THE INFORMATIONAL ECOSYSTEM:
Overlapping Organismal Realities in a Shared Physical Environment
DOI: [to be assigned]
John Swygert
September 3, 2026
Abstract
An ecosystem is conventionally understood through interactions among organisms, energy, matter, resources, populations, and physical environmental conditions. Yet every ecological interaction also depends upon information. Organisms detect only selected portions of their physical environments, produce signals and environmental modifications detectable by other organisms, and respond according to biological architectures that differ across species, individuals, developmental states, and circumstances.
This paper proposes the informational ecosystem as a framework for representing an ecosystem as a superposition of partially overlapping organismal realities within a shared physical environment. The framework begins from a distinction established by comparative sensory biology: organisms occupying the same physical location need not possess access to the same environmental information. Ultraviolet reflectance may be available to one organism but absent from another's visual system. Weak electrical fields, magnetic-field properties, chemical gradients, substrate vibrations, hydrodynamic disturbances, acoustic signals, temperature differences, moisture gradients, molecular signals, and other environmental properties similarly differ in biological accessibility.
Let \(E\) represent a shared physical environment and let \(R_i=\Phi_i(E)\) represent the biologically accessible informational reality of organism \(i\). An ecosystem containing \(n\) organisms can then be represented not by a single perceptual environment but by a family of overlapping informational mappings:
\[ \mathcal{R}(E)=\{R_1,R_2,\ldots,R_n\}. \]
The intersections among these mappings identify shared informational domains. Their differences identify informational asymmetries. Their union approximates the portion of the environment accessible to the biological community as a whole, while remaining a subset of the complete physical environment.
This framework has implications for predator-prey interactions, pollination, symbiosis, parasitism, communication, camouflage, migration, microbial ecology, plant-fungal relationships, conservation, environmental disturbance, artificial intelligence, and the simulation of ecosystems. It also suggests a new experimental program: construct a common measured environment, model the informational access of multiple organisms within it, superimpose those models, and determine whether ecological interactions become more intelligible when analyzed as exchanges among overlapping biological realities.
An ecosystem is therefore not only a network through which matter and energy move.
It is also a network through which detectable differences become biological information.
1. Introduction
A forest contains trees, animals, fungi, microorganisms, soil, water, atmosphere, electromagnetic radiation, chemicals, sound, temperature gradients, pressure differences, mechanical forces, electrical activity, magnetic fields, and countless interactions among them.
Humans entering that forest perceive only a fraction of this structure.
A bird may detect visual and acoustic information unavailable or differently resolved by a human.
An insect may detect wavelengths and polarization patterns unavailable to unaided human vision.
A spider may receive detailed vibrational information through a web.
A plant may respond to light direction, photoperiod, water status, mechanical disturbance, neighboring organisms, herbivore damage, and chemical information.
A fungus may encounter the same location primarily through chemical, nutritional, mechanical, and moisture gradients.
A microorganism may inhabit a chemical landscape existing at spatial and temporal scales largely inaccessible to unaided human perception.
These organisms occupy one ecosystem.
They do not occupy one identical informational representation of that ecosystem.
This observation suggests that ecological systems can be analyzed not merely as collections of organisms interacting within a physical environment, but as collections of partially overlapping organismal realities.
The central proposition of this paper is:
An ecosystem contains one shared physical environment but many biologically accessible informational environments. Ecological interactions occur where those informational environments intersect, influence one another, or remain asymmetrical.
2. The Shared Physical Environment
Let the physical state of an ecosystem be represented as:
\[ E(x,t) \]
where \(x\) represents location and \(t\) represents time.
The environment contains numerous physical and chemical variables:
\[ E=\{L,A,C,T,P,M,F,V,Q,G,H,N,\ldots\} \]
where, for example:
\(L\) = electromagnetic radiation,
\(A\) = acoustic energy,
\(C\) = chemical composition,
\(T\) = temperature,
\(P\) = pressure,
\(M\) = magnetic-field properties,
\(F\) = fluid movement,
\(V\) = mechanical vibration,
\(Q\) = electrical properties,
\(G\) = gravitational information,
\(H\) = humidity or water availability,
\(N\) = nutrient distributions.
This environmental state exists independently of whether any particular organism detects every component.
No known organism has unrestricted access to \(E\).
Each organism instead interacts with a biologically filtered subset.
3. Organismal Reality Within the Ecosystem
For organism \(i\), define a biological accessibility transformation:
\[ \Phi_i. \]
Its biologically accessible environment is:
\[ R_i=\Phi_i(E). \]
Two organisms occupying the same physical coordinates may therefore satisfy:
\[ E_i=E_j \]
while:
\[ R_i\neq R_j. \]
This does not imply multiple physical universes.
It describes multiple biological mappings of one physical universe.
An ecosystem containing \(n\) organisms therefore contains:
\[ R_1,R_2,R_3,\ldots,R_n. \]
The complete set can be represented as:
\[ \mathcal{R}(E)=\{R_i\}_{i=1}^{n}. \]
This is the informational ecosystem.
4. Superposition Without Multiplying Reality
The term superposition is used here in a representational and comparative sense, not in the quantum-mechanical sense.
The organismal mappings can be placed upon a common physical coordinate system because they originate from the same environment.
For example:
\[ R_{\text{human}} \] \[ R_{\text{bee}} \] \[ R_{\text{spider}} \] \[ R_{\text{tree}} \] \[ R_{\text{fungus}} \] \[ R_{\text{bacterium}} \]
can be mapped onto the same square meter of forest.
The resulting layers expose information that is:
jointly accessible,
accessible to only one organism,
accessible to particular groups,
generated by one organism and detected by another,
or physically present but inaccessible to all organisms represented.
Thus superposition does not claim that realities physically coexist as separate worlds.
It is a method for comparing different biological access functions within the same world.
5. Informational Intersection
For organisms \(A\) and \(B\), consider:
\[ R_A\cap R_B. \]
This represents environmental information accessible to both organisms, subject to differences in resolution, threshold, timing, and biological interpretation.
The intersection can enable interaction.
A predator and prey may both detect movement.
Two animals may hear the same sound.
A pollinator and flowering plant may participate in an interaction involving signals generated by one and detected by the other.
Two microorganisms may respond to the same chemical environment.
However, shared detection does not imply identical representation.
Both organisms may detect a physical variable while responding to different ranges or aspects of it.
Therefore informational intersection should eventually be represented quantitatively rather than simply as present or absent.
6. Informational Asymmetry
Perhaps even more important is:
\[ R_A-R_B. \]
This represents information accessible to organism \(A\) but unavailable to organism \(B\).
Ecology contains enormous numbers of such asymmetries.
A predator may detect prey through a sensory channel unavailable to the prey.
A prey organism may detect the predator earlier through another channel.
A parasite may respond to chemical information produced unintentionally by a host.
A flower may generate ultraviolet patterns detectable by a pollinator but invisible to a human observer.
A shark may detect electrical information from an organism concealed from ordinary vision.
These asymmetries can determine survival.
An informational ecosystem is therefore not merely a network of communication.
It is also a network of unequal access.
7. Detection Is Not Necessarily Communication
A crucial distinction must be maintained between signals and cues.
An organism can generate information without having evolved to communicate it.
Footsteps produce vibration.
Metabolism produces heat.
Respiration alters chemical conditions.
Movement displaces water.
Electrical activity generates fields.
Waste products alter chemistry.
An organism may exploit these effects even when the source organism receives no benefit from producing them.
Thus:
\[ \text{information available to receiver} \]
does not imply:
\[ \text{intentional or evolved communication}. \]
The informational ecosystem includes both deliberate biological signaling and incidental detectable consequences of existence.
8. Predator and Prey
Predator-prey relationships provide a clear example.
Let:
\[ R_P \]
represent the predator's informational reality and:
\[ R_Y \]
the prey's.
Successful predation can depend partly upon:
\[ R_P(Y) \]
—the information about prey accessible to the predator.
Successful avoidance can depend upon:
\[ R_Y(P) \]
—the information about the predator accessible to the prey.
These quantities need not be symmetrical.
The ecological contest can therefore be viewed partly as:
\[ R_P(Y)\quad \text{versus}\quad R_Y(P). \]
Camouflage reduces information available through particular channels.
Silence reduces acoustic information.
Remaining down-current can alter chemical detection.
Motionlessness may reduce visual or mechanosensory information.
Burial can remove visual information while leaving electrical or chemical information detectable.
Predation therefore becomes partly a competition over informational visibility.
9. Camouflage as Informational Manipulation
Camouflage is usually discussed visually because human observers are highly visual.
But the broader framework reveals camouflage as a general informational phenomenon.
An organism can reduce detectability by altering:
visual contrast,
odor,
sound,
vibration,
thermal signature,
electrical signature,
movement,
chemical release,
or environmental disturbance.
The relevant question becomes:
Camouflaged from whom, and through which informational channel?
An organism may be visually concealed while remaining chemically obvious.
It may be acoustically quiet while producing detectable substrate vibrations.
It may be thermally conspicuous in visual darkness.
There is therefore no universal camouflage.
There is only camouflage relative to a detector.
10. Pollination as Informational Intersection
Pollination provides a different relationship.
Flowers can present combinations of:
color,
ultraviolet patterning,
shape,
odor,
nectar,
temperature,
spatial arrangement,
and timing.
Pollinators possess corresponding detection capabilities.
The interaction therefore depends upon an intersection between plant-generated environmental structure and pollinator-accessible information.
This can be represented conceptually as:
\[ I_{FP}=S_F\cap R_P \]
where:
\(S_F\) represents flower-generated information,
\(R_P\) represents pollinator-accessible reality,
\(I_{FP}\) represents the usable informational intersection.
A floral property physically present but biologically inaccessible to the pollinator cannot influence pollinator behavior through that channel.
Thus ecological signaling requires both production and accessibility.
11. Plants as Information-Producing Organisms
Plants do not merely receive environmental information.
They continuously modify informational environments.
Flowers alter spectral and chemical structure.
Roots alter soil chemistry.
Leaves alter light distribution.
Plants release volatile compounds.
Growth modifies mechanical environments.
Water uptake changes local conditions.
Herbivore damage can alter plant chemistry and subsequent interactions.
A plant is therefore simultaneously:
\[ \text{detector} + \text{processor} + \text{environmental modifier}. \]
The same is true, in different forms, for virtually every organism.
12. Fungal Networks
Fungi introduce distributed informational relationships.
A mycelial system can occupy large, heterogeneous spatial regions.
Different portions encounter different:
nutrients,
moisture,
chemicals,
organisms,
temperatures,
physical barriers,
and resource conditions.
The resulting growth and resource-allocation patterns can change the surrounding environment.
In mycorrhizal associations, fungal and plant biology become tightly coupled through exchanges of resources and signals.
The informational ecosystem therefore cannot always be represented as isolated organisms exchanging discrete messages.
Some relationships are continuous, distributed, and spatially interwoven.
13. Microbial Informational Ecosystems
At microbial scales, information can become extraordinarily dense.
Microorganisms respond to:
nutrients,
oxygen,
pH,
temperature,
toxins,
metabolites,
signaling molecules,
neighboring organisms,
surfaces,
flow,
and other environmental variables.
Quorum sensing demonstrates that chemical information associated with population density can alter collective biological behavior.
Biofilms further transform the local physical and chemical environment.
A microscopic environment that appears homogeneous to human observation may therefore contain numerous overlapping microbial informational landscapes.
Scale changes what constitutes an ecologically meaningful difference.
14. Symbiosis as Coupled Informational Reality
Symbiotic relationships create especially interesting overlaps.
Let organisms \(A\) and \(B\) interact repeatedly.
Each modifies the environment:
\[ E(t)\rightarrow E'(t) \]
and therefore modifies the other's accessible reality:
\[ R_B(t)=\Phi_B[E(t)] \]
becomes:
\[ R_B(t+1)=\Phi_B[E'(t)]. \]
Organism \(B\) then acts upon the altered environment, changing what becomes available to \(A\).
This produces a coupled informational loop:
\[ A\rightarrow E\rightarrow B\rightarrow E'\rightarrow A. \]
The relationship is therefore dynamic.
Organisms do not merely perceive an ecosystem.
They continuously rewrite portions of the informational ecosystem for one another.
15. Host and Pathogen
Host-pathogen relationships provide another form of informational conflict.
A pathogen must encounter, recognize, enter, exploit, or reproduce within a host environment.
The host simultaneously detects molecular evidence associated with invasion and initiates defensive responses.
The relevant informational spaces occur at molecular and cellular scales rather than at the ordinary human perceptual scale.
Thus the informational ecosystem extends inward.
An organism can itself constitute an ecosystem containing numerous informational relationships among host cells, microorganisms, parasites, viruses, and molecular signaling systems.
The distinction between environment and organism becomes scale-dependent.
16. Informational Reality Across Scale
One reason human intuition struggles with organismal reality is that biological information operates across enormous ranges of scale.
Relevant structures may occur across:
molecular,
cellular,
organismal,
local environmental,
landscape,
oceanic,
atmospheric,
and planetary scales.
Geomagnetic information may contribute to migration over enormous distances.
A chemical gradient may guide a microorganism across microscopic distances.
Both can function as navigational information.
The important variable is not absolute physical scale.
It is the relationship between environmental structure and biological detection.
17. Time as an Informational Dimension
Organisms also occupy different temporal realities.
Some information changes in milliseconds.
Other biologically relevant patterns unfold over:
minutes,
hours,
days,
seasons,
years,
or generations.
A human observer may fail to recognize plant movement because it occurs slowly relative to ordinary human attention.
A microorganism may respond to chemical fluctuations occurring at scales humans do not naturally monitor.
Migratory organisms respond to seasonal environmental change.
Circadian systems extract information from repeating planetary cycles.
Therefore organismal reality must be modeled in:
\[ (x,t) \]
rather than spatial coordinates alone.
Different organisms effectively sample the temporal environment at different resolutions.
18. Internal State Changes External Meaning
The same environmental information can have different biological significance depending upon internal state.
Food odor matters differently when an animal is satiated.
Water availability matters differently depending upon hydration.
Light can have different consequences depending upon circadian phase.
Chemical gradients can change significance according to metabolic requirements.
Reproductive state can alter responses to signals.
Thus:
\[ R_i=\Phi_i(E,S_i,H_i) \]
where:
\(E\) = environment,
\(S_i\) = internal state,
\(H_i\) = relevant biological history.
The informational ecosystem is therefore not static even when the physical environment remains temporarily unchanged.
19. The Biological Union
For an ecosystem containing \(n\) organisms, define the union:
\[ R_{\cup}=\bigcup_{i=1}^{n}R_i. \]
This represents, conceptually, the total portion of the environment biologically accessible to at least one organism included in the model.
This union may be enormously richer than the informational reality of any individual organism.
However:
\[ R_{\cup}\subseteq E. \]
There may be physically real information that none of the organisms detects.
Indeed, even if every known organism on Earth were included:
\[ R_{\text{life}} = \bigcup_{i=1}^{N}R_i, \]
there is no reason to assume:
\[ R_{\text{life}}=E. \]
This distinction is fundamental.
Life samples reality.
Life does not necessarily exhaust reality.
20. The Known Biological Envelope
The union of all demonstrated biological detection systems can be conceptualized as a known biological informational envelope.
Within it lie all physical variables currently known to be biologically detected or used by known life.
Outside it may lie physically measurable phenomena for which no biological detector is known.
The boundary itself becomes scientifically interesting.
We can ask:
Which dimensions of physical reality has evolution made biologically accessible somewhere on Earth?
And conversely:
Which measurable dimensions appear biologically unused?
These questions transform biodiversity into a survey of naturally evolved information-detection technologies.
21. Science Beyond the Biological Envelope
Human scientific instruments extend access beyond ordinary human biology and potentially beyond the entire known biological envelope.
Let:
\[ T(E) \]
represent technologically detected physical information.
Then scientific access can include:
\[ R_{\text{science}} = R_H\cup T(E). \]
Radio astronomy, particle physics, spectroscopy, microscopy, gravitational-wave detection, magnetic measurement, thermal imaging, and numerous other technologies reveal domains inaccessible to unaided humans.
Thus the complete comparison becomes:
\[ R_H \subset R_{\text{life}} \subseteq E \]
while technology provides additional mappings from \(E\) into human-accessible representation.
The precise set relationships must be established empirically rather than assumed, but the conceptual architecture is useful.
22. Organisms as Natural Detectors
Every sensory or environmental-response system can be viewed as an evolved detector.
The bee demonstrates one solution for extracting spectral information.
The shark demonstrates another for electrical information.
The salmon demonstrates biologically useful responses to chemical and geomagnetic information.
The spider demonstrates vibration-based information acquisition.
Plants demonstrate molecular and physiological environmental detection without animal nervous systems.
Microorganisms demonstrate navigation through chemical landscapes.
Fungi demonstrate distributed responses within heterogeneous environments.
Biodiversity therefore represents, among many other things, a vast natural library of physical-information detectors.
23. Ecosystem Information Networks
An informational ecosystem can be represented as a network.
Let organisms be nodes:
\[ O_1,O_2,\ldots,O_n. \]
Let an edge:
\[ O_i\rightarrow O_j \]
exist whenever activity by organism \(i\) produces or modifies information detectable by organism \(j\).
The edge can be classified by modality:
visual,
chemical,
acoustic,
electrical,
mechanical,
thermal,
hydrodynamic,
magnetic,
molecular,
or multimodal.
Edges can also be:
intentional signals,
incidental cues,
beneficial,
harmful,
mutualistic,
deceptive,
or neutral.
The ecosystem therefore becomes a dynamic, multilayer informational network superimposed upon conventional ecological networks of matter and energy.
24. Information Can Be Deceptive
Once organisms respond to detectable information, evolution can favor manipulation of those responses.
Mimicry provides an obvious example.
An organism can generate information resembling another organism or environmental feature.
The receiver acts upon the information because its biological detection and interpretation architecture maps that pattern to a particular response.
Therefore:
\[ \text{detectable information} \neq \text{accurate inference}. \]
The informational ecosystem contains not merely signals and cues, but misdirection.
This parallels human perception.
Biological systems act upon accessible information, not upon omniscient knowledge of physical reality.
25. Environmental Disturbance as Informational Disturbance
Human environmental modification can alter ecosystems informationally even when organisms remain physically present.
Artificial light changes nocturnal visual environments.
Anthropogenic noise changes acoustic environments.
Chemical pollution modifies chemical landscapes.
Electromagnetic infrastructure may alter environmental fields.
Turbidity changes optical conditions.
Habitat fragmentation changes spatial cues and navigation.
Climate change alters temperature gradients, seasonal timing, currents, and other environmental signals.
Therefore conservation should sometimes ask not only:
Has the habitat physically survived?
but:
Has the informational environment required by its organisms survived?
A habitat can remain geographically present while becoming informationally degraded.
26. Informational Pollution
This suggests a broader category: informational pollution.
Informational pollution occurs when environmental modification interferes with biologically important information acquisition or produces misleading signals.
Examples can include:
artificial light obscuring natural light cues,
noise masking communication,
chemicals interfering with olfactory environments,
altered water conditions changing signal propagation,
human structures disrupting navigational cues.
The concept unifies environmental effects that are otherwise treated separately.
The common mechanism is:
\[ \text{environmental modification} \rightarrow \text{altered accessible information} \rightarrow \text{altered biological behavior}. \]
27. Simulating an Informational Ecosystem
The framework can be tested computationally.
First construct a shared physical environment:
\[ E(x,t). \]
Then construct organismal mappings:
\[ R_1=\Phi_1(E) \] \[ R_2=\Phi_2(E) \] \[ \vdots \] \[ R_n=\Phi_n(E). \]
Then simulate interactions among organisms.
Each organism or artificial agent receives only the information available through its own model.
No organism receives omniscient access to \(E\).
The simulation records:
detected information,
undetected information,
behavioral decisions,
signaling,
environmental modifications,
interactions,
survival,
movement,
reproduction,
and ecological consequences.
This creates an experimentally manipulable informational ecosystem.
28. Informational Ablation
Individual channels can then be removed.
For example:
\[ R_i^{(-M)} \]
could represent an organism model deprived of magnetic information.
Similarly:
\[ R_i^{(-C)} \]
could remove a chemical channel.
Researchers could ask whether ecological interactions change.
Does migration deteriorate?
Does predation success change?
Does pollination decline?
Does habitat selection change?
Does symbiosis destabilize?
The ecosystem-level consequences of sensory loss may reveal dependencies invisible when organisms are studied individually.
29. Informational Addition
The inverse experiment is equally interesting.
An organism model can be given access to a channel it does not biologically possess.
For example:
\[ R_i^{(+X)}. \]
Researchers could then ask:
What would this organism be capable of if this dimension of physical information became available to it?
This is not a claim about biological evolution.
It is a counterfactual experiment concerning informational constraint.
Such experiments could illuminate why particular sensory architectures confer advantages in particular ecological niches.
30. Artificial Intelligence Within the Informational Ecosystem
Artificial agents provide a powerful experimental tool because their informational access can be precisely controlled.
One agent can receive:
\[ R_A. \]
Another receives:
\[ R_B. \]
A third receives a novel combination:
\[ R_C=R_A\cup R_B. \]
The agents can then compete, cooperate, forage, migrate, or survive within the same environment.
This allows researchers to test whether informational differences alone can generate ecological advantages or behavioral patterns.
It also provides a way to investigate the value of sensory combinations that do not occur naturally.
31. The Human Observer Must Also Be a Layer
Humans should not occupy a privileged position within the informational ecosystem model.
The human observer should be represented as:
\[ R_H=\Phi_H(E). \]
A separate scientific instrumentation layer can then reveal:
\[ T(E). \]
This distinction prevents human perception from being confused with the underlying simulation.
The researcher can compare:
\[ R_H \]
with:
\[ R_i \]
and with:
\[ E. \]
Humanity thereby becomes one organismal perspective among many while retaining the ability to construct technological translations of information outside ordinary human access.
32. A Square Meter of Forest
A practical experimental unit could be remarkably small.
Consider one square meter of forest floor.
Measure:
light,
temperature,
humidity,
soil moisture,
chemical gradients,
vibration,
acoustic conditions,
airflow,
electrical conditions,
magnetic conditions,
nutrient distributions,
and biological activity.
Then model the informational realities of:
a human,
an insect,
a spider,
a plant,
a fungus,
a nematode,
and several microorganisms.
The physical coordinate domain remains constant.
The organismal mappings differ.
Superimpose them.
The result would not merely show what lives there.
It would show which portions of that place exist as biologically usable information for each form of life.
That is a fundamentally different map of an ecosystem.
33. From Maps of Species to Maps of Perspective
Traditional ecological maps commonly represent:
species distributions,
habitat types,
vegetation,
temperature,
precipitation,
resources,
and physical geography.
An informational ecosystem map would add:
detectable chemical fields,
relevant spectral information,
acoustic environments,
vibration networks,
electrical fields,
hydrodynamic cues,
navigational information,
organism-generated signals,
and sensory accessibility.
The resulting map would be observer-indexed.
Instead of asking:
What is present here?
it would also ask:
Present to whom?
34. Implications for Ecology
The informational ecosystem framework does not replace conventional ecology.
Energy remains essential.
Matter remains essential.
Population dynamics remain essential.
Evolution remains essential.
Physical habitat remains essential.
The proposed framework adds another layer:
\[ \text{Matter} + \text{Energy} + \text{Information}. \]
Organisms require matter and energy to survive.
But they require information to locate resources, avoid threats, reproduce, regulate internal conditions, interact with other organisms, and navigate changing environments.
Information is therefore not an ornamental description of ecology.
It participates directly in ecological function.
35. Implications for Evolution
Evolution acts upon organisms whose survival depends partly upon detecting useful environmental differences.
Sensory systems can therefore evolve as solutions to informational problems.
Likewise, signaling systems can evolve because receivers respond to particular information.
Camouflage evolves because detectors can be defeated.
Mimicry evolves because informational classifications can be exploited.
Predators evolve improved detection.
Prey evolve improved concealment or warning systems.
The informational ecosystem is therefore itself evolutionary.
The mappings:
\[ \Phi_i \]
change across generations.
Life evolves not only within physical environments.
It evolves ways of accessing those environments.
36. Implications for Consciousness
The informational ecosystem framework does not require consciousness.
A bacterium can participate.
A plant can participate.
A fungus can participate.
An animal can participate.
A human can participate.
The relevant criterion is biologically consequential information detection and response.
Nevertheless, consciousness research may benefit from the framework because conscious organisms exist inside informational architectures shaped by these ecological relationships.
If consciousness depends in part upon integrated biological information, then understanding the information available to an organism provides constraints upon what could enter its experienced world.
But the distinction must remain:
\[ \text{informational reality} \neq \text{phenomenal consciousness}. \]
The former can be studied without presupposing the latter.
37. A Research Program
The informational ecosystem hypothesis suggests a concrete research program.
Select a bounded environment.
Measure its relevant physical variables.
Identify representative organisms.
Characterize their demonstrated detection systems.
Construct organism-specific informational mappings.
Track real behavior.
Construct simulated agents restricted to the same information.
Compare simulated and biological behavior.
Then manipulate:
sensory channels,
signal strength,
environmental noise,
cue conflict,
internal state,
and ecological relationships.
The result would be a controlled test of whether ecological behavior becomes more predictable when organisms are modeled according to the information actually available to them rather than according to an omniscient representation of the environment.
38. The Informational Ecosystem Hypothesis
The central hypothesis of this paper can be stated formally:
Ecological interactions can be more completely modeled by representing each organism as operating within a biologically constrained informational mapping of a shared physical environment and by explicitly modeling the intersections, asymmetries, transmissions, modifications, and conflicts among those mappings.
This hypothesis generates empirical predictions.
Models incorporating realistic informational constraints should, under appropriate circumstances, outperform equivalent models that provide agents with unrestricted environmental information.
Removing biologically important information channels should produce predictable behavioral and ecological disruptions.
Restoring those channels should restore corresponding performance.
Environmental disturbances that alter relevant information should produce effects consistent with field observations.
These propositions are testable.
39. Conclusion
An ecosystem is not experienced uniformly by the organisms inhabiting it.
The physical forest is shared.
The informational forest is plural.
The physical ocean is shared.
The informational ocean is plural.
The physical soil is shared.
The informational soil is plural.
A human, bee, spider, plant, fungus, bacterium, shark, salmon, bat, and every other organism interacts with physical reality through biological constraints that determine which environmental differences become accessible information.
These mappings can be represented as:
\[ R_i=\Phi_i(E). \]
The ecosystem then becomes:
\[ \mathcal{R}(E)=\{R_1,R_2,\ldots,R_n\}. \]
Its informational overlaps are:
\[ R_i\cap R_j. \]
Its asymmetries include:
\[ R_i-R_j. \]
Its biologically accessible union is:
\[ R_{\cup}=\bigcup_{i=1}^{n}R_i. \]
Yet even that union need not equal:
\[ E. \]
There may always remain physical information outside the biological access of the organisms being considered.
This distinction changes the way an ecosystem can be conceptualized.
A predator does not merely encounter prey.
It encounters information produced by prey.
Prey does not merely encounter a predator.
It encounters whatever information about that predator its biology permits it to detect.
A pollinator does not encounter an abstract flower.
It encounters a particular subset of the flower's physical properties.
A plant does not inhabit generic soil.
Its biology responds to particular gradients and signals within that soil.
A microorganism does not inhabit an empty droplet.
It inhabits a structured chemical and physical landscape.
A fungus does not merely occupy space.
It grows through a distributed field of biologically meaningful differences.
These realities overlap.
They interact.
They conceal.
They reveal.
They deceive.
They cooperate.
They compete.
And organisms continuously modify them for one another.
Ecology can therefore be understood not only as the study of organisms exchanging matter and energy within environments, but also as the study of organisms detecting, producing, transforming, obscuring, and exploiting information within those environments.
The proposed informational ecosystem does not replace the physical ecosystem.
It exposes another dimension of it.
There is one physical environment.
There are many biological mappings of that environment.
Their intersections help make ecological relationships possible.
Their differences create informational advantages and disadvantages.
Their union reveals more of the environment than any single organism can access.
And their superposition offers a new way of seeing life:
not merely as organisms occupying the same world, but as organisms occupying overlapping windows onto the same reality.
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John Swygert
September 3, 2026
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