). A rank value close to ‘Root’ indicates a ‘close-to-root’ age value while an age value close to ‘Tips’ indicates a ‘close-to-tips’ age value. Specifically the randomizations at age ‘Root’ are ‘across-clades randomisation’ (AC-R), i.e. all tips are shuffled among each other. Closed (open) symbols indicates the coupling with EQ-R (SB-R); square (circle) symbols indicate the distribution of ranks for communities whose phylogeny was generated by a δ parameter of 0.1 (10) and thus high (low) phylogenetic signal.

Fig. 3. Comparison of the outcomes of the different constrained null models for the case study according to the scales of the analysis.

Results at large spatial and evolutionary scales (EQ-R: AC-R) are compared against the results: at fine spatial scale and large evolutionary scale (SB-R, first column), at large spatial scales and small evolutionary (IL-R, second column), and at fine spatial and evolutionary scales (SB-R: IL-R, third column). The first row presents results for the reduced species pool (R-SP) and the second row presents results for the extended species pool (E-SP). The dotted lines represent the significance threshold of the rank values (0.025 and 0.975). The thick lines separate the communities whose ranks increased from these whose ranks decreased with the use of the constrained null model vs. the non-constrained null model. For a numerical summary, see Table 2

Fig. 4. Sample space of ‘intra-lineage randomization’ (IL-R) as a function of the evolutionary scale.

Sample space was estimated as the number of different random communities the IL-R null model can generate for an ‘observed’ community and at varying evolutionary scales. We displayed the median (diamond), maximum (square) and minimum (circle) over communities for each species pool (R-SP, filled symbols; E-SP, open symbols). The horizontal dotted line indicates the threshold of 1000 random possibilities. The x-axis represents the age value used as a parameter for the IL-R and the vertical dotted line indicates the evolutionary scale used to generate Fig. 3.