Unit 05 — Long-distance transport and source–sink networks

Conducting tissues redistribute water, mineral ions and sugars through interconnected hydraulic and metabolic pathways. An understanding of basic biological chemistry, membrane transport, gas exchange and high-school algebra will help. The emphasis in this unit is on the causal chain connecting an environmental or developmental input to a cellular mechanism, the resulting physiological rate and the observable performance of an entire plant. A well-formed explanation has all four pieces. Naming a hormone or an enzyme without explaining how its action changes matter or energy flows does not answer a physiological question.

The five topics below relate to the following fundamental distinctions: hydraulic conductance and tissue structure; pressure-driven sieve-tube flow; loading and unloading pathways; sink strength and competition; temporal dynamics of partitioning. They are best learned as tools for deciding what to measure and how to interpret measurements. Many leaf measurements report a concentration, whereas the biological question concerns a flow or accumulation rate. Many comparative studies manipulate fertilizer, light, temperature or watering yet change several physiological inputs at once. A useful experimental diagram therefore shows where the treatment enters, what process it should alter, and what separate processes could mimic its observed consequence.

By the end of this unit a learner should be able to explain all five processes using cell or tissue mechanisms; independently reproduce five worked calculations with correct physical units; interpret the study's simulated dataset without treating it as real evidence; and propose a discriminating control for a competing hypothesis. Additional practice asks for counterexamples, not just definitions. In living plants a mechanism can be plausible and still fail to explain a particular experiment because organ age, water status, whole-plant resource availability or the time of day changes its operating state.

A reproducible plant-physiology experiment begins with specimen identity, developmental stage, time of measurement, environmental history and a declared biological replicate. For gas exchange or transpiration specify area basis and temperature. For solutes specify concentration versus total amount, fresh mass versus dry mass and intracellular compartment where known. For time series decide whether independent plants were destructively harvested or repeated measurements came from the same individual. Neither a spectacular photograph nor a perfectly smooth fitted curve establishes a causal mechanism by itself.

There are three useful levels of uncertainty. Instrument uncertainty concerns calibration and resolution; sampling uncertainty concerns variation among independently grown plants or plots; and model uncertainty concerns assumptions used to turn readings into rates or parameters. These should not be merged into one vague caveat. Synthetic datasets in this unit demonstrate analytical reasoning but cannot estimate real-world uncertainty. In appropriate classroom experiments, use benign and authorized materials, avoid harmful chemical handling and follow the institution's biological and greenhouse safety policies.

Mechanistic explanation

Water-conducting tracheids and vessels form a distributed network in roots, stems and leaves. Hydraulic conductance depends on conduit geometry, connections and the water-potential gradient imposed by evaporation and soil conditions. Mineral ions carried in xylem sap move with bulk water but can also experience storage and exchange with living cells along the path.

Coupled constraints and functional trade-offs

Transpiration-driven sap flow fluctuates over a day. A stem-sensor value alone may be influenced by capacitance as tissues release or store water, so sap flow need not match instantaneous leaf transpiration. Drought-induced embolism and structural differences create nonlinear behavior not captured by a single permanent conductance.

How evidence would be collected

Compare sap-flow estimates against gravimetric pot water loss or canopy gas exchange under a controlled day. Record plant size and sapwood area. A change in apparent conductance must be evaluated with concurrent leaf and stem water-potential measurements.

Scope, assumptions and what must not be inferred

Do not assume all xylem tissues are functioning conduits or that stem sap-flow sensors measure every bypass and storage pathway.

Diagnostic distinction

In xylem architecture and sap movement, the central distinction is hydraulic conductance and tissue structure. Specify the treatment, one tissue-level mediator, and a direct measurement that could distinguish the proposed mechanism from an alternative involving pressure-driven sieve-tube flow. A time course and appropriate control are stronger evidence than a single endpoint.

Worked quantitative case 5.1

Question. For a pressure difference of 0.38 MPa and xylem conductance 4.5 mL h−1 MPa−1, estimate flow.

Relation. Q = KΔΨ. Evaluate the supplied hypothetical quantities in consistent units. The calculated value is 1.71 mL h−1. Reconstruct the arithmetic independently and specify whether it estimates a flux, concentration, coefficient or fraction. A correct numerical answer without units and domain assumptions is incomplete.

Interpretation. Conductance and driving force jointly determine flow. Model boundary. Nonlinear loss of functioning conduits is omitted. This is a synthetic calculation, not a measured field value. Replace one positive input with a value 20% greater, recompute, and ask whether an intact plant could instead respond through feedback that changes other inputs. When a model assumes constant conditions, a measurement during a rapid transient can deviate even if the mechanism is scientifically reasonable.

Test your explanation

Propose a control for the measurement described above. Explain how plant age or environment could change the predicted response, and identify a result that would falsify your favored mechanism. Distinguish a directly measured quantity from one inferred through the equation.

Mechanistic explanation

In many species, sucrose is loaded into the sieve-tube–companion-cell complex through apoplastic transporters or symplastic connections. Solute accumulation lowers water potential, draws water in and can build turgor that drives pressure-driven mass flow toward tissues where solutes are removed. Conductive sieve tubes are living tissue whose function depends on surrounding cells and local metabolic regulation.

Coupled constraints and functional trade-offs

Pressure flow moves solution, but unloading, loading and pressure generation are active and regulated. A tracer arriving in a sink is not equivalent to a direct measurement of bulk sap velocity if exchanges with intermediate pools occur. Sugar gradients alone do not establish the direction or speed of every individual sieve tube in a complex network.

How evidence would be collected

Use noninvasive imaging or suitably controlled tracer movement together with sugar assays in source and sink tissues. Vary source supply while keeping sink demand comparable. Distinguish short-term tracer appearance from the slower consequences for growth and storage.

Scope, assumptions and what must not be inferred

The Münch pressure-flow picture is an explanatory framework; it omits multicellular exchange, variable conduit geometry and dynamic sinks when used as a two-compartment toy model.

Diagnostic distinction

In phloem pressure flow and sucrose loading, the central distinction is pressure-driven sieve-tube flow. Specify the treatment, one tissue-level mediator, and a direct measurement that could distinguish the proposed mechanism from an alternative involving loading and unloading pathways. A time course and appropriate control are stronger evidence than a single endpoint.

Worked quantitative case 5.2

Question. Phloem volume flow is 0.08 mL h−1 with sucrose concentration 0.55 mmol/mL. Compute sucrose movement.

Relation. solute flux = Q×c. Evaluate the supplied hypothetical quantities in consistent units. The calculated value is 0.044 mmol h−1. Reconstruct the arithmetic independently and specify whether it estimates a flux, concentration, coefficient or fraction. A correct numerical answer without units and domain assumptions is incomplete.

Interpretation. Sugar concentration alone does not equal translocation. Model boundary. Flow and concentration must refer to the same route and interval. This is a synthetic calculation, not a measured field value. Replace one positive input with a value 20% greater, recompute, and ask whether an intact plant could instead respond through feedback that changes other inputs. When a model assumes constant conditions, a measurement during a rapid transient can deviate even if the mechanism is scientifically reasonable.

Test your explanation

Propose a control for the measurement described above. Explain how plant age or environment could change the predicted response, and identify a result that would falsify your favored mechanism. Distinguish a directly measured quantity from one inferred through the equation.

Mechanistic explanation

A mature illuminated leaf usually acts as a carbon source, while developing leaves, roots, fruits and storage organs can be sinks. Sink strength reflects potential size and the capacity to import and use assimilates; a developing organ can switch from importing to exporting as its physiology changes. Allocation emerges from multiple competing processes rather than one central distribution controller.

Coupled constraints and functional trade-offs

Removing fruits may increase carbohydrate storage in vegetative tissues but does not prove that fruit removal increased photosynthetic capacity. Sink demand can feed back to source activity, and water or nutrient limitation can suppress both sides simultaneously. Ratios of shoot to root dry mass can shift with organ turnover and growth stage.

How evidence would be collected

Follow tagged carbon through several organs while measuring growth increments and soluble sugar pools. Use a partial removal or shading treatment, with mock handling and known initial organ sizes. Normalize allocation fractions so they sum correctly and separate proportional change from absolute carbon amount.

Scope, assumptions and what must not be inferred

A fixed partition coefficient may describe a short window but should not be extrapolated through flowering, seed fill or dormancy.

Diagnostic distinction

In source strength, sink strength and allocation, the central distinction is loading and unloading pathways. Specify the treatment, one tissue-level mediator, and a direct measurement that could distinguish the proposed mechanism from an alternative involving sink strength and competition. A time course and appropriate control are stronger evidence than a single endpoint.

Worked quantitative case 5.3

Question. Fruits receive 42 mg of exported carbon and roots 28 mg. Estimate fruit fraction of these two measured destinations.

Relation. fraction = fruit/(fruit+root)×100. Evaluate the supplied hypothetical quantities in consistent units. The calculated value is 60 %. Reconstruct the arithmetic independently and specify whether it estimates a flux, concentration, coefficient or fraction. A correct numerical answer without units and domain assumptions is incomplete.

Interpretation. Allocation fractions require a clearly stated destination pool. Model boundary. Leaves, stems, respiration and exudates are missing. This is a synthetic calculation, not a measured field value. Replace one positive input with a value 20% greater, recompute, and ask whether an intact plant could instead respond through feedback that changes other inputs. When a model assumes constant conditions, a measurement during a rapid transient can deviate even if the mechanism is scientifically reasonable.

Test your explanation

Propose a control for the measurement described above. Explain how plant age or environment could change the predicted response, and identify a result that would falsify your favored mechanism. Distinguish a directly measured quantity from one inferred through the equation.

Mechanistic explanation

Water shortage may alter phloem turgor, xylem conductance and source carbon production together. Because the two transport systems exchange water, a hydraulic perturbation can indirectly affect assimilate movement even when a phloem transporter is intact. Pathogens and wounding can trigger callose deposition or altered source–sink relations, sometimes restricting translocation.

Coupled constraints and functional trade-offs

Reduced carbohydrate arrival at roots after drought could arise from less carbon fixation, slower mass flow or stronger allocation to competing tissues. Observing root growth decline cannot distinguish these explanations without simultaneous carbon and water measurements. Recovery can be asynchronous: leaf water status may improve before carbon distribution is restored.

How evidence would be collected

Measure assimilation, phloem-proxy transport, leaf water potential and root respiration through a before–during–after stress experiment. Include a rehydration group and a never-stressed comparator. Choose sampling times that can detect different lags rather than only final biomass.

Scope, assumptions and what must not be inferred

Sap sugar concentration is not the same as sugar flux. Flux needs volume or mass movement per time and an appropriate concentration at the measured location.

Diagnostic distinction

In transport under stress and phloem vulnerability, the central distinction is sink strength and competition. Specify the treatment, one tissue-level mediator, and a direct measurement that could distinguish the proposed mechanism from an alternative involving temporal dynamics of partitioning. A time course and appropriate control are stronger evidence than a single endpoint.

Worked quantitative case 5.4

Question. A sink imports 12 mg sucrose per hour before drought and 8 mg per hour afterward. Estimate proportional change.

Relation. relative difference=(new−old)/old×100. Evaluate the supplied hypothetical quantities in consistent units. The calculated value is -33.3333 %. Reconstruct the arithmetic independently and specify whether it estimates a flux, concentration, coefficient or fraction. A correct numerical answer without units and domain assumptions is incomplete.

Interpretation. Reduced import can reflect supply, hydraulic or unloading limits. Model boundary. Tracers must be corrected for pool turnover. This is a synthetic calculation, not a measured field value. Replace one positive input with a value 20% greater, recompute, and ask whether an intact plant could instead respond through feedback that changes other inputs. When a model assumes constant conditions, a measurement during a rapid transient can deviate even if the mechanism is scientifically reasonable.

Test your explanation

Propose a control for the measurement described above. Explain how plant age or environment could change the predicted response, and identify a result that would falsify your favored mechanism. Distinguish a directly measured quantity from one inferred through the equation.

Mechanistic explanation

Crop yield depends on how much assimilate is produced, retained and delivered to reproductive or storage sinks during development. Partitioning can be affected by flowering time, temperature, water supply and nutrient status. A harvest index compares economically harvested dry matter with total aboveground biomass in many agronomic settings, but definitions depend on the product being considered.

Coupled constraints and functional trade-offs

A high harvest index can coexist with low overall productivity if total biomass is small. Conversely, high photosynthetic rate measured on one leaf may not scale to an entire canopy where shading, respiration and season length matter. An intervention that increases grain number may lower mean grain mass, revealing a sink limitation under finite carbon supply.

How evidence would be collected

Compare harvested dry mass, total aboveground dry mass, canopy interception and developmental timing across independent field plots. Describe the product definition and whether moisture contents were standardized. Separate differences in total carbon supply from shifts in partitioning.

Scope, assumptions and what must not be inferred

Harvest index and fitted source–sink relationships summarize outcomes; they do not independently identify the transporter or enzyme causing the difference.

Diagnostic distinction

In source–sink models and harvest outcomes, the central distinction is temporal dynamics of partitioning. Specify the treatment, one tissue-level mediator, and a direct measurement that could distinguish the proposed mechanism from an alternative involving hydraulic conductance and tissue structure. A time course and appropriate control are stronger evidence than a single endpoint.

Worked quantitative case 5.5

Question. Economic dry harvest is 210 g and total aboveground dry mass is 560 g. Estimate harvest index.

Relation. HI = harvested dry mass/total dry mass×100. Evaluate the supplied hypothetical quantities in consistent units. The calculated value is 37.5 %. Reconstruct the arithmetic independently and specify whether it estimates a flux, concentration, coefficient or fraction. A correct numerical answer without units and domain assumptions is incomplete.

Interpretation. Partitioning can change even if total assimilation does not. Model boundary. Crop quality, roots and missing harvest losses require separate analysis. This is a synthetic calculation, not a measured field value. Replace one positive input with a value 20% greater, recompute, and ask whether an intact plant could instead respond through feedback that changes other inputs. When a model assumes constant conditions, a measurement during a rapid transient can deviate even if the mechanism is scientifically reasonable.

Test your explanation

Propose a control for the measurement described above. Explain how plant age or environment could change the predicted response, and identify a result that would falsify your favored mechanism. Distinguish a directly measured quantity from one inferred through the equation.

Biological puzzle and mechanistic alternatives

Primary question: After fruit removal, does higher leaf sugar indicate reduced export capacity or weakened demand? Different physiological mechanisms can produce the same apparent trend in a leaf measurement or growth endpoint. The role of a good experiment is to make those explanations diverge under an observation that can actually be collected. The case below is hypothetical: none of the tabulated values come from published research, field trials or experiments performed for this book. They exist only to exercise design, plotting, arithmetic and interpretation.

Two competing accounts are: Hypothesis A: Reduced sink strength lowers delivery without changing vascular capacity. Hypothesis B: Feedback from soluble-sugar buildup suppresses assimilation in source leaves. Neither is assumed correct merely because it sounds familiar. Before collecting data, specify which mediator each requires, where the mediator would change and what difference in timing or environmental dependence would distinguish them. If both hypotheses make the same prediction for the primary outcome, adding sample size will not solve the identification problem; a better intervention or second observable is needed.

Proposed experimental design

Cross shading of mature source leaves with partial fruit removal. Follow leaf assimilation, sugar pools, phloem tracer travel and final fruit dry matter. Choose independent plants or plots as biological replicates, rather than treating multiple leaves from one plant as fully independent individuals. Randomize positions within the available growth space and rotate placements only if rotation itself will not change the intended light treatment. Document watering, temperature, developmental stage and the time of sampling, because plant physiology exhibits strong diel and developmental variation. Repeat technical instrument readings to estimate precision but analyze them as nested observations within a plant where appropriate.

Most concerning confound: Removing fruits changes hormone signals and sink number as well as carbon allocation; wound handling needs controls. A rigorous design should either hold this variable stable, measure it independently or include it as an explicit factor. Predeclare the primary endpoint and sample interval. Add a negative control for the manipulation, an untreated baseline and a positive control when one can be implemented safely and ethically. Technical success means the planned exposure and measurement occurred; it does not automatically mean the biological hypothesis was supported.

Synthetic data to analyze

Axes: Hours after labeling and Tracer fraction recovered at sink (%). For clarity the table uses serial index values 0–4, corresponding to five ordered levels or time points selected for this teaching example. Values are illustrative and carry no real-world standard errors. Treatment A and B are not empirical groups.

The endpoint change for series A is 39 in the stated outcome scale, and for series B is 25. Because we have no independent replication, there is no valid standard error, confidence interval, statistical test or effect-size claim about a population. Plot both series on shared axes, explain what is and is not comparable, and reconstruct one contrast by hand. A difference between these five-number curves is a mathematical observation about constructed numbers, not evidence for either biological hypothesis.

Interpretation, checks and follow-up

Include a complete carbon recovery accounting. Tracer concentration without destination pool sizes can overstate delivery. Compare patterns in at least three ways: absolute change, proportional change against the initial state, and the shape of the progression. Determine whether a lag, plateau or recovery segment is present. Ask what unmeasured variable could generate a similar pattern and what instrumentation would make it observable. Where the data describe a concentration or a fraction, do not call its slope a transport flux until a volume, area, residence time or mass-balance assumption has been supplied.

For an undergraduate replication assignment, prepare an analysis plan with a clear naming convention for raw measurements and calibration records. Preserve negative readings, missing samples and handling deviations rather than silently dropping them. Create a version-controlled script or spreadsheet with equations, units and plots, and keep an untouched raw-data copy. Estimate power and replication requirements only when realistic preliminary variance estimates exist; this table provides none. A scientifically useful final report should give the causal reasoning, the alternative mechanism, and limitations with the same prominence as the resulting graph.

Five decisions for the lab notebook

1. Define the independent sampling unit and explain why extra sensor readings cannot substitute for new plants or plots.
2. Select a measurable process variable from hydraulic conductance and tissue structure and explain why it is more mechanistic than the visual appearance of an organ.
3. Identify which part of Removing fruits changes hormone signals and sink number as well as carbon allocation; wound handling needs controls. can be standardized and which requires measurement as a covariate.
4. Describe the plot and model comparison that would genuinely differentiate the two stated hypotheses under a larger, replicated dataset.
5. Write a cautious conclusion that states exactly what would be learned if the new experiment agrees with series A, or if instead it agrees with series B. Never call the synthetic sequence published evidence.

Physiological accounts become more reliable when processes are linked without silently changing scale. In this unit the chain spans Xylem architecture and sap movement, Phloem pressure flow and sucrose loading, Source strength, sink strength and allocation, Transport under stress and phloem vulnerability, and Source–sink models and harvest outcomes. These five labels identify related mechanisms, but they do not establish which one limits performance in any particular specimen. Draw them as an interacting network rather than a rigid left-to-right progression. A local cellular process can determine a whole-plant response only if material transport, signaling or mechanical continuity connects the two. Conversely, a change in an organ’s environment can alter cellular behavior through feedback, even if the molecular machinery itself is unchanged.

Consider a thought experiment with two healthy plants that show equal final biomass after a seven-day treatment. The first maintained relatively stable daily assimilation but spent more carbon on defense or repair; the second experienced a severe early decline followed by compensatory growth. Their identical endpoint conceals opposite physiological trajectories and potentially different resource reserves. Sampling only the last day cannot distinguish them. For the five topics above, choose one early mediator and one later integrative variable whose trajectories would separate these alternatives. Include time and physical units in the predictions. Do not equate relative rate with absolute amount: a small organ can have a high rate per area but a small total flux.

Now change the experimental setting from a controlled growth cabinet to a variable outdoor canopy. Differences in illumination, boundary-layer conductance, root-zone moisture, developmental stage and nutrient supply arise over centimeters and hours. A fitted response in a single leaf may therefore be unsuitable for prediction of plant performance under the field regime. A better transferable model makes the limiting assumptions visible, specifies the range of conditions it is calibrated for and tests whether the relation survives at least one new condition. The chapter's simplified equations are tools for understanding direction and scale, not calibrated growth-management systems.

Finally create a mass and energy ledger. Identify water, carbon, mineral ions, ATP or reductant relevant to this unit. For each, record where it is supplied, where it is transformed, where it is stored and where it leaves the defined plant compartment. A balance that does not close should prompt a search for a missing flow or measurement error; it should not be forced to close by arbitrarily changing a measured term. Ask a partner to challenge one causal link and one numerical assumption. The strongest answer will describe what data could prove your account incomplete.

End-of-unit oral defense

Explain a plausible experiment in which xylem architecture and sap movement changes before source–sink models and harvest outcomes; then propose a second experiment in which the apparent order reverses because another condition becomes limiting. Explain what result would distinguish a direct mechanism from a feedback effect. Choose one of the worked calculations, double-check its dimensions and identify whether it estimates a state variable, a rate, a fraction or a phenomenological coefficient. Use at least two possible mechanisms for a common symptom such as slow growth, lower gas exchange or color change; give one measurement that discriminates them. End by stating a meaningful boundary to the generalization: species, tissue age, temperature range, developmental phase or assay method.

The reference list at the reader index provides textbook and review starting points. Review each primary claim against the original literature before presenting this manuscript as an accredited or peer-reviewed textbook. Next unit: Growth regulators and environmental signaling.