Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities

Abstract

A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a transition as a function of available energy fluxes from a "resource-limited" regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is the overlap between species' consumption preferences. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experimental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.

Fig 1. Microbial communities engineer complex chemical environments using a single energy source.

(A) Schematic of microbe-mediated energy fluxes in the Thermodynamic Microbial Consumer Resource Model. Each cell of species i(= 1, 2, … S) supplies itself with energy through import of resources, generating an incoming energy flux $J_{i\alpha }^{\text{in}}$ for each resource type α(= 1, 2, … M). A fraction l_α of this energy leaks back into the environment in the form of metabolic byproducts, with each byproduct type carrying an outgoing energy flux $J_{i\beta }^{\text{out}}={\sum }_{\alpha }{l}_{\alpha }{D}_{\beta \alpha }{J}_{i\alpha }^{\text{in}}$. The remaining energy, $J_i^{\text{grow}}$, is used to replicate the cell. (B) Each species is defined by a vector of consumer preferences that encode its capacity for harvesting energy from each resource type. These vectors comprise a consumer matrix c_iα. (C) A regional pool of species is randomly generated, and communities are initialized with random subsets of these species to simulate stochastic colonization. (D) Each community is supplied with a constant flux κ₀ of a single resource type (α = 0), and all resources are continuously diluted at a fixed rate ${\tau }_R^{-1}$. (E) Consumer populations N_i and resource concentrations R_α as a function of time for two realizations of this model, with low (l = 0.001) and high (l = 0.8) levels of uniform metabolic leakage (see S1 Text Section 2B for parameters).

Fig 2. Steady-state richness as a function of metabolic leakage l and externally supplied energy flux w₀κ₀.

We generated 200 species, initialized 10 communities of 100 species each from this pool, and ran the dynamics to steady state under different combinations of w₀κ₀ and l (see main text and S1 Text Section 2B for parameters). (A) Heat map summarizing all simulations, colored by the average number of surviving species per steady-state community (“Richness”). Slices through the heat map are plotted in S3 Fig. (B) Community compositions are displayed as rank-abundance curves for three illustrative w₀κ₀, l combinations (colored by community richness): (1) “syntrophy-limited” (w₀κ₀ = 1000, l = 0.1), (2) “energy-limited” (w₀κ₀ = 28, l = 0.6) and (3) “similarity-limited” (w₀κ₀ = 1000, l = 0.9). The lines are assigned different shades for clarity. The first two examples are parts of the same resource-limited regime, manifesting similar statistical properties. The plots are truncated at a relative abundance of 0.5%; see S4 Fig. for full data.

Fig 3. Energy flux networks differ in the two regimes.

Community-scale energy flux networks are plotted for a characteristic example from the diverse and resource limited regimes and two different choices of metabolic matrix D_αβ. The color of each pixel in the heat maps indicates the logarithm (base 10) of the corresponding matrix entry. In the networks, each node represents one of the M = 100 resource types. Edges represent steady-state energy flux from one resource type into another, mediated by consumer metabolism and leakage/secretion. The thickness of each edge is proportional to the flux magnitude, and edges with magnitudes less than 1% of the maximum flux are not displayed. The single node at the top of each graph is the externally supplied resource, and the rows of nodes at the bottom are resources that are not connected to the external supply by any flux above the 1% threshold. A topological analysis of the flux networks of all the simulated communities can be found in S10 Fig.

Fig 4. Structure and stability of resource dynamics depend on ecological regime.

(A) Consumed energy fluxes $(1-l)J_{i\alpha }^{\text{in}}$ for each of the ten surviving species in a resource-limited community (example 2 from Fig 2). The black portion of the bar is the flux $(1-l)J_{i0}^{\text{in}}$ due to the externally supplied resource, and the colored bars represent the contributions of the other resources. Since these communities have reached the steady state, Eq (3) implies that the total height of each bar equals the maintenance cost m_i of the corresponding consumer species. (B) Same as previous panel, but for a community from the diverse regime (example 3 from Fig 2). (C) Simpson diversity $M_i^{\text{eff}}$ of steady-state flux vector $J_{i\alpha }^{\text{in}}$ for each species from examples 2 (resource-limited) and 3 (diverse) in Fig 2. Vertical lines indicate the values of this metric when all the flux is concentrated on a single resource (“Specialist”), and where it is evenly spread over ten resource types (“Generalist”). (D) Logarithm of susceptibility ${log}_{10}\partial {\overline{R}}_{\alpha }/\partial {\kappa }_{\alpha }$ of community-supplied resources (α ≠ 0) to addition of an externally supplied flux κ_α in these two examples.

Fig 5. Richness of diverse regime depends on generalized niche-overlap.

We took the values of supplied energy flux w₀κ₀ and leakage fraction l from the three examples highlighted in Fig 2, and varied the average niche overlap 〈ρ_ij〉 between members of the metacommunity. For each w₀κ₀, l combination and each value of 〈ρ_ij〉, we generated 10 pools of 200 species, initialized 10 communities of 100 species each from this pool, and ran the dynamics to steady state. The steady-state richness of each community is plotted against the niche overlap. Points are colored by their regime (diverse or resource-limited), and solid lines are linear regressions. Inset: c_iα matrices that define the regional pool for two different levels of overlap, with dark squares representing high consumption coefficients.

Fig 6. Resource-limited regime features community-level environmental filtering.

(A) Presence (black) or absence (white) of all species in all 1,000 communities from the original simulations of Fig 2. (B, C, D) We initialized 200 new communities for each of the three examples highlighted in Fig 2A, by randomly choosing sets of 100 species from the regional pool. Each panel shows the projection of final community compositions {N_i} onto the first two principal components of the set of compositions.

Fig 7. Ecological regimes and nestedness in microbiome data.

(A) 16S OTU compositions of tropical mesopelagic zone samples from the Tara Oceans database, collected at a depth of 200 to 1,000 meters [36]. Each dot is the projection of one sample onto the first two principal components of the collection of mesopelagic zone samples. (B) Same as A, but for tropical surface water layer samples, collected at a depth of 5 meters. (C) Presence (black) or absence (white) of each OTU above 0.5% relative abundance across all Tara Oceans samples.