Dendritic connectivity controls biodiversity patterns in experimental metacommunities

Abstract

Biological communities often occur in spatially structured habitats where connectivity directly affects dispersal and metacommunity processes. Recent theoretical work suggests that dispersal constrained by the connectivity of specific habitat structures, such as dendrites like river networks, can explain observed features of biodiversity, but direct evidence is still lacking. We experimentally show that connectivity per se shapes diversity patterns in microcosm metacommunities at different levels. Local dispersal in isotropic lattice landscapes homogenizes local species richness and leads to pronounced spatial persistence. On the contrary, dispersal along dendritic landscapes leads to higher variability in local diversity and among-community composition. Although headwaters exhibit relatively lower species richness, they are crucial for the maintenance of regional biodiversity. Our results establish that spatially constrained dendritic connectivity is a key factor for community composition and population persistence.

Fig. 1.

Design of the connectivity experiment. (A) The river network (RN) landscape (Lower: red points label the position of LCs, and the black point is the outlet) derives from a coarse-grained optimal channel network (OCN) that reflects the 3D structure of a river basin (Upper). (B–E) The microcosm experiment involves protozoan and rotifer species. (B) Subset of the species (for names see SI Materials and Methods). (Scale bars, 100 μm.) (C) Communities were kept in 36-well plates. (D and E) Dispersal to neighboring communities follows the respective network structure: blue lines are for RN (D), same network as in A, and black lines are for 2D lattice with four nearest neighbors (E).

Fig. 2.

Experimental and theoretical local species richness in river network (RN) and lattice (2D) landscapes. (A and B) Mean local species richness (α-diversity, color coded; every dot represents a LC) for the microcosm experiment averaged over the six replicates. (C and D) Species richness for each of these replicates individually. (E and F) The stochastic model predicts similar mean α-diversity patterns (note different scales).

Fig. 3.

(A and B) Probability density function (pdf) of α-diversity for RN and 2D landscapes, with model distributions rescaled to experimental averages. (C) β-diversity (JSI) in 2D (red) and in RN (blue), as a function of topological distance between LC pairs (mean ± SD of experimental data, dotted lines are model predictions). The maximum geodesic distance obtained in a 36-site lattice landscape is 6 (in units of topological distance) in the 2D landscape and 11 in the RN. (D) Predicted time behavior of mean ± SD α-diversity for RN and 2D at two landscape sizes (36 and 1,040 LCs for RN and 36 and 1,225 LCs for 2D). (Upper Inset) α-diversity at t_exp = 24 d (black dashed line gives the experimentally measured time point) for a 1,040 LCs RN landscape (O is the outlet) and for a 1,225 LCs 2D landscape. (E) Rank-occupancy curve (red for 2D, blue for RN, and cyan for isolation): dotted lines are model predictions. Note the sharp decrease in occupancy for some protozoan species that the model does not predict, indicating stronger competition in the experiment (SI Materials and Methods).

Fig. 4.

(A) Experimentally observed α-diversity as a function of the degree of connectivity (d), e.g., the number of connected neighboring nodes to a LC. For LCs in isolation treatment, d = 0; in RN confluences (Cs) have d = 3 and headwaters (Hs) have d = 1; whereas in 2D all LCs have d = 4. Larger d results in significantly higher species richness. Boxes represent the median and 25th/75th percentile, and whiskers extend to 1.5 times the interquartile range. (B) JSI for Cs (green) and for Hs (black) separately. Solid symbols represent the mean ± SD of the experimental data and dotted lines the model predictions. For comparison, JSI for the entire RN (blue) and for the 2D (red) are shown.