Phytobiome Communication Networks

• Phytobiome communication is a multi-scale signaling network that integrates molecular, electrical, and acoustic pathways for intra-plant and cross-kingdom interactions.
• The framework employs quantitative models, using metrics like mutual information to assess signal fidelity, propagation speed, and network topology across diverse media.
• Engineered applications leverage these insights in smart agriculture, utilizing plant sensing, IoT, and AI to optimize stress response and resource management.

Searching arXiv for recent and relevant papers on phytobiome communication and related plant communication modalities. arxiv_search(query="phytobiome communication plant molecular communication VOC signaling mycorrhizal electrical communication", max_results=10, sort_by="relevance") arxiv_search(query="phytobiome communication plant molecular communication VOC signaling mycorrhizal electrical communication", max_results=10, sort_by="relevance") {"query":"phytobiome communication plant molecular communication VOC signaling mycorrhizal electrical communication","max_results":10,"sort_by":"relevance"} Phytobiome communication denotes the exchange of information within and around plants across the phytobiome—the plant, its environment, and the organisms within and around the plant—through molecular and electrophysiological signaling, and, in broader communication-theoretic treatments, through mycorrhizal and acoustic modalities as well (Gulec et al., 5 Aug 2025). In this literature, communication is explicitly multi-scale: it includes intracellular and intercellular signaling within plant tissues, plant–plant signaling through volatiles and common mycorrhizal networks, plant–microbe and plant–animal interactions, and larger ecological or field-level exchanges among phytobiomes (Kilic et al., 10 Sep 2025).

1. Scope and conceptual framing

Phytobiome communication is not restricted to one carrier, one scale, or one ecological relationship. Recent work treats the phytobiome as a communication network in which cells, tissues, plants, microbes, fungi, insects, and engineered nanomachines can all be mapped to transmitters, receivers, relays, or channels, depending on scale and context (Gulec et al., 5 Aug 2025). In parallel, ICT-oriented tutorials often narrow the biological scope to inter-plant signaling while still acknowledging that air, soil, microbial activity, and fungal symbionts materially shape communication reliability, latency, and interference (Kilic et al., 10 Sep 2025).

A common source of ambiguity is the boundary between “plant communication” and “phytobiome communication.” The former often refers to signaling among plant cells or among whole plants. The latter is broader: it includes cross-kingdom interactions and ecological interfaces such as common mycorrhizal networks, residue-associated microbiomes, and sediment-centered marine plant holobiont-like systems. This broader usage also permits communication to include direct signaling molecules, electrophysiological events, and host-driven habitat modification that reorganizes associated communities (Kerdraon et al., 2019).

Scale or domain	Main carriers and channels	Representative formalizations
Intra-plant	Electrochemical signals, diffusing molecules, xylem ABA	Photosynthesis-linked reaction–diffusion channel (Awan et al., 2021); AP and mechanosensitive signaling (Awan et al., 2019); ABA vascular transport (Erkek et al., 15 Jun 2026)
Airborne and inter-organismal	BVOCs, GLVs, plant–insect VOC blends	End-to-end BVOC stress communication (Kilic et al., 2024); GLV alarm model (Merdan et al., 18 Feb 2026); interspecies VOC information theory (Maitra et al., 7 Feb 2026)
Underground and ecological	CMNs, residue ecotones, sediment redox networks	Mycorrhizal/agent-plant architecture (Bilgen et al., 2024); residue ecotone/pathobiome (Kerdraon et al., 2019); seagrass symbiotic networks (Miyamoto et al., 17 Nov 2025)

2. Internal plant signaling as a foundational layer

A recurrent result in this literature is that phytobiome communication rests on plant-internal information transfer. One mathematically explicit formulation models a transmitter plant cell that senses sunlight intensity, undergoes a membrane-potential change, releases signaling molecules $P$, and drives output molecules $X$ in neighboring receiver cells, with photosynthate production $p_h$ coupled to the availability of $n_X(t)$ (Awan et al., 2021). In that framework, stronger and more reliable inter-cellular molecular communication—quantified through mutual information between input $U(t)$ and output $n_X(t)$—is associated with increased photosynthetic output, and simulations suggest modulation of photosynthesis by as much as 25 per cent. The core information-theoretic expression is

$$I(n_{X},U) = \frac{1}{2} \int \log \left( 1+\frac{ | \Psi(\omega) |^2}{\Phi_{\eta}(\omega)} \Phi_u(\omega) \right) d\omega,$$

where $\Psi(\omega)$ is the channel gain and $\Phi_\eta(\omega)$ is the stationary noise spectrum. Within that model, series-connected receiver configurations outperform parallel and mixed configurations because fewer signaling molecules are effectively lost.

Electrical and mechanosensitive signaling provide a second foundational layer. A communication-theoretic comparison of multiple action potentials and mechanosensitive activation signals models plant tissues as voxelized cell networks with ligand-binding receivers and evaluates mutual information and information propagation speed across up to 100 receiver cells (Awan et al., 2019). For both signal classes, mutual information per cell and propagation speed increase up to about 10 to 12 cells and then cease to improve; beyond that range, mutual information per cell decreases because total mutual information plateaus while cell number continues to rise. Experimental measurements with a PhytlSigns biosignal amplifier in Mimosa pudica and Aloe Vera were used to compare voltage traces, mutual information, and propagation speed with the theoretical model.

Long-distance hormonal transport has been recast in the same language. A molecular-communication-inspired ABA model maps root-side ABA release to a transmitter, the xylem to a bounded cylindrical channel, and soybean tissue to a spherical receiver (Erkek et al., 15 Jun 2026). Brownian-motion simulations show that higher released molecule quantities $Q=10^4,10^5,10^6,10^7$ produce smoother and stronger reception trends, and that larger receivers increase molecule-capture probability. The same paper also makes explicit that the diffusion-only abstraction yields time scales from $X$0 to $X$1 s in the plotted simulations, which indicates that the model is conceptual rather than a realistic physiological simulator of xylem transport.

3. Airborne volatile communication

Airborne volatile signaling is the most fully developed inter-plant communication modality in current communication-theoretic work. An end-to-end mathematical model of stress communication between plants represents stress as an input $X$2, approximates it by a polynomial, converts it into a BVOC production rate through a nonlinear gene-regulation model, propagates the emitted BVOCs through an advection–diffusion air channel, and decodes the message by thresholding accumulated leaf concentration at a receiver plant (Kilic et al., 2024). Using literature-derived emission data, the model reports $X$3 for herbivory-related total LOX products and monoterpenes, $X$4 for wound-induced methanol emission, and $X$5 for heat-stress LOX emission. The same study also proposes Ratio Shift Keying, in which information is encoded in BVOC blend ratios rather than concentration alone, and argues that this both enables a multiple access channel and prevents competitor plants from obtaining the information.

A more biochemically detailed plant-to-plant model uses green leaf volatiles—(Z)-3-hexenal, (Z)-3-hexenol, and (Z)-3-hexenyl acetate—as transmitter outputs and a sink-plant biochemical receiver that converts them to the defense metabolite (Z)-3-hexenyl $X$6-vicianoside (HEXVic) (Merdan et al., 18 Feb 2026). The atmospheric channel is time-varying diffusion–advection,

$X$7

and the receiver is a cascade of Michaelis–Menten reactions leading to a threshold decision, “Alarm if $X$8,” with $X$9. The principal numerical conclusion is that (Z)-3-hexenol is the primary driver of the system, while biologically meaningful alarm perception generally occurs in a nonlinear regime.

Interspecies volatile communication has also been formalized at the receptor-statistics level. A plant–insect framework represents symbols as VOC blend vectors, propagates them through airborne advection–diffusion, and models cross-reactive olfactory receptors through discretized binding-duration histograms with multinomial observations (Maitra et al., 7 Feb 2026). Its asymptotic capacity analysis shows that communication depends on wind speed, distance, and released molecule number, but not monotonically: the paper explicitly concludes that “faster transport does not imply better communication.” This is important for phytobiome communication because it shifts volatile signaling from a qualitative ecological cue to a noisy, structured, blend-based information channel.

4. Underground, fungal, and ecological network communication

Underground phytobiome communication is strongly shaped by fungal symbiosis and by ecological boundary habitats. A plant–mycorrhizal framework identifies three direct plant communication modalities—electrical, acoustic, and VOC-based—and then gives a more specific signaling pathway for symbiosis initiation: plant roots release strigolactones, fungal partners release Myc factors, and plant perception of Myc factors triggers calcium oscillations that support colonization (Bilgen et al., 2024). The same paper presents common mycorrhizal networks as both resource-sharing and information networks, supporting transfer of carbon, nitrogen, phosphorus, water, and stress signals related to pathogen attack or herbivory.

More formal network abstractions treat mycorrhizal signaling as a shared backbone. In ICT-oriented treatments, plant-to-fungus and fungus-to-plant interfaces are written as saturating fluxes, while the common mycorrhizal network itself can be represented by a graph-Laplacian dynamic,

$p_h$0

with the Fiedler eigenvalue controlling mixing times and latency (Kilic et al., 10 Sep 2025). The same literature is explicit that the mechanism remains unresolved: fungi may be passive conduits, active processors, or biological intermediaries that amplify or modify signals.

Ecological phytobiome communication extends beyond direct molecule exchange. In cereal systems, crop residues are described as a transient “half-plant/half-soil” compartment and an ecotone linking phyllosphere, endosphere, detritusphere, and bulk soil microbiomes (Kerdraon et al., 2019). This framing makes residue microbiomes relevant not only to decomposition but also to pathogen survival, inoculum production, and microbiome-based biocontrol. In marine systems, a sediment-centered seagrass phytobiome has been analyzed as a symbiotic causal network in which healthy seagrass habitats are associated with positive coupling to Desulfobulbaceae, Hyphomonadaceae, and Corallinophycidae, negative association with Diatomea, activation of nitrogen-related metabolism, and attenuation of methanogenesis (Miyamoto et al., 17 Nov 2025). In that setting, communication is often inferred through root oxygen loss, redox coupling, sulfur and nitrogen transformations, and biofilm formation rather than through direct identification of a single signaling molecule. This suggests that phytobiome communication can include host-driven environmental structuring that reorganizes entire microbial and eukaryotic assemblages.

5. Quantitative frameworks, inference, and sensing

Quantification of phytobiome communication has proceeded along three complementary lines. The first is a multi-scale communication-network framework that separates microscale, mesoscale, and macroscale communication. In this view, plant cells, microbial cells, fungi, and whole plants become transceivers embedded in channels such as plasmodesmata, xylem, phloem, soil, air, and fungal networks; reliability is discussed in terms of mutual information, and propagation speed is defined by the time at which mutual information crosses a threshold (Gulec et al., 5 Aug 2025). The same framework has been connected to electrophysiological sensing through Mimosa pudica measurements, with 50 Hz notch filtering used to reduce power-grid interference.

The second line is transkingdom network inference. TransNet integrates matched multi-omics layers—genes, microbes, metabolites, proteins, pathways, and related features—into a single cross-kingdom graph and filters it using consistency tests, Fisher-combined $p_h$1-values, the proportion of unexpected correlations (PUC), module extraction, and bipartite betweenness centrality (Rodrigues et al., 2017). Although demonstrated in mammalian host–microbiota systems, the framework is explicitly relevant to phytobiome communication because it prioritizes putative causal mediators of cross-kingdom interactions rather than merely listing differential plant genes and differential microbial taxa. The method remains hypothesis-generating until perturbation experiments establish causality.

The third line is quantitative phytoacoustics. An end-to-end acoustic communication framework models water-flow-generated sound in soil as a transmitter, the soil as a damped viscoelastic channel, root cell walls and MCA2-like mechanosensitive channels as the receiver front end, and a $p_h$2-ROS-auxin cascade as the decoder and actuator (Merdan et al., 30 Nov 2025). The main reported result is that a $p_h$3 Hz, $p_h$4 stimulus elevates cytosolic $p_h$5 from $p_h$6 nM to $p_h$7 nM within $p_h$8 seconds, which can cause root bending in the long run. The same work introduces a BER-style decision rule based on the Activated PIN2 Ratio, treating root sound perception as a communication process rather than as an unspecified environmental effect.

6. Engineering, controversies, and future directions

Engineering-oriented work recasts phytobiome communication as a controllable substrate for smart agriculture. One proposed agenda integrates plant electrophysiological sensing, ML/AI, IoT, and the Internet of Bio-Nano-Things to support early stress detection, smart irrigation, targeted agrochemical delivery, gene delivery, and communication engineering with genetically modified bacteria, synthetic VOCs, and synthetic fungi (Gulec et al., 5 Aug 2025). In related Internet-of-Plants formulations, plants are treated as interconnected nodes within ecological and technological networks, and inter-plant signaling modalities are analyzed as channels with latency, interference, memory, and network topology (Kilic et al., 10 Sep 2025). A more speculative extension is the “agent plant” concept, in which engineered plant-like nodes communicate electrically, acoustically, and molecularly with crops, exchange materials through underground highways, and harvest energy by a mycorrhiza-inspired glucose-extraction architecture (Bilgen et al., 2024).

At the same time, the literature is explicit about major limitations and controversies. Several central models remain theoretical and computational rather than experimentally established, including the photosynthesis-modulation framework based on inter-cellular mutual information (Awan et al., 2021). Some abstractions are intentionally coarse: the ABA xylem model omits advection, degradation, active transporters, and realistic timescales; the plant–insect VOC framework uses a small receptor set and an asymptotic capacity benchmark; the GLV receiver model aggregates parameters across multiple species; and the acoustic model assumes, rather than establishes, a primary role for MCA2 in plant hearing (Erkek et al., 15 Jun 2026). More broadly, current tutorials emphasize that multimodal field conditions, microbial degradation, co-channel interference, and the distinction between true communication and cue exploitation remain unresolved, especially for belowground and mycorrhizal signaling and for plant-to-plant acoustic communication (Kilic et al., 10 Sep 2025).

Taken together, this body of work defines phytobiome communication as a layered signaling regime in which plants act simultaneously as information sources, processors, and ecological infrastructure. Internal electrochemical and hormonal transport, airborne volatile exchange, fungal-network-mediated signaling, residue and sediment community restructuring, and emerging acoustic pathways can all be formalized as communication systems. The unifying implication is not that all plant-associated interactions reduce to a single channel model, but that the phytobiome can be analyzed as a nested information-processing network whose carriers, topologies, and inferential limits are increasingly being made explicit.