THE TELEMETRY OF LIFE:
Multistream Environmental Sensing, Biological Equilibrium, and Closed-Loop Control in Organismal Behavior
DOI: [to be assigned]
John Swygert
September 3, 2026
Abstract
Organisms do not merely receive information about their environments. They continuously use external and internal information to regulate physiological state, select behavior, orient movement, exploit environmental energy, avoid danger, acquire resources, reproduce, and maintain conditions compatible with life. Yet biological behavior is often described in terms of individual senses or final outcomes—migration, navigation, thermoregulation, foraging, dispersal—rather than as the product of multiple simultaneous information streams participating in closed biological control loops.
This paper proposes a framework termed the telemetry of life: the continuous acquisition, integration, and biological use of external and internal state information by organisms. The term telemetry is used here as a systems analogy and should not be confused with conventional wildlife telemetry, in which instruments remotely transmit measurements. Biological organisms do not literally transmit all sensory information to an external receiver. Rather, they possess distributed streams of environmental and physiological information that can function analogously to telemetry within a control architecture.
The framework begins with a visually striking comparison between ballooning spiders and the deep-sea bigfin squid, Magnapinna spp. Ballooning spiders use silk to couple themselves to atmospheric forces and can behaviorally evaluate environmental conditions before takeoff. Magnapinna possesses an unusual morphology characterized by large fins, extended proximal appendages, and extraordinarily long distal filaments. The functional significance of its characteristic posture remains incompletely understood. The superficial resemblance between these organisms does not establish convergent evolution or a shared locomotor mechanism. It does, however, motivate a testable biomechanical question: can extended and filamentous morphologies provide useful ways of coupling organisms to moving fluids, while biological information determines when and how that coupling is exploited?
From this comparison, a broader model is developed in which behavior arises through repeated cycles:
[ \text{environmental state} \rightarrow \text{biological detection} \rightarrow \text{state integration} \rightarrow \text{action} \rightarrow \text{new environmental relationship} \rightarrow \text{new detection}. ]
Within fluid environments, this framework produces a specific hypothesis. Atmospheres and oceans contain vertically and horizontally structured velocity fields rather than uniform currents. An organism may therefore reduce the energetic cost of horizontal travel by maneuvering between moving fluid layers. Temperature, pressure, oxygen, salinity, chemistry, flow, light, magnetic information, internal energy state, and other variables could provide simultaneous control inputs. Repeated local responses intended to maintain or restore biological equilibrium could consequently generate large-scale trajectories without requiring a human-like representation of the complete route.
The framework is extended beyond locomotion to homeostasis, habitat selection, foraging, predator avoidance, reproduction, microbial behavior, plant responses, and ecological interaction. It generates an empirical program in which real organismal telemetry and environmental measurements are used to construct artificial agents possessing only biologically plausible information streams. Sensory channels can then be removed, combined, altered, or placed in conflict to determine which information is sufficient to reproduce observed behavior.
The central proposition is:
Morphology determines how an organism can interact with physical forces; biological telemetry provides information about its relationship to those forces and to its internal state; closed-loop control transforms that information into actions that can maintain life and generate complex behavior.
1. Introduction
Every living organism exists inside changing physical conditions.
Temperature changes.
Light changes.
Chemical concentrations change.
Water availability changes.
Air and water move.
Resources appear and disappear.
Predators approach.
Prey move.
Internal energy stores decline.
Cells experience changing molecular conditions.
The organism itself also changes continuously.
Life therefore presents a fundamental control problem:
[ \text{How can a biological system remain viable while both itself and its environment are changing?} ]
The answer necessarily involves information.
An organism must detect at least some biologically relevant differences, directly or indirectly, and respond to them.
This does not require consciousness.
It does not require a nervous system.
It does not require symbolic reasoning.
It requires biological mechanisms capable of coupling detectable differences to consequential responses.
The framework developed here treats those simultaneously changing information streams as the telemetry of life.
2. What Is Meant by Biological Telemetry?
In engineering and field biology, telemetry conventionally refers to measurements collected at one location and transmitted elsewhere for observation.
That is not the literal meaning proposed here.
The term biological telemetry is used as a systems-level analogy for the streams of information available within an organism concerning:
- the external environment,
- the organism's own internal condition,
- other organisms,
- its movement relative to the environment,
- and changes occurring through time.
A useful distinction is therefore:
[ \text{instrumental telemetry} \neq \text{biological telemetry as defined here}. ]
The latter describes information entering and circulating through biological control architectures.
Examples include information associated with:
[ T=\text{temperature} ]
[ P=\text{pressure} ]
[ C=\text{chemical conditions} ]
[ L=\text{light} ]
[ F=\text{fluid movement} ]
[ M=\text{magnetic-field properties} ]
[ Q=\text{electrical conditions} ]
[ V=\text{mechanical vibration} ]
[ O=\text{oxygen availability} ]
[ S=\text{salinity} ]
together with internal variables such as energy state, hydration, nutrient status, metabolic state, damage, reproductive condition, and other physiological variables.
No claim is made that every organism measures all of these.
The available streams are organism-specific.
3. From Sensory Reality to Biological Control
Previous formulations of organismal informational reality can be represented as:
[ R_i=\Phi_i(E), ]
where:
- E is the physical environment,
- \Phi_i is the detection architecture of organism i,
- R_i is the portion of environmental information biologically available to that organism.
But detection alone is insufficient to explain behavior.
An organism must somehow transform available information into biological response.
Let:
[ D_i(t)=\Phi_i[E(t)] ]
represent external information detected at time t.
Let:
[ I_i(t) ]
represent internal-state information.
Let:
[ H_i(t) ]
represent biologically relevant recent history, memory, adaptation, or prior state where applicable.
The organism's operational state can then be represented:
[ X_i(t)=\Psi_i[D_i(t),I_i(t),H_i(t)]. ]
Action becomes:
[ A_i(t)=\pi_i[X_i(t)]. ]
That action changes the organism's relationship with the environment:
[ A_i(t)\rightarrow E_i(t+1). ]
The organism consequently receives new information:
[ E_i(t+1)\rightarrow D_i(t+1). ]
The complete architecture is therefore recursive:
[ E_t \rightarrow D_t \rightarrow X_t \rightarrow A_t \rightarrow E_{t+1} \rightarrow D_{t+1}. ]
Life operates within loops.
4. A Visual Observation: Spider and Bigfin Squid
A useful starting point comes from two organisms inhabiting dramatically different environments.
The first is a ballooning spider.
The second is the deep-sea bigfin squid:
[ \textit{Magnapinna}\ \text{spp.} ]
They are phylogenetically distant and occupy entirely different ecological settings.
Yet certain images of Magnapinna produce a striking superficial resemblance to a spider: a relatively centralized body architecture surrounded by long, thin, extended structures.
The resemblance itself proves nothing.
It does not demonstrate common ancestry for the morphology.
It does not establish convergent evolution.
It does not demonstrate that the squid's appendages function like ballooning silk.
But scientific hypotheses often begin with noticing a pattern and asking whether it contains useful information.
The appropriate question is therefore not:
Do these organisms look similar because they use the same locomotor mechanism?
The appropriate question is:
Could some aspects of superficially similar extended geometries provide analogous fluid-dynamic advantages in different fluid environments?
That question can be tested.
5. The Atmospheric Example: Ballooning Spiders
Spider ballooning provides a demonstrated example of an organism exploiting environmental physical forces for transportation.
Ballooning spiders climb to an exposed location, release silk, and can become airborne through interaction between the silk and atmospheric forces.
Experiments and observations show that the phenomenon is more sophisticated than simply describing the spider as an inert particle carried randomly by wind.
Studies of ballooning spiders have documented behavioral evaluation of wind conditions before launch. Experiments with Erigone spp. have additionally shown that atmospheric-strength electric fields can elicit pre-ballooning behavior and takeoff, and mechanosensory hairs respond to weak electric fields.
Ballooning therefore involves at least two scientifically important concepts:
[ \text{environmental force} ]
and
[ \text{biological detection of environmental conditions}. ]
The spider does not generate the atmospheric transportation field.
It exploits it.
Its silk provides physical coupling.
Its behavior affects when that coupling begins.
Thus:
[ \text{environmental information} \rightarrow \text{launch decision} \rightarrow \text{fluid/electrostatic coupling} \rightarrow \text{transport}. ]
This immediately distinguishes passive propulsion from passive behavior.
An organism can receive most of its transportation energy from the environment while actively controlling its entry into that transportation system.
6. The Marine Example: Bigfin Squid
Bigfin squid of the genus Magnapinna are rarely observed deep-sea cephalopods distinguished by large fins and extraordinarily elongated arm and tentacle filaments.
A characteristic configuration involves proximal appendages extending outward before extremely long distal filaments descend through the surrounding water.
The function of this distinctive configuration remains incompletely resolved. Feeding has been proposed as one possible function because of the adhesive characteristics of the filaments and comparisons with other deep-sea squid.
Consequently, this paper does not propose that the long filaments evolved primarily for transport.
Instead, it proposes a question:
Do the extended appendages and filaments have measurable consequences for drag, stability, orientation, current coupling, flow sensing, vertical positioning, or energetic expenditure in moving water?
The answer could be yes, no, or functionally insignificant.
Each outcome would be scientifically informative.
7. Morphology as an Interface with Environmental Energy
An organism's morphology determines how physical forces act upon it.
For an organism in a fluid, relevant properties include:
- projected area,
- drag coefficient,
- buoyancy,
- density,
- appendage geometry,
- flexibility,
- orientation,
- surface properties,
- center of mass,
- center of buoyancy,
- and the surrounding flow regime.
Thus morphology can be represented as part of a physical coupling function:
[ K_i=K(M_i,F), ]
where:
- M_i represents organismal morphology,
- F represents the surrounding fluid field,
- K_i represents the resulting physical coupling.
The important hypothesis is therefore broader than either spiders or squid:
Extended or filamentous biological structures may sometimes increase, regulate, or otherwise alter coupling between an organism and moving fluid, although their primary evolutionary functions may be entirely different or multifunctional.
The spider and Magnapinna serve as visually intuitive endpoints of this larger question.
8. Air and Water Are Both Fluids—but They Are Not Equivalent
The comparison must not obscure major physical differences.
Air and seawater differ substantially in:
- density,
- viscosity,
- buoyancy,
- pressure structure,
- compressibility,
- turbulence,
- conductivity,
- and the forces acting on organisms and filaments.
The relevant Reynolds-number regimes may also differ dramatically between structures and organisms.
Therefore:
[ \text{atmospheric ballooning} \neq \text{underwater ballooning}. ]
The proposed analogy is structural:
[ \text{organism} + \text{extended geometry} + \text{moving fluid} + \text{environmental information} ]
rather than a claim of identical mechanics.
The comparison asks whether evolution can exploit environmental movement through different biological solutions operating under different physical regimes.
9. The Transportation-Network Hypothesis
Atmospheres and oceans do not move uniformly.
They contain structured velocity fields:
[ \mathbf{v}(x,y,z,t). ]
Flow direction and velocity can vary with:
- horizontal location,
- altitude or depth,
- time,
- temperature,
- density,
- pressure,
- topography,
- tides,
- atmospheric structure,
- ocean stratification,
- and turbulence.
An organism embedded in such a system therefore exists within a moving transportation network.
At one vertical position:
[ z_1:\mathbf{v}_1. ]
At another:
[ z_2:\mathbf{v}_2. ]
And:
[ \mathbf{v}_1\neq\mathbf{v}_2. ]
The flows may differ in speed and direction.
This produces the moving-highway hypothesis:
For some organisms, energetically efficient horizontal displacement may be achieved partly by controlling vertical or local position so that environmental fluid motion supplies a substantial portion of horizontal transportation.
The organism need not propel itself across the entire journey.
It may need to maneuver between environmental transportation layers.
10. Passive Transport Does Not Mean Passive Navigation
Consider a human transportation analogy.
A passenger traveling by train does not generate the locomotive force.
The important actions are:
- choosing a route,
- reaching the appropriate station,
- boarding,
- transferring when necessary,
- and leaving the system at the appropriate point.
Environmental transport can operate similarly in principle.
The organism supplies:
[ E_{\text{control}} ]
while the environment supplies:
[ E_{\text{transport}}. ]
When:
[ E_{\text{transport}}\gg E_{\text{control}}, ]
large displacement can occur for comparatively little organism-generated locomotor energy.
This suggests an important distinction:
[ \boxed{\text{passive propulsion}\neq\text{passive navigation}}. ]
An organism can exploit passive transport through active decisions.
11. The Multistream Problem
How might an organism determine when to change position?
A marine organism could potentially possess biologically accessible information related to:
[ T=\text{temperature}, ]
[ P=\text{pressure/depth}, ]
[ O=\text{oxygen}, ]
[ S=\text{salinity}, ]
[ F=\text{fluid velocity}, ]
[ C=\text{chemical conditions}, ]
[ L=\text{light}, ]
[ M=\text{geomagnetic information}, ]
plus organism-specific biological signals.
Its internal state may simultaneously provide:
[ I={\text{energy, hydration, metabolic demand, reproductive state, stress, damage,\ldots}}. ]
Behavior can therefore depend upon a multivariable state:
[ A_t=f(T,P,O,S,F,C,L,M,I,H). ]
The organism need not consciously calculate this function.
Evolution, physiology, neural processing where present, and other biological regulatory systems can embody it.
12. Equilibrium as a Control Objective
Many biological systems regulate variables around viable ranges.
Let a biologically preferred or viable state be represented:
[ X^*. ]
Current state is:
[ X_t. ]
Deviation is:
[ \epsilon_t=X_t-X^*. ]
A regulatory response can act to reduce:
[ |\epsilon_t|. ]
In reality, biological regulation usually involves ranges, tradeoffs, nonlinearities, multiple competing variables, and changing set points rather than a single perfect equilibrium.
Nevertheless, the control formulation captures a central principle:
[ \text{detect deviation} \rightarrow \text{respond} \rightarrow \text{measure new state}. ]
This is the architecture of feedback.
13. Temperature as a Hypothetical Driver
Consider a simplified marine example.
An organism occupies water at temperature:
[ T_t. ]
Its viable or preferred temperature range is:
[ [T_{\min},T_{\max}]. ]
Suppose:
[ T_t<T_{\min}. ]
A biological response produces vertical movement:
[ A_t=\text{ascend} ]
or:
[ A_t=\text{descend}, ]
depending upon local ocean structure.
The organism enters another water mass.
Temperature changes:
[ T_{t+1}. ]
But something else changes simultaneously:
[ F_t\rightarrow F_{t+1}. ]
The new depth contains a different current.
Thus a maneuver initiated in response to temperature can produce horizontal displacement.
The causal chain becomes:
[ \text{temperature deviation} \rightarrow \text{vertical movement} \rightarrow \text{current change} \rightarrow \text{horizontal transport}. ]
Repeated over time, this could generate a large-scale trajectory.
This is a hypothesis, not a claim that Magnapinna or any particular organism uses this mechanism.
14. Equilibrium-Seeking Migration
The preceding example suggests a broader possibility.
Some large-scale movement could emerge from repeated local regulation:
[ \text{local state correction} + \text{environmental structure} \rightarrow \text{large-scale trajectory}. ]
An outside observer sees:
[ \text{migration}. ]
The organism's control architecture may instead be repeatedly solving local problems:
[ \text{too cold} ]
[ \text{oxygen declining} ]
[ \text{chemical cue strengthening} ]
[ \text{current unfavorable} ]
[ \text{light inappropriate} ]
[ \text{pressure changing} ]
[ \text{magnetic condition inconsistent} ]
[ \text{internal energy declining}. ]
No single variable need control the journey.
The trajectory could emerge from multistream control.
15. Geography and Equilibrium Need Not Be Alternatives
A crucial point follows.
Navigation based upon geographical information and movement based upon physiological regulation are not mutually exclusive.
An organism could possess information related to location or direction while simultaneously regulating physiological state.
Thus:
[ A_t= f( \text{geographic information}, \text{environmental state}, \text{internal state} ). ]
For example, a migrating organism could maintain a general directional tendency while selecting depths or altitudes according to temperature, oxygen, currents, winds, or resource conditions.
This is more realistic than requiring a single sensory mechanism to explain an entire migration.
16. Salmon as a Multistream Example
Salmon provide a useful established example of multimodal navigation.
Evidence supports the use of olfactory information in natal-stream recognition and geomagnetic information in large-scale navigation, while environmental variables such as temperature, flow, salinity, and other conditions also affect salmon biology and movement.
The important conceptual lesson is not that one channel has been identified as the navigation system.
It is that biological navigation can be sequential and multimodal.
A salmon can therefore be modeled not as:
[ \text{destination}\rightarrow\text{movement} ]
but as:
[ A_t=f(M,C,F,T,S,I,H,\ldots). ]
The scientific challenge becomes determining the relative contribution and interaction of those channels.
17. From Destination Thinking to Control Thinking
Human observers naturally describe animal movement according to destinations.
“The salmon is returning home.”
“The bird is migrating south.”
“The spider is dispersing.”
“The marine animal is moving toward warmer water.”
Those descriptions may be accurate at the ecological level while obscuring the control mechanism.
A more mechanistic question is:
What information is available to the organism at this moment, what internal variables are changing, and what action follows?
Then ask the same question at:
[ t+1, ]
and:
[ t+2. ]
A complete migration may emerge from thousands or millions of local control events.
18. The Organism Does Not Need the Human Map
Suppose a simulated organism is told:
[ \text{Travel to coordinate }(x_d,y_d). ]
Success reveals little about biological navigation because the destination has been supplied explicitly.
Instead, remove the coordinate.
Give the agent only biologically plausible inputs:
[ D_t. ]
Allow it to respond through:
[ A_t=\pi(D_t,I_t,H_t). ]
Then determine whether:
[ (x_t,y_t,z_t)\rightarrow(x_d,y_d,z_d) ]
emerges.
If it does, the modeled information may be sufficient.
If it does not, the model may lack:
- a sensory channel,
- an internal variable,
- memory,
- an appropriate control rule,
- environmental resolution,
- developmental information,
- social information,
- or some other relevant factor.
Failure becomes informative.
19. Maneuvering Versus Propulsion
The framework suggests separating two concepts often merged under locomotion.
Propulsion
Energy that produces displacement.
Maneuvering
Control that changes how displacement occurs.
For an organism exploiting environmental flow:
[ \text{net displacement}
\text{self-propulsion} + \text{environmental transport}. ]
But biological control can determine the weighting of these terms.
A small vertical maneuver may yield enormous horizontal displacement if it places the organism inside a strong current.
Therefore the energetic cost of navigation cannot always be inferred from travel distance alone.
20. Morphology + Telemetry + Environmental Energy
The full framework can now be stated:
[ \boxed{ \text{Behavioral outcome}
f( \text{morphology}, \text{telemetry}, \text{control}, \text{environmental energy} ) } ]
Morphology determines possible physical interactions.
Telemetry supplies information.
Control transforms information into response.
Environmental energy can amplify the consequences of that response.
The spider provides a demonstrated atmospheric example of this general architecture.
The Magnapinna comparison raises the question of how far analogous principles may extend into marine systems.
Many other organisms provide additional cases.
21. Beyond Spiders and Squid
The spider–bigfin-squid comparison is illustrative rather than unique.
Environmental transport is relevant to:
- aerial arthropods,
- plankton,
- jellyfish,
- larvae,
- spores,
- seeds,
- microorganisms,
- fishes,
- marine invertebrates,
- birds using thermals and wind,
- and numerous other organisms.
The balance between active propulsion and environmental transport varies enormously.
The general question remains:
How much biological control can be achieved by sensing and exploiting energy already present in the environment?
22. Telemetry for Supporting Life
Movement is only one application.
The same architecture supports basic biological viability.
Consider:
[ \text{temperature telemetry} \rightarrow \text{thermoregulatory response}, ]
[ \text{water-status telemetry} \rightarrow \text{water conservation/acquisition}, ]
[ \text{energy telemetry} \rightarrow \text{foraging}, ]
[ \text{oxygen telemetry} \rightarrow \text{respiratory or positional response}, ]
[ \text{damage telemetry} \rightarrow \text{repair/avoidance response}. ]
The organism continually adjusts itself relative to changing conditions.
Life is therefore not merely structure.
It is regulated structure.
23. Plants and Distributed Biological Control
The framework is not limited to animals.
Plants continuously respond to environmental information involving:
- light,
- gravity,
- moisture,
- temperature,
- mechanical stimulation,
- chemical conditions,
- nutrient availability,
- pathogens,
- herbivory,
- and neighboring organisms.
Growth itself can function as a long-timescale control response.
Roots change direction.
Shoots change orientation.
Stomata regulate gas exchange.
Development changes with environmental conditions.
The relevant timescale differs from rapid animal maneuvering, but the architecture remains:
[ \text{detect} \rightarrow \text{integrate} \rightarrow \text{respond} \rightarrow \text{detect again}. ]
24. Microorganisms and Gradient Navigation
Microbial chemotaxis provides an especially clear demonstration that complex-looking navigation need not require a cognitive map.
A microorganism can alter movement according to changes in chemical conditions.
Local sampling produces directional bias.
Repeated local responses generate movement through a chemical landscape.
This offers an important analogy for the larger hypothesis:
[ \text{simple local rule} + \text{structured environment} + \text{feedback} \rightarrow \text{complex trajectory}. ]
Large-scale animal navigation may involve vastly more sophisticated systems, but the underlying control principle is general.
25. Internal Telemetry
External sensing alone cannot explain biological behavior.
The organism also has information concerning itself.
Let:
[ I_t= {i_1,i_2,\ldots,i_m}. ]
These variables can include physiological conditions relevant to:
- energy,
- hydration,
- oxygenation,
- temperature,
- nutrient status,
- reproductive condition,
- injury,
- immune activity,
- metabolic state,
- and other internal processes.
Behavior therefore depends upon the relationship:
[ A_t=f(D_t,I_t). ]
The same external environment can generate different behavior because:
[ I_t\neq I_{t+1}. ]
A hungry organism and a satiated organism can inhabit the same physical environment while making different decisions.
26. Equilibrium Is Dynamic
The word equilibrium must be used carefully.
Living systems are not generally static equilibrium systems.
They are dynamic, energy-consuming systems maintaining viable states far from thermodynamic equilibrium.
The relevant biological concept is therefore closer to:
- homeostasis,
- allostasis,
- regulation,
- dynamic stability,
- viable operating range,
- and state-dependent control.
Thus the proposed equilibrium model should be understood as shorthand for:
[ X_t\in\mathcal{V}, ]
where:
[ \mathcal{V} ]
is a viable region of biological state space.
Behavior acts partly to keep the organism within, or return it toward, that region.
27. Competing Equilibria and Tradeoffs
Organisms frequently cannot optimize every variable simultaneously.
Warmer water may contain less oxygen.
A favorable current may move away from food.
A resource-rich habitat may contain predators.
A thermally favorable altitude may have unfavorable wind.
An organism therefore faces a multi-objective control problem:
[ J= w_1J_T+ w_2J_O+ w_3J_E+ w_4J_P+ w_5J_R+\cdots ]
where the weights:
[ w_i ]
may themselves change with internal state.
Behavior can therefore appear inconsistent when viewed through only one variable while being rational within a multivariable biological control architecture.
28. Cue Conflict
This creates a powerful experimental method.
Present conflicting information.
For example:
[ T\rightarrow A ]
while:
[ M\rightarrow B. ]
Which input dominates?
Then vary internal state.
Does hunger change the weighting?
Does reproductive state?
Does developmental stage?
Does prior experience?
Cue-conflict experiments can reveal the hidden weighting structure of biological telemetry.
29. Sensory Ablation
Another method is informational removal.
Let the full detected state be:
[ D={T,P,O,S,F,C,M,L}. ]
Remove magnetic information:
[ D^{(-M)}. ]
Remove temperature:
[ D^{(-T)}. ]
Remove flow:
[ D^{(-F)}. ]
Compare resulting behavior.
If removal of one channel causes predictable degradation, that channel likely contributes to the control architecture.
If behavior remains unchanged, redundancy may exist.
Biological control may therefore be robust because several telemetry streams partially overlap.
30. Redundant Telemetry
Redundancy is potentially critical.
Suppose:
[ M ]
and:
[ C ]
both provide information correlated with location.
If one becomes unreliable, the other may still support navigation.
Similarly:
[ T, S, F, P ]
may jointly identify particular water masses better than any variable alone.
The organism may therefore exploit environmental signatures rather than isolated measurements.
Define:
[ \Sigma_t=(T,S,P,O,C,F,\ldots). ]
A location or habitat may possess a characteristic multidimensional signature.
Navigation could then depend partly upon recognizing trajectories through signature space rather than explicit geographic coordinates.
31. Environmental Signatures as Biological Coordinates
This possibility deserves special attention.
Humans represent geography primarily using:
[ \text{latitude}, \text{longitude}, \text{altitude/depth}. ]
Another organism need not.
Its effective coordinates could be combinations of:
[ T, S, M, C, F, L, P. ]
Thus biological geography may sometimes be represented as:
[ G_i=\Gamma_i(E), ]
where:
[ G_i ]
is an organism-specific navigational coordinate system.
A salmon's informational ocean may therefore contain landmarks that are invisible to humans without instruments.
A spider's atmosphere may contain usable structures that a human standing nearby does not consciously perceive.
The physical world is shared.
The navigational coordinate systems need not be.
32. Testing the Moving-Highway Hypothesis
The moving-highway hypothesis can be tested directly.
For a marine organism, measure:
[ (x,y,z,t) ]
together with:
[ \mathbf{v}(x,y,z,t), ]
temperature, pressure, salinity, oxygen, chemistry, light, magnetic information, and relevant internal variables where possible.
Then determine whether vertical movements precede predictable changes in horizontal displacement.
Compare:
[ \Delta z_t ]
with:
[ \Delta \mathbf{v}_{t+1}. ]
If organisms repeatedly enter flow layers that improve displacement relative to a biologically relevant direction or state, the pattern would support active flow selection.
Randomized or null models could determine whether the observed association exceeds chance expectation.
33. Testing the Morphological Hypothesis
The spider–Magnapinna comparison can likewise be made experimentally rigorous.
Construct physical or computational models representing:
- compact morphology,
- extended appendage morphology,
- filamentous morphology,
- alternative orientations,
- altered appendage lengths,
- altered flexibility.
Expose these models to measured or simulated fluid fields.
Measure:
- drag,
- stability,
- rotation,
- orientation,
- vertical displacement,
- lateral displacement,
- energy required for station keeping,
- response to shear,
- and flow-induced deformation.
For Magnapinna, such modeling could determine whether the characteristic geometry produces meaningful hydrodynamic consequences even if feeding remains its primary function.
A negative result would be equally important.
34. Predictions
The framework produces several testable predictions.
Prediction 1
Some organisms exploiting environmental flows should exhibit behavioral transitions correlated with measurable changes in environmental information before entering or leaving particular flow regimes.
Prediction 2
Vertical movement should sometimes produce disproportionately large horizontal displacement because of stratified current or wind fields.
Prediction 3
Models containing multiple biologically realistic telemetry streams should reproduce observed trajectories better than equivalent single-cue models where behavior is genuinely multimodal.
Prediction 4
Cue-conflict experiments should reveal state-dependent weighting among information channels.
Prediction 5
Removing biologically important telemetry streams from simulations should degrade behavioral performance in characteristic ways.
Prediction 6
Morphologies containing extended or filamentous structures should produce measurable changes in fluid coupling relative to geometrically simplified controls, although the sign and biological importance of those effects must be determined experimentally.
Prediction 7
Some apparently destination-directed movement should be reproducible without supplying agents with explicit destination coordinates if environmental information and control rules are sufficient.
35. Artificial Organisms as Experimental Models
Artificial agents provide an unusually powerful way to test these propositions.
Construct a physical simulation:
[ E(x,y,z,t). ]
Create an artificial organism with morphology:
[ M_i. ]
Give it only biologically plausible inputs:
[ D_i=\Phi_i(E). ]
Give it internal state:
[ I_i. ]
Allow behavior:
[ A_i=\pi_i(D_i,I_i,H_i). ]
Then ask whether observed biological behavior emerges.
The artificial organism must not receive privileged access to:
- true global coordinates,
- complete flow fields,
- hidden prey locations,
- destination coordinates,
- or other information unavailable to the biological organism.
This constraint is essential.
Otherwise the simulation tests the programmer's knowledge rather than the organism's informational architecture.
36. The Fundamental Experiment
A particularly powerful experiment would involve migration.
Select a species with sufficiently rich tracking and environmental data.
Construct a high-resolution environmental reconstruction.
Create an artificial organism possessing only demonstrated or strongly supported sensory channels.
Do not tell it where the destination is.
Initialize it at the observed starting region.
Then allow:
[ E_t \rightarrow D_t \rightarrow X_t \rightarrow A_t \rightarrow E_{t+1}. ]
Ask:
Does the trajectory emerge?
If yes, progressively remove channels.
If no, progressively add biologically plausible information.
The result becomes a search for the minimum sufficient telemetry architecture capable of reproducing the behavior.
37. Minimum Sufficient Telemetry
Define:
[ D^* ]
as the smallest information set capable of reproducing a target behavior within specified accuracy.
Then:
[ D^*= \arg\min_D |D| ]
subject to:
[ \mathcal{B}(D)\geq\theta, ]
where:
- \mathcal{B}(D) measures behavioral agreement,
- \theta is a predefined validation threshold.
This transforms questions such as:
How does this animal navigate?
into:
What is the minimum set of information required to reproduce its navigation?
That is experimentally tractable.
38. From Organisms to Ecosystems
No organism operates alone.
Its telemetry includes information generated by other organisms.
Predator signals affect prey.
Prey movement affects predators.
Flowers affect pollinators.
Hosts affect parasites.
Microbes alter chemical landscapes.
Plants modify temperature, humidity, light, chemistry, and structure.
Thus:
[ E_{t+1}
f(E_t,A_1,A_2,\ldots,A_n). ]
Every organism can alter the telemetry available to others.
Closed-loop biological control therefore scales upward into the informational ecosystem.
39. Relationship to the Informational Ecosystem
The informational ecosystem describes overlapping organismal access:
[ \mathcal{R}(E)={R_1,R_2,\ldots,R_n}. ]
The telemetry-of-life framework adds dynamics.
It asks:
What happens next?
For each organism:
[ R_i \rightarrow X_i \rightarrow A_i. ]
Those actions alter:
[ E. ]
The altered environment changes:
[ R_1,R_2,\ldots,R_n. ]
Therefore:
[ \mathcal{R}(E_t) \rightarrow {A_i} \rightarrow E_{t+1} \rightarrow \mathcal{R}(E_{t+1}). ]
The informational ecosystem is not a static map.
It is a continuously updating network of biological control loops.
40. Relationship to Organismal Reality
The simulation of organismal reality asks:
What portion of the physical environment is available to this organism?
The telemetry framework asks:
How is that available information used?
The distinction is:
[ \text{access} \rightarrow \text{control}. ]
An organismal reality becomes biologically consequential when detectable differences influence biological state or action.
Thus the present framework provides a bridge from sensory ecology to behavior, physiology, navigation, and ecological dynamics.
41. Relationship to Human Perspectival Reality
Humans also operate through telemetry.
Vision, audition, vestibular information, proprioception, interoception, touch, temperature, chemical senses, and numerous internal regulatory systems continuously constrain behavior.
Humans supplement these biological streams with technological telemetry:
- GPS,
- thermometers,
- radar,
- sonar,
- weather instruments,
- magnetometers,
- medical monitors,
- satellites,
- environmental sensors.
Technology therefore makes explicit what biology has always required:
[ \text{measure state} \rightarrow \text{compare} \rightarrow \text{decide} \rightarrow \text{act} \rightarrow \text{measure again}. ]
The difference is not the existence of feedback.
It is the architecture through which feedback occurs.
42. Consciousness Is Not Required
Nothing in this framework requires an organism to consciously experience the information it uses.
Therefore:
[ \text{telemetry} \neq \text{conscious perception}. ]
Likewise:
[ \text{decision} ]
is used functionally here and need not imply deliberative cognition.
A regulatory molecular pathway can implement state-dependent selection.
A bacterium can alter movement.
A plant can alter growth.
An animal can execute reflexive behavior.
A human can consciously deliberate.
These processes differ enormously, but all can participate in closed biological feedback.
43. Levels of Biological Control
The telemetry framework can therefore be organized hierarchically:
Molecular
Chemical detection and regulation.
Cellular
Membrane potentials, signaling, metabolic regulation.
Physiological
Homeostatic and allostatic regulation.
Organismal
Movement, feeding, avoidance, reproduction, navigation.
Ecological
Predator-prey, symbiosis, competition, communication.
Population
Dispersal, migration, aggregation.
Evolutionary
Selection acting upon detection and control architectures.
The telemetry of life exists across scales.
44. The Larger Principle
The spider and the bigfin squid return us to the original observation.
Their superficial resemblance may prove biologically irrelevant.
Or particular aspects of their extended geometries may reveal something general about organisms interacting with moving fluids.
Either result is valuable.
What matters is the larger principle exposed by the comparison:
An organism does not need to generate every force responsible for its movement or survival. It can evolve morphology that couples it to environmental forces and biological control systems that exploit information about those forces.
Thus:
[ \text{environment} ]
is not merely something an organism moves through.
It can become part of the organism's effective transportation and control architecture.
45. Conclusion
Life exists inside streams of information.
Some originate outside the organism.
Some originate within it.
Some describe physical conditions.
Some describe other organisms.
Some indicate deviation from viable biological states.
Some reveal opportunities.
Some reveal danger.
The organism continuously changes its relationship with these streams through action.
The fundamental cycle is:
[ \boxed{ E_t \rightarrow D_t \rightarrow X_t \rightarrow A_t \rightarrow E_{t+1} } ]
and then it begins again.
The spider releasing silk into the atmosphere provides a striking example. It can sense environmental conditions, initiate ballooning behavior, physically couple itself to atmospheric forces, and obtain transportation from energy it did not generate.
The bigfin squid provides a provocative comparative question. Its extraordinarily extended appendages and filaments operate in another fluid environment under radically different physical conditions. Their known or proposed functions must not be displaced by unsupported claims. Yet their geometry invites quantitative investigation into drag, stability, orientation, sensing, and interaction with deep-ocean flow.
The comparison therefore produces a broader hypothesis:
[ \boxed{ \text{Morphology} + \text{Telemetry} + \text{Control} + \text{Environmental Energy} \rightarrow \text{Behavior} } ]
The same principle can be examined in migration.
An organism need not necessarily generate all of the energy required to traverse enormous distances.
It may exploit moving atmospheric or oceanic structures.
Its principal energetic contribution may sometimes involve maneuvering between those structures rather than overcoming them.
Temperature may influence one maneuver.
Oxygen another.
Chemistry another.
Current velocity another.
Magnetic information another.
Internal physiological state another.
A large-scale trajectory can therefore potentially emerge from thousands of local decisions made within a changing multivariable environment.
This produces a different way to investigate navigation.
Instead of asking only:
Where does the organism know it is going?
we can ask:
What information is available to the organism now?
What biological state is it regulating?
What action follows from those conditions?
How does that action change the information available next?
Repeated through time, those questions may reconstruct the trajectory.
This approach also avoids assuming that an organism represents geography as humans do.
The human map uses latitude, longitude, and altitude.
Another organism may navigate through combinations of temperature, pressure, chemistry, salinity, light, flow, magnetic information, odor, vibration, or other variables.
Its biological coordinate system may therefore be:
[ G_i=\Gamma_i(E), ]
rather than a human cartographic representation.
The empirical challenge is to discover those coordinates.
This can be approached through measurement, tracking, sensory experiments, cue conflicts, channel ablation, fluid-dynamic modeling, and artificial organisms constrained to biologically plausible information.
Ultimately, the strongest test is simple to state:
Give the simulated organism only what the real organism could plausibly know and determine whether the real behavior still emerges.
If it does, the model has discovered something important.
If it does not, something is missing.
Either result advances the investigation.
Life can therefore be approached not merely as matter organized into organisms, nor merely as organisms sensing environments, but as a hierarchy of continuously operating control systems.
They detect.
They compare.
They regulate.
They maneuver.
They exploit physical forces.
They alter their surroundings.
Then they detect again.
Life does not merely exist within the physical environment. Life continuously measures its relationship to that environment and acts upon the difference.
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John Swygert
September 3, 2026
Copyright © John Swygert 2026
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