Superorganisms or loose collections of species? A unifying theory of community patterns along environmental gradients

Abstract

The question whether communities should be viewed as superorganisms or loose collections of individual species has been the subject of a long-standing debate in ecology. Each view implies different spatiotemporal community patterns. Along spatial environmental gradients, the organismic view predicts that species turnover is discontinuous, with sharp boundaries between communities, while the individualistic view predicts gradual changes in species composition. Using a spatially explicit multispecies competition model, we show that organismic and individualistic forms of community organisation are two limiting cases along a continuum of outcomes. A high variance of competition strength leads to the emergence of organism-like communities due to the presence of alternative stable states, while weak and uniform interactions induce gradual changes in species composition. Dispersal can play a confounding role in these patterns. Our work highlights the critical importance of considering species interactions to understand and predict the responses of species and communities to environmental changes.

Keywords: Alternative stable states; Lotka-Volterra model; community organisation; competition theory; critical transitions; environmental gradient.

Figure 1

Effects of competition on spatial community patterns. (a) Effects of the mean, μ(A), and standard deviation, σ(A), of competition strength on the inequality of abundance change along the gradient, G(ΔN). (b) Spatial community patterns associated with cases I, II, III and IV in (a). (c) Histograms of Jaccard’s distance, representing species turnover, associated with cases I, II, III and IV in (a). X-axis: Intensity of turnover, Y-axis: Number of turnover events on a log scale.

Figure 2

(a) Bifurcation diagrams obtained by the superimposition of the various community state indices (CSI) obtained from 100 simulations with different initial conditions. Each point on the bifurcation diagram represents a stable equilibrium. (b) Observed Multistability Index. This index represents the fraction of nine simulation runs for which multistability is observed. Theoretically, multistability is predicted to occur above the dashed line (Bunin 2018).

Figure 3

(a) Positive Feedback Index, which measures the fraction of indirect interactions that are positive. (b) Absolute Turnover Index, which combines two effects: (1) how many species appear or disappear in compositional shifts, and (2) how unequally these shifts are distributed along the gradient. For both indices, results are averaged over 9 simulations.

Figure 4

Effects of dispersal on spatial community patterns. (a) Effects of dispersal rate (d) and kernel size (σ_d) on inequality in abundance changes G(ΔN) along the gradient. (b) Spatial community patterns associated with cases 1, 2, and 3 in (a). At high dispersal, changes in species abundances along the gradient are very small and their inequality (as measured by the Gini coefficient) does not reflect any perceptible abruptness. To overcome this issue, we computed G(ΔN + N_min) with a threshold N_min = 1.