Dynamic Phenotypic Clustering in Noisy Ecosystems

Abstract

In natural ecosystems, hundreds of species typically share the same environment and are connected by a dense network of interactions such as predation or competition for resources. Much is known about how fixed ecological niches can determine species abundances in such systems, but far less attention has been paid to patterns of abundances in randomly varying environments. Here, we study this question in a simple model of competition between many species in a patchy ecosystem with randomly fluctuating environmental conditions. Paradoxically, we find that introducing noise can actually induce ordered patterns of abundance-fluctuations, leading to a distinct periodic variation in the correlations between species as a function of the phenotypic distance between them; here, difference in growth rate. This is further accompanied by the formation of discrete, dynamic clusters of abundant species along this otherwise continuous phenotypic axis. These ordered patterns depend on the collective behavior of many species; they disappear when only individual or pairs of species are considered in isolation. We show that they arise from a balance between the tendency of shared environmental noise to synchronize species abundances and the tendency for competition among species to make them fluctuate out of step. Our results demonstrate that in highly interconnected ecosystems, noise can act as an ordering force, dynamically generating ecological patterns even in environments lacking explicit niches.

Challenges in linking trait patterns to niche differentiation

“…Furthermore, Scheffer et al showed that the addition of a stabilizing term to the population dynamical equations of the MacArthur–Levins model leads to permanent rather than just transient clusters (Scheffer and van Nes , Vergnon et al , but see Barabás et al ). Further, clusters are permanent in niche models that include the effects of frequent environmental variation (Ernebjerg and Kishony ), mutation (Bonsall et al ), and immigration (Barabás et al ) – see Fig. B.…”

“…Furthermore, Scheffer et al showed that the addition of a stabilizing term to the population dynamical equations of the MacArthur-Levins model leads to permanent rather than just transient clusters (Scheffer andvan Nes 2006, Vergnon et al 2012, but see Barabás et al 2013b). Further, clusters are permanent in niche models that include the effects of frequent environmental variation (Ernebjerg and Kishony 2011), mutation (Bonsall et al 2004), and immigration (Barabás Barabás et al 2013a. mechanism. If trait variation stabilizes coexistence through spatial heterogeneity in the environment, then the grain of environmental heterogeneity will affect the expected scale of trait dispersion.…”

Community Genetic and Competition Effects in a Model Pea Aphid System

“…The magnitude of competition effects, however, may also be influenced by the environment-modifying feedback of the herbivores themselves (Bü rger, 2002;Ernebjerg and Kishony, 2011;Fordyce, 2006;Jablonka, 2004;Laland, 2004;OdlingSmee et al, 2003). While many studies have examined effects of plant genetic variation on the form of associated communities, a process that likely influences herbivore competition (see Poelman et al, 2008), only a handful of studies have explicitly examined the influence of plant genetics on insect competition (e.g.…”

Emergent niche structuring leads to increased differences from neutrality in species abundance distributions

“…, Holt , Scheffer and van Nes , Vergnon et al. , , Ernebjerg and Kishony , Segura et al. ), and reviewed in D'Andrea and Ostling ().…”

“…In these examples, species with similar traits should compete more strongly because they will consume available resources or interact similarly with "limiting factors." Recent theoretical studies show that competitive interactions driven by species' differences along a trait axis typically lead to niche structuring in the form of persistent clusters of similar species (Bonsall et al 2004, Holt 2006, Scheffer and van Nes 2006, Pigolotti et al 2007, Ernebjerg and Kishony 2011, Segura et al 2011, Vergnon et al 2012, D'Andrea and Ostling 2016. These clusters emerge from the dynamics themselves instead of being externally imposed.…”