Substrate-Agnostic Ecology

• Substrate-agnostic ecology is an abstract framework that examines ecological dynamics independent of specific material substrates, emphasizing agent-environment interactions.
• It spans diverse fields such as astrobiology, microbial biophysics, and digital systems, merging biosignatures and technosignatures through common ecological principles.
• The approach leverages mechanisms like niche construction and stigmergy to establish invariant ecological relationships, guiding robust diagnostics and modeling across substrates.

Substrate-agnostic ecology denotes ecological analysis that does not presuppose a particular material realization of organisms, environments, or technologies. In its most explicit formulation, it is “an abstraction of ecological relationships that does not presuppose terrestrial substrates, chemistries, or anthropocentric notions of technology,” and instead treats ecological organization as emerging from agent–environment coupling, reciprocal causation, ecological inheritance, and stigmergic environmental modification (Likavčan, 2 Jul 2026). In adjacent literatures, the same expression also names mechanisms whose ecological effects survive changes in the underlying medium, as in barrier-free microbial microhabitats produced by crowding, growth-driven flows, and roughness or friction across gut crypts, plant apoplasts, soil pores, and engineered surfaces (Slepukhin et al., 27 Mar 2025).

1. Conceptual scope and usage

Within astrobiology and SETI, substrate-agnostic ecology is framed as the ecological unification of agnostic biosignatures, agnostic technosignatures, and ecologies. The “3x” framing treats both biosignatures and technosignatures as detectable environmental modifications produced by agents, so that the conventional boundary between “bio” and “techno” collapses into the broader ecological category of stigmergic, niche-constructed environmental structure (Likavčan, 2 Jul 2026).

In microbial biophysics, the expression is used more mechanistically. There, “substrate-agnostic” means that coexistence does not depend on substrate-specific chemical properties, but on generic physical features of colonization on surfaces: crowding and jamming at high densities, growth-induced flow fields, and ubiquitous surface interactions such as roughness, friction, and adhesion (Slepukhin et al., 27 Mar 2025). In software and digital-systems research, the same broad idea appears as the claim that ecological laws and organizational principles apply irrespective of whether the substrate is biological or computational: “species” map to modules, services, or agents; interactions map to dependencies, flows, and competition; environments map to runtime conditions, user demand, and platform constraints (Baudry et al., 2012).

A related digital-ecology literature makes the substrate claim even more explicitly by locating robustness, scalability, and self-organization in abstract mechanisms—selection acting on variation in a spatially structured, dynamically changing environment with local interactions—rather than in carbon chemistry or any specific embodiment (0712.4153). This suggests that substrate-agnostic ecology functions both as a philosophical abstraction and as a program for identifying invariant ecological relations across different material, informational, and chemical realizations.

2. Theoretical foundations

The core theoretical foundation is niche construction. Organisms modify selective environments, and those modified environments reciprocally shape the subsequent evolution, behavior, and organization of the organisms. In this formulation, neither agent nor environment is ontologically prior; they are co-constitutive. Ecological inheritance then names the persistence of modified environments across generations, so that what is transmitted is environmental information and structure—such as atmospheric composition, surface morphology, or resource distributions—rather than any particular biochemistry or artifact class (Likavčan, 2 Jul 2026).

Stigmergy supplies the communicative and coordinative mechanism. Agents “write” traces into a shared medium and later agents “read” and respond to those traces. At planetary scales, atmospheric, geological, and chemical modifications become stigmergic traces of the same general type as those sought in biosignature and technosignature searches. Agency, in this framework, is defined ecologically as the capacity to pursue goals by responding to environmental affordances; that definition is substrate-agnostic because it requires agents, environments, and structured relationships, not particular materials or metabolic families (Likavčan, 2 Jul 2026).

Prebiotic and autocatalytic systems extend this foundation below the level of templated genetics. Spatially structured autocatalytic chemical ecosystems exhibit competition, priority effects, facilitation, niche partitioning, and spatial refugia through autocatalysis, reaction–diffusion dynamics, adsorption, and desorption. The ecological vocabulary therefore applies before canonical organismality, provided there are self-amplifying cycles, shared resources, waste products, and finite dispersal (Plum et al., 2022). A plausible implication is that substrate-agnostic ecology is best understood as a theory of organization under feedback, inheritance, and spatial constraint, rather than as a theory restricted to already-recognized living systems.

3. Biosignatures, technosignatures, and ecological diagnostics

The diagnostic ambition of substrate-agnostic ecology is to replace lists of expected molecules or artifacts with criteria based on feedback, persistence, complexity, and structured environmental modification. One conceptual decision framework asks whether there is a detectable environmental modification departing from expected abiotic baselines, whether it persists and exhibits structured dynamics consistent with stigmergic coordination, whether reciprocal feedback is present, and whether complexity or assembly-depth measures support a historically layered process. The same framework recommends time-series multispectral data for statistical complexity analysis via epsilon machines, assembly index as a measure of temporal depth, differential abiotic modeling, and cross-scale coherence across atmospheric, geological, and thermal signatures (Likavčan, 2 Jul 2026).

One concrete biosignature proposal is energy-ordered resource stratification. In a 1D reaction–diffusion consumer–resource model with species abundances $N_i(x,t)$ and resource concentrations $R_\alpha(x,t)$, the relevant ecological conditions are self-replication ($\gamma \neq 0$), antagonistic interactions ($\rho > 0$), and spatially inhomogeneous resource supply. Under these conditions, higher-energy resources are depleted nearer the supply source and lower-energy resources penetrate deeper, and the resulting ordering can be quantified by the stratification order parameter $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$. Simulations identify self-replication and ecology as jointly necessary and sufficient, show robustness under cross-feeding and Monod kinetics, and report loss of stratification beyond generalism $\Delta \approx 0.4$ (Goyal et al., 2024).

A different agnostic signature is the monomer abundance distribution biosignature. In Avida, the abiotic baseline is the mutational substitution distribution $q=\{q_i\}$, whereas the evolved biotic distribution is $p=\{p_i\}$. Selection biases remain robust even when mutational availability is strongly distorted: in the altered spectra experiments, frequent instructions appeared up to $30\times$ more often than rare ones, yet evolved populations still enriched GET, PUT, and NAND and depleted RETURN and JUMP-F relative to abiotic supply (Dorn et al., 2011). The general logic is that adaptive utility rather than formation cost governs component usage in evolving systems.

A further line of work characterizes ecological organization in chemical space by elemental stoichiometry. Across 11,834 metagenomes, Environmental Microbial Space is enriched in heteroatoms such as P, S, N, and O relative to C and shifted toward higher O:C and H:C ratios. At the system level, element counts follow $y_i = k_i S^{\beta_i}$, with strongly sublinear exponents in the microbial datasets—$R_\alpha(x,t)$0 [0.869, 0.871], $R_\alpha(x,t)$1 [0.955, 0.959], $R_\alpha(x,t)$2 [0.901, 0.903], and $R_\alpha(x,t)$3 [0.913, 0.915]—whereas the Reaxys synthetic-chemistry reference is linear for S, N, and O and weakly superlinear for P (Vergeli et al., 19 May 2026). In that sense, substrate-agnostic ecology can be operationalized as a statistical signature of how life-like systems occupy chemical space, rather than as a catalog of specific biomolecules.

4. Spatial structure, seclusion, and habitat formation

A central mechanistic strand of substrate-agnostic ecology concerns the ecological consequences of spatial structure. In jammed microbial populations, protected microhabitats can emerge dynamically without hard barriers. These are regions in which local flow and crowding prevent invasion: cells at the margins experience an outward-directed velocity field that keeps competitors from penetrating, while direct molecular exchange across the interface remains possible. In smooth cavities, two-strain competition reduces to the logistic takeover equation

$R_\alpha(x,t)$4

but defects, tilted walls, and wall friction can reshape the pressure field so that the interstrain boundary is pinned at $R_\alpha(x,t)$5, suppressing effective selection even when $R_\alpha(x,t)$6 (Slepukhin et al., 27 Mar 2025).

The same work provides explicit coexistence criteria. For a circular defect on the floor of width-$R_\alpha(x,t)$7 cavity, coexistence requires

$R_\alpha(x,t)$8

where $R_\alpha(x,t)$9; with friction, the relevant dimensionless coupling is $\gamma \neq 0$0, and increasing wall friction strengthens inward transverse flux and boundary pinning. In the jammed state, $\gamma \neq 0$1 and the advective Péclet number $\gamma \neq 0$2, so lineage mixing becomes negligible and interfaces persist (Slepukhin et al., 27 Mar 2025).

Prebiotic autocatalytic chemical ecosystems show closely related spatial effects. In well-mixed conditions, mutually inhibiting autocatalytic cycles are bistable and one excludes the other. In open 2D reaction–diffusion arrays and on adsorptive mineral surfaces, spatial structure permits otherwise mutually exclusive cycles to coexist and opens new selective axes, especially diffusivity. Low diffusion yields low local diversity but high global diversity; high diffusion collapses to global exclusion; intermediate diffusion maximizes local diversity, with patch boundaries acting as chemically productive interaction zones (Plum et al., 2022).

Habitat theory supplies a more abstract spatial formalism. In Hiebeler’s correlated percolation model, habitat is a binary field $\gamma \neq 0$3 with suitable-site density $\gamma \neq 0$4 and nearest-neighbor aggregation parameter

$\gamma \neq 0$5

The uncorrelated case is $\gamma \neq 0$6, with square-lattice threshold $\gamma \neq 0$7. At $\gamma \neq 0$8, the reported percolation threshold is $\gamma \neq 0$9 (also reported as $\rho > 0$0), and the measured correlation function frequently satisfies $\rho > 0$1 over wide parameter regions. Yet the same study shows that the long-range correlation structure depends on the algorithm used to realize a given $\rho > 0$2, which calls into question the idea of a uniquely defined model of aggregated habitat (Huth et al., 2014).

5. Digital, software, and semantic ecologies

Digital-ecology research treats ecological organization as a substrate-independent consequence of reproduction, variation, interaction, movement, and death. In one decentralized peer-to-peer architecture, habitats host local evolving populations of agents in response to user requests; migration occurs along network links; and connectivity adapts by Hebbian learning, so that successful exchanges strengthen links and unsuccessful ones weaken them. The resulting topology tends toward a clustered small-world structure. Using ecological measures, the system exhibits a species–area power law and a succession-like improvement in responsiveness: after 1000 requests, agent-sequences reached near 70% effectiveness in only ten generations, on average (0712.4153).

Ecology-inspired software engineering pushes the same analogy into system design. Biodiversity maps to managed and automated diversity of components; trophic webs map to producer–consumer and layered service architectures; refuges map to low-usage but preserved projects and forks. The framework proposes ecological metrics for software, including Shannon entropy

$\rho > 0$3

Simpson’s index $\rho > 0$4, Jacobian-based stability criteria, algebraic connectivity $\rho > 0$5, and Ashby’s law of requisite variety $\rho > 0$6 (Baudry et al., 2012). Its empirical refuge example examined the 48 most-forked GitHub projects, totaling 36,746 projects, and identified Janus, Sinatra, and Delayed_job as cases where low-success parents seeded highly successful descendants, with 533 vs 5 direct forks, 341 vs 66, and 315 vs 181, respectively (Baudry et al., 2012).

A more recent semantic instantiation is the evolutionary ecology of words. Agents occupy a 2D toroidal grid, each agent carries a word or short phrase, interactions occur when neighbors meet, and an LLM decides competition through prompts such as “Which one is stronger ‘#word1#’ or ‘#word2#’?”. The loser’s word is overwritten by the winner’s, and mutation queries generate semantically related animal names. Across ten 300-step trials on a $\rho > 0$7 grid with $\rho > 0$8, populations produced more than 100 up to more than 300 species per trial; in a 2,000-step run with $\rho > 0$9 and $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$0, 3,704 unique species emerged, with punctuated shifts among marine giants, poisonous taxa, extinct megafauna, and parasites (Suzuki et al., 9 May 2025). This suggests that spatial locality, replacement, and open-ended mutation can generate recognizable ecological dynamics even when the substrate is purely linguistic.

6. Functional universality, modularity, and multi-level organization

Another major strand of substrate-agnostic ecology seeks invariant aggregate organization even when microscopic composition varies. In slow-growing, energy-limited, anoxic microbial communities, reversible reactions with product inhibition drive convergence of community metabolic networks independent of species composition and biochemical details. The governing principle is maximum heat dissipation: among feasible competing paths from a supplied resource $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$1 to downstream resources, the path with larger cumulative dissipation $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$2 wins, yielding a maximally dissipative arborescence. Steady-state resource concentrations take a Boltzmann-like form,

$S = -\mathrm{corr}(Y_\alpha,d_\alpha)$3

so leading-order predictions depend on thermodynamics and dilution rather than on enzyme budgets or taxonomic identity (George et al., 2022). At very slow growth, around 1% of $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$4, the principle correctly predicted all five active reactions in the reported simulations; at roughly 30% of $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$5, only 2/5 reactions matched, indicating the breakdown of universality as kinetics reassert importance (George et al., 2022).

Genome-scale metabolic modeling reaches a related conclusion from another direction. In sampled networks viable on many environments, modularity is defined by fully coupled sets of reactions, with pairwise full coupling tested by linear programs constrained by $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$6 and flux bounds. For reaction-set sizes $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$7, both the number of reactions in modules $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$8 and the number of modules $S = -\mathrm{corr}(Y_\alpha,d_\alpha)$9 increase monotonically with environmental versatility $\Delta \approx 0.4$0. In a sample of 1000 genotypes with $\Delta \approx 0.4$1 viable on all 89 environments, the modularity measure $\Delta \approx 0.4$2 of E. coli lay in the top 3% of the distribution ($\Delta \approx 0.4$3), and the versatility-linked reactions were significantly closer to nutrient uptake than the global reaction set (Samal et al., 2011). The ecological implication is that substrate-generalist metabolisms tend to acquire modular catabolic branches feeding a shared biosynthetic core.

Multi-level selection models extend substrate-agnostic ecology to role differentiation in common-pool systems. In one coupled resource ecology, agents exploit a positive-sum intake channel (“grazing”) and a zero-sum redistribution channel (“exchange”) through the same behavioral primitives. Group-level selection optimizes a shared CTRNN substrate and a learned mutation operator, while birth and death occur under individual-level viability thresholds. The learned ecology maintained occupancy of both channels at the colony level, avoided collapse into a single acquisition mode, and showed increasing zero-sum channel usage over generations despite exchange not being directly optimized in the group fitness (Chaturvedi et al., 1 Apr 2026). Most baseline performance was carried by the inherited behavioral basis, while the learned variation process supplied a smaller but systematic improvement prior to saturation (Chaturvedi et al., 1 Apr 2026). Taken together, these strands argue that aggregate ecological regularities can become predictable even when lower-level composition, identity, or embodiment remains variable.

7. Ambiguities, limits, and open problems

The literature is heterogeneous in both aim and level of formality. The niche-construction-based articulation is explicitly conceptual and provides no equations or quantitative thresholds; it also emphasizes that contextual information may be insufficient to resolve whether a trace is “biological” or “technological,” and that in many cases the distinction is irrelevant or undecidable (Likavčan, 2 Jul 2026). Remote operationalization remains uneven: assembly index is highlighted as promising but difficult to operationalize for remote observations, and energy-ordered resource stratification is described as likely inaccessible to remote sensing and more relevant to sample return or in situ profiling (Likavčan, 2 Jul 2026, Goyal et al., 2024).

Several models also have sharp domain restrictions. The microbial self-organized seclusion framework assumes non-motile strains, a 2D projection, density-dependent $\Delta \approx 0.4$4 and $\Delta \approx 0.4$5, an incompressible jammed phase, simplified friction via $\Delta \approx 0.4$6, no explicit EPS viscoelasticity, no chemotaxis, simplified nutrient heterogeneity, and no rod-shape disorder at jamming (Slepukhin et al., 27 Mar 2025). The thermodynamic universality of slow-growing communities weakens in faster, far-from-equilibrium, oxic, or high-energy regimes, where enzyme kinetics, saturation, and species-specific traits dominate (George et al., 2022). Correlated-percolation habitat models show that emergent structural properties, especially $\Delta \approx 0.4$7, are algorithm-dependent; $\Delta \approx 0.4$8 therefore do not uniquely specify a habitat, and the notion of a well-defined aggregated-habitat model becomes unstable (Huth et al., 2014).

Planetary and chemical applications add methodological ambiguity. The elemental-stoichiometry framework shows that datasets from planetary mission molecules occupy statistically distinct regions from both terrestrial biological and Reaxys distributions, but it also stresses that standardized methods for data collection are required. In the Bennu comparison, no P was detected by the reported methods, and the study cautions that such cross-dataset comparisons are not definitive because analytical methodologies differ (Vergeli et al., 19 May 2026). More broadly, the literature repeatedly warns against terrestrial bias: one should not universalize CHNOPS chemistry, human artifact types, or familiar engineering tropes without grounding inference in ecological function, persistence, feedback, and structure (Likavčan, 2 Jul 2026).

Substrate-agnostic ecology therefore remains less a single closed theory than a research program. Its unifying claim is not that all substrates are equivalent, but that ecological explanation can be anchored in relations—feedback, inheritance, spatial structure, dissipation, coordination, and constraint—that recur across microbial cavities, autocatalytic chemistries, planetary atmospheres, service architectures, and semantic populations. This suggests a continuing shift from substrate-specific taxonomies toward function-first ecological diagnostics and models, while leaving the question of embodiment open rather than ignored.