Higher order interactions and species coexistence

Abstract

Higher order interactions (HOIs) have been suggested to stabilize diverse ecological communities. However, their role in maintaining species coexistence from the perspective of modern coexistence theory is not known. Here, using generalized Lotka-Volterra model, we derive a general rule for species coexistence modulated by HOIs. We show that where pairwise species interactions fail to promote species coexistence in regions of extreme fitness differences, negative HOIs that intensify pairwise competition, however, can promote coexistence provided that HOIs strengthen intraspecific competition more than interspecific competition. In contrast, positive HOIs that alleviate pairwise competition can stabilize coexistence across a wide range of fitness differences, irrespective of differences in strength of inter- and intraspecific competition. In addition, we extend our three-species analytical result to multispecies communities and show, using simulations, that multispecies coexistence is possible provided that strength of negative intraspecific HOIs is higher than interspecific HOIs. Our work sheds light on the underlying mechanisms through which HOIs can maintain species diversity.

Coexistence in diverse communities with higher-order interactions

“…We explored our research questions with a simple extension of the generalized Lotka-Volterra model to include third-order interactions [30,31,32], similar to those used in recent empirical studies [13,14,15]. In a community with S species, the dynamics of species i's density, N i , is given by…”

“…We explored our research questions with a simple extension of the generalized Lotka-Volterra model to include third-order interactions [30, 31, 32], similar to those used in recent empirical studies [13, 14, 15]. In a community with S species, the dynamics of species i ’s density, N i , is given by where R i is species i ’s intrinsic growth rate, d i describes the strength of intra-specific limitation (set to 1 from now on for simplicity), and the coefficient A ij describes the pairwise impact of species j on species i ’s growth (Fig.…”

Guided by Microbes: Applying Community Coalescence Principles for Predictive Microbiome Engineering

“…Moreover, some degree of stochasticity in community assembly, including priority effects and drift ( 38 ), makes the reproducibility of community coalescence difficult to predict in engineered systems. Likewise, the roles of higher-order interactions ( 32 , 39 , 40 ) and trophic interactions (e.g., viral predation) are still largely unknown and therefore make predicting microbiome dynamics challenging. How do we parameterize the collective interactions and account for stochastic events in engineered systems that require an intentional outcome?…”

Modeling phytoplankton-zooplankton interactions: opportunities for species richness and challenges for modern coexistence theory

“…As a result, we have little understanding of coexistence mechanisms in the highest trophic level or, more generally, how the entire community coexists (Godoy et al ., 2018). Additionally, modern coexistence theory is often applied to models with phenomenological or linear terms describing species interactions, excluding higher order interactions (Chesson, 2018; Germain et al ., 2016; Pérez-Ramos et al ., 2019) but see (Spaak et al ., 2021a; Shoemaker et al ., 2019; Spaak & De Laender, 2021; Spaak et al ., 2021b; Singh & Baruah, 2020; Letten & Stouffer, 2019). Consequentially, we have only limited understanding of the drivers and mechanisms of species coexistence in communities including multiple trophic levels and non-linear species interactions.…”