Changing Conditions on Photosynthesis
Counting bubbles rising from a piece of pondweed is one of the oldest experiments in biology, and one of the most powerful, because each bubble is oxygen, the visible signature of photosynthesis happening right in front of you. In this practical lesson you plan how to change a single condition, predict the outcome from the limiting-factor model, and design a fair test that turns "I think" into "I measured".
Orient yourself
Predict, scan the goals and key terms, and clear the misconception this lesson exists to fix.
Before You Read
warm-upYou want to test how light intensity affects the rate of photosynthesis in pondweed by counting oxygen bubbles per minute at different lamp distances.
Before reading: name the independent variable, the dependent variable, and two variables you would need to keep constant for a fair test.
Learning Intentions
goalsKnow
- How oxygen output measures the rate of photosynthesis
- The independent, dependent and controlled variables of the prac
- How to change a condition systematically
Understand
- Why a prediction is based on the limiting-factor model
- Why only one variable may change at a time
- How to make the test fair, reliable and valid
Can Do
- Plan and conduct a practical to predict how a changed condition affects photosynthesis
- Write a prediction with a scientific justification
- Identify and control variables for a valid result
Scan these before reading
vocabMisconceptions To Fix
watch outOdd one out: Three of these are sound practical design. Which one is wrong?
Measuring the Rate with Oxygen Bubbles
Why bubble count is a valid measure of photosynthesis
Core Content
Measuring the Rate with Oxygen Bubbles
+5 XPWhy bubble count is a valid measure of photosynthesis
Because oxygen is a product of photosynthesis, the faster oxygen is released, the faster photosynthesis must be occurring, so counting oxygen gives a direct measure of the rate.
Oxygen is a product of photosynthesis, so the rate at which oxygen bubbles are released from an aquatic plant (such as Elodea/Cabomba pondweed) is a valid measure of the rate of photosynthesis. You count bubbles per minute, or collect and measure the volume of gas produced in a fixed time.
The plant is placed in water containing dissolved carbon dioxide (often from sodium hydrogen carbonate), and a lamp provides light. As conditions change, the bubble rate changes, letting you measure the effect quantitatively.
Pause, copy the highlighted reason that oxygen output measures the rate into your book.
In the pondweed prac, the gas collected and counted as a measure of the rate of photosynthesis is _____.
Planning a Fair Test
Variables, controls and the predicted result
Planning a Fair Test
+5 XPVariables, controls and the predicted result
We just established that oxygen output measures the rate. That raises a question: how do we set up the experiment so the result is trustworthy? This card answers it → control every variable except the one you change, and predict from the model.
A fair test changes one condition at a time and holds all others constant, so any change in oxygen output can confidently be attributed to that one condition.
To investigate light intensity: the independent variable is light intensity (set by lamp distance), the dependent variable is the rate of oxygen production (bubbles/min), and controlled variables include temperature, CO₂ concentration, the species and mass of pondweed, and the time of counting. Repeat each measurement and average to improve reliability.
Text description
- A lamp shines on a beaker of water containing pondweed.
- The distance d from lamp to beaker is the independent variable: it sets the light intensity.
- The pondweed releases O₂ bubbles; bubbles per minute is the dependent variable.
Pause, copy the highlighted variables list into your book.
In this prac the rate of oxygen production is the dependent variable.
Changing both light and temperature together gives a more valid result.
Repeating each measurement and averaging improves the reliability of the results.
Plan the Investigation and Predict the Result
Do the activity in your book, then check what you have understood.
Activity 1
Plan the Investigation and Predict the Result
Pattern E, Plan-and-predict (WS02)
Choose ONE condition to investigate (light intensity OR carbon dioxide concentration OR temperature). Write a method that: (1) states your independent, dependent and three controlled variables; (2) describes how you change the IV in at least five steps; (3) includes repeats. Then write a prediction of the result and justify it using the limiting-factor model (for temperature, remember the optimum). Sketch the graph you expect.
Match each part of the pondweed investigation to the kind of variable it is:
- Distance of the lamp from the beaker
- Bubbles of oxygen counted per minute
- Water temperature, held with a heat shield
- Warming of the water by the lamp, if not controlled
- Dependent variable
- Confounding variable
- Independent variable
- Controlled variable
Predicting the Outcome from the Model
Turning the limiting-factor model into a tested prediction
Predicting the Outcome from the Model
+5 XPTurning the limiting-factor model into a tested prediction
We just planned a fair test. That raises a question: what should the result actually be? This card answers it → predict it before measuring, using the model from L05.
A good practical does not just observe, it predicts, so the data can confirm or challenge the underlying model.
Using the limiting-factor model, predict: for light intensity or CO₂, the rate will rise as the factor increases and then plateau when another factor becomes limiting; for temperature, the rate will rise to an optimum and then fall as enzymes denature. If the data match the prediction, they support the model; if not, you re-examine your method.
This prediction-and-test cycle is the heart of the scientific method: the prediction is risky (it could be wrong), and measuring oxygen output puts it to the test. Inverting the lamp distance (using 1/d²) lets you plot rate against true light intensity for a cleaner curve.
Add the highlighted predicted shapes (plateau vs optimum) to your notes before the check below.
Using the limiting-factor model, the predicted graph of rate against light intensity should:
Drive It: Find the Limiting Factor
+XP for exploringThe rate is held back by whichever factor is in shortest supply
Use the simulation. At normal air CO2, the rate has reached its plateau. What happens if you raise the light intensity further?
Use the simulation. True or false: raising the temperature always increases the rate of photosynthesis.
How crop scientists optimise glasshouses
Where this biology is doing real work outside the classroom.
How crop scientists optimise glasshouses
The same predict-change-measure cycle you practise with pondweed is exactly how agricultural scientists fine-tune commercial glasshouses. They systematically vary one condition, light spectrum from LED panels, CO₂ enrichment, or temperature, while holding the others constant, measure crop growth, and use the limiting-factor model to predict which single change will most increase yield. The pondweed bubble counter is a classroom version of a billion-dollar optimisation problem: feed a plant exactly the conditions it is short of, and nothing it already has in excess.
Outline a practical investigation to predict how changing light intensity affects the rate of photosynthesis in pondweed. Identify the variables, describe how the rate is measured, and state and justify your prediction.
Predict here sits inside a practical-design question, so two things are being marked: a workable method, and a prediction that is justified by a model rather than guessed. The model is the limiting-factor model from the previous lesson.
6 marks. Plan: justify the measure, name the three kinds of variable, describe how the independent variable is changed, add repeats, then state the prediction and the reasoning behind it.
Oxygen is a product of photosynthesis, so the rate at which oxygen is released is a valid measure of the rate of photosynthesis. A sprig of pondweed such as Elodea or Cabomba is placed in water containing dissolved carbon dioxide, supplied from sodium hydrogen carbonate, and the number of oxygen bubbles released per minute is counted. Collecting the gas and measuring its volume in a fixed time is a more precise alternative, since bubbles vary in size.
The independent variable is light intensity, changed by varying the distance of the lamp from the beaker across at least five settings. The dependent variable is the rate of photosynthesis, measured as oxygen bubbles per minute. Controlled variables include the temperature of the water, the carbon dioxide concentration, and the species and mass of pondweed used, together with the counting time.
Moving the lamp closer also warms the water, and temperature affects the rate independently, so lamp heating is a confounding variable. Place a heat shield — a glass tank of water — between the lamp and the beaker, or stand the beaker in a water bath, and check the temperature with a thermometer at each distance.
Repeat the count at each lamp distance at least three times and average the results, discarding any obvious outlier. Allow the pondweed a few minutes to equilibrate after each change of distance before beginning to count.
I predict that as light intensity increases the rate of oxygen production will rise steeply at first and then level off into a plateau. The justification is the limiting-factor model: at low intensity light is the factor in shortest supply, so increasing it raises the rate; once light is plentiful it is no longer limiting, and another condition, most likely carbon dioxide concentration or temperature, takes over as the constraint, so further increases in light produce no further rise. Plotting rate against the inverse square of the lamp distance gives a cleaner curve, because that is proportional to the true light intensity reaching the plant.
Because oxygen is a product of photosynthesis, the rate at which it is released is a valid measure of the rate of photosynthesis. A sprig of pondweed such as Elodea is placed in water containing dissolved carbon dioxide from sodium hydrogen carbonate, and the oxygen bubbles released per minute are counted, or the gas is collected and its volume measured in a fixed time, which avoids the variation in bubble size. The independent variable is light intensity, changed by moving the lamp to at least five measured distances; the dependent variable is the rate of oxygen production; and the controlled variables include water temperature, carbon dioxide concentration, the species and mass of pondweed, and the counting time. Because moving the lamp closer also warms the water, lamp heating is a confounding variable, so a heat shield or water bath is used and the temperature checked at each distance. Each count is repeated at least three times and averaged to improve reliability, with a short equilibration period after every change of distance. I predict that the rate will rise steeply as light intensity increases and then level off into a plateau. This follows from the limiting-factor model: at low intensity light is the factor in shortest supply, so raising it raises the rate, but once light is plentiful it is no longer limiting and another condition — carbon dioxide concentration or temperature — becomes the constraint, so further light produces no further increase. Plotting rate against the inverse square of the lamp distance gives a cleaner curve, since that is proportional to the true light intensity at the plant.
A student counts pondweed bubbles once at each of five lamp distances and reports that the rate of photosynthesis rises as the lamp gets closer. Evaluate the reliability and validity of this investigation, and recommend two specific changes that would improve it.
Evaluate asks for a judgement supported by evidence — here, a judgement about how far the conclusion is supported by the method. Note that the question names two criteria, reliability and validity, and they are not the same thing.
5 marks. Define the two criteria apart from each other, judge the investigation against each, then give two concrete improvements that follow from the weaknesses identified.
Reliability is the consistency of results when a measurement is repeated: reliable data can be reproduced. Validity is whether the investigation measures what it claims to, which requires that only the independent variable changes and that the dependent variable genuinely reflects the quantity of interest.
The investigation is unreliable. Each lamp distance was measured only once, so there are no repeats to average and no way to tell whether any single count is representative or an anomaly. Bubble counting is especially prone to this, because bubbles vary in size and are easily miscounted when the rate is fast.
The investigation is also of questionable validity, because moving the lamp closer increases not only the light intensity reaching the pondweed but also the temperature of the water. Temperature affects the rate of photosynthesis independently, so two conditions changed together and the observed rise cannot confidently be attributed to light alone. A further limitation is that counting bubbles assumes every bubble is the same size, which is not reliably true.
Two improvements follow directly. First, repeat the count at each distance at least three times and average, discarding outliers, which addresses reliability. Second, place a heat shield or water bath between the lamp and the beaker and verify the temperature at every distance, which removes the confounding variable and addresses validity; collecting and measuring the volume of gas rather than counting bubbles would improve the measurement further. Until these changes are made the trend reported is plausible but not established, because the method cannot separate the effect of light from the effect of heat.
Reliability is the consistency of a measurement on repetition, while validity is whether the investigation measures what it claims to, which requires that only the independent variable changes. This investigation is unreliable, because each lamp distance was measured only once, so there are no repeats to average and no way to judge whether any count is representative or anomalous — a particular risk with bubble counting, since bubbles vary in size and are easily miscounted at speed. Its validity is also questionable, because moving the lamp closer raises both the light intensity and the temperature of the water, and temperature affects the rate of photosynthesis independently; two conditions therefore changed together and the observed rise cannot be attributed to light alone. Two changes would improve it. First, repeat each count at least three times and average, discarding outliers, which addresses reliability. Second, interpose a heat shield or stand the beaker in a water bath and check the temperature at each distance, which removes the confounding variable and addresses validity, with gas volume collected over a fixed time as a more precise measure than bubble counts. Until then the reported trend is plausible but not established, because the method cannot separate the effect of light from the effect of heat.
Practise independently
Answer in your own words first. The model answers are for checking, not for copying.
Multiple Choice
+5 XPA fresh set drawn from this lesson's question bank, feedback shown immediately. +5 XP per correct · +25 XP all correct
Pick your answer, then rate your confidence, that tells the system what to drill next.
Short Answer, 3 questions · 14 marks
+5 XPCreateBand 6(5 marks) 1. Outline a practical investigation to predict how changing light intensity affects the rate of photosynthesis in pondweed. Identify the variables, describe how the rate is measured, and state and justify your prediction.
1 mark: IV (light intensity) and DV (O₂ rate) identified · 1 mark: two valid controlled variables · 1 mark: method to change IV and measure rate (with repeats) · 1 mark: prediction of rising-then-plateau curve · 1 mark: prediction justified using the limiting-factor model
AnalyseBand 5(4 marks) 2. A student counts pondweed bubbles once at each of five lamp distances and reports that the rate rises as the lamp gets closer. Evaluate the reliability and validity of this investigation, and describe two specific changes that would improve it.
1 mark: identifies the absence of repeats as a reliability weakness · 1 mark: identifies lamp heating (an uncontrolled variable) as a validity weakness · 1 mark: proposes repeats and averaging · 1 mark: proposes a heat shield or water bath, or measuring gas volume rather than counting bubbles
CreateBand 5–6(5 marks) 3. Design an investigation to predict how changing carbon dioxide concentration affects the rate of photosynthesis in pondweed. State your independent, dependent and three controlled variables, outline how you would change the independent variable across at least five values, state your prediction, and justify it using the limiting-factor model.
1 mark: IV (CO₂ concentration, varied by sodium hydrogen carbonate mass) and DV (rate of O₂ production) · 1 mark: three valid controlled variables · 1 mark: at least five values of the IV plus repeats · 1 mark: prediction of a rise then a plateau · 1 mark: prediction justified by limiting factors, naming what limits at the plateau
Show all answers
Multiple choice
MC answers and full explanations are shown inline as you complete each question. Use the retry button to attempt a fresh set from the lesson bank.
Short Answer Model Answer
Q1 (5 marks): Place a sprig of pondweed in water containing dissolved CO₂ and shine a lamp on it. The independent variable is light intensity, changed by varying the lamp distance over at least five settings; the dependent variable is the rate of photosynthesis, measured as the number of oxygen bubbles released per minute (or the volume of gas collected in a set time). Controlled variables include temperature (use a heat-shield/water bath), CO₂ concentration, and the species and mass of pondweed. Repeat each measurement three times and average to improve reliability. Prediction: as light intensity increases, the rate will rise (light is the limiting factor), then plateau once light is plentiful and another factor such as CO₂ or temperature becomes limiting. This is justified by the limiting-factor model: the rate is controlled only by the factor in shortest supply, so increasing light helps only while light is limiting.
Q2 (4 marks): The investigation is not reliable, because each distance was measured only once, so there is no way to tell a real trend from a one-off miscount, and bubbles vary in size and are easy to miss. It may also not be valid, because a lamp moved closer warms the water as well as lighting it, so temperature changes alongside light intensity and the effect cannot be attributed to light alone, temperature has become a confounding variable. Two improvements: first, repeat each distance at least three times and average the counts, which improves reliability; second, place a heat-absorbing water bath or glass heat shield between the lamp and the beaker and check the temperature with a thermometer at every distance, which restores validity by controlling temperature. A third worthwhile change is to collect the gas in a capillary tube and measure its volume in a fixed time, which removes the error in counting bubbles of different sizes.
Q3 (5 marks): The independent variable is carbon dioxide concentration, varied by dissolving five different masses of sodium hydrogen carbonate (for example 0.2, 0.4, 0.6, 0.8 and 1.0 g per 100 mL) in the water around the pondweed. The dependent variable is the rate of photosynthesis, measured as the volume of oxygen collected in a fixed time or as bubbles per minute. Controlled variables are light intensity (a fixed lamp distance with a heat shield), temperature (checked with a thermometer or held in a water bath) and the species, mass and length of the pondweed sprig; the same time interval is used for every reading, and each reading is repeated three times and averaged. Prediction: the rate will rise as CO₂ concentration increases and then level off at a plateau. Justification: by the limiting-factor model the rate is set only by the condition in shortest supply. At low concentrations CO₂ is that condition, so adding more raises the rate. Once CO₂ is plentiful it is no longer limiting, and light intensity or temperature takes over as the limiting factor, so further CO₂ produces no further increase and the curve plateaus.
Retrieve and reflect
Use the spaced-review session, then compare how your thinking has changed since your opening prediction.
Check what actually stuck
A short retrieval session mixing this lesson with earlier ones, so the ideas move into long-term memory instead of fading by the exam.
Lock it in
bossYou can now plan a valid photosynthesis prac and predict its result from the limiting-factor model. With the plant's food-making covered, the next lessons follow the plant's plumbing, how water and sugar are transported through xylem and phloem.
↩ Back to focus-area hubHow did your thinking change?
At the start you identified the variables. The independent variable is light intensity (lamp distance), the dependent variable is the oxygen bubble rate, and controlled variables include temperature, CO₂ level and the pondweed itself. Keeping these constant is what makes the test fair and the conclusion valid.
If you suggested changing more than one factor, revisit the misconception box: only the IV may change, or you cannot attribute the effect.