Community coalescence asks what happens when two previously assembled microbiomes enter a shared environment. In a microbial consumer-resource model (MiCRM), the workflow combines their consumers and resource-processing traits, then measures the composition and function of the resulting community.

Use coalescence experiments to ask whether one parent dominates, whether resource complementarity supports coexistence, and how competition, cross-feeding, carbon-use efficiency, or stability change after mixing.

Define the parental systems

For parent communities 1 and 2, record their consumer sets, ordered resource sets, uptake matrices $u$, leakage tensors $l$, mortality vectors $m$, supply and loss terms, and equilibrium states $(\hat C, \hat R)$. Do not merge systems until each parental endpoint has passed the chosen equilibrium diagnostic.

If both parents use the same ordered resources, stack consumer-specific parameters:

\[u^{(3)} = \begin{bmatrix}u^{(1)} \\ u^{(2)}\end{bmatrix},\qquad l^{(3)} = \begin{bmatrix}l^{(1)} \\ l^{(2)}\end{bmatrix},\qquad m^{(3)} = \begin{bmatrix}m^{(1)} \\ m^{(2)}\end{bmatrix}.\]

A common biomass initial condition is the concatenated parental equilibrium:

\[C^{(3)}(0) = \begin{bmatrix}\hat C^{(1)} \\ \hat C^{(2)}\end{bmatrix}.\]

Choose the initial resource state explicitly: a standard medium, one parent’s state, an average of both parents, or a measured post-mixing environment. This choice changes the initial invasion conditions and must be reported.

Reconcile resource identities

For partially overlapping resource sets, construct a union with stable resource identifiers and embed both parents into that common ordering. Zero uptake for a resource that a parent could not use before mixing; do not silently reinterpret matrix columns by position.

The resource-overlap ratio is:

\[\Omega_R = \frac{|R^{(1)} \cap R^{(2)}|}{|R^{(1)} \cup R^{(2)}|}.\]

Higher overlap can increase direct competition. Lower overlap can support complementarity when one parent’s leaked products match the other’s demand.

Quantify mechanisms and outcomes

Uptake similarity provides a simple competition proxy:

\[\mathrm{competition} = \frac{2}{N(N-1)}\sum_{i<j} \frac{u_i\cdot u_j}{\lVert u_i\rVert\,\lVert u_j\rVert}.\]

An uptake-weighted leakage profile is $L^{\mathrm{eff}}{i\beta}=\sum\alpha u_{i\alpha}l_{i\alpha\beta}$. Comparing this profile with other consumers’ uptake identifies potential cross-feeding, but it does not prove that the corresponding flux occurs in the simulated resource state.

Report at least:

  • the equilibrium and extinction criteria used for both parents and the merge;
  • the initial post-mixing resource state and resource-identity mapping;
  • survivors and biomass contribution from each parent;
  • similarity of the merged community to each parent;
  • residual resources or resource drawdown; and
  • any competition, facilitation, carbon-use-efficiency, or stability metric.

Implementation status

DigiMicPy can express this workflow by constructing a combined MiCRMParameters object and calling its existing solver, but it does not yet provide a dedicated coalescence helper. See the DigiMicPy package documentation for the current Python recipe.

Back to all workflows