explain the effects of changes in light intensity, carbon dioxide concentration and temperature on the rate of photosynthesis

Cambridge International AS & A Level Biology 9700 – Investigation of Limiting Factors in Photosynthesis

Learning Objective

Explain how light intensity, carbon‑dioxide (CO₂) concentration and temperature affect the rate of photosynthesis, and design a practical investigation that isolates each factor while keeping the other two constant.

Key‑Point Box – Biochemical Links

Key Concepts

• Photosynthetic rate – amount of O₂ evolved (or CO₂ fixed) per unit time, usually expressed as mL O₂ min⁻¹ or µmol CO₂ m⁻² s⁻¹.
• The three principal environmental variables that can become limiting are:

  • Light intensity – provides the energy for the light‑dependent reactions.
  • CO₂ concentration – substrate for the Calvin cycle; limits Rubisco activity.
  • Temperature – controls the kinetic rate of all photosynthetic enzymes.

• Each factor shows a characteristic response curve: rapid rise when it is limiting, followed by a plateau when another factor becomes limiting.

Experimental Design – Isolating Each Factor

Three separate investigations are carried out, one for each variable. All other variables are kept constant for the whole series of trials.

Procedure (outline)

1. Grow a healthy, fully expanded plant (e.g., Phaseolus vulgaris) under uniform conditions.
2. Using a cork borer, cut leaf discs of 1 cm diameter; record the exact area (π r²) for later normalisation.
3. Place each disc in a sealed glass chamber containing 10 mL of distilled water.
4. Allow the disc to equilibrate for 5 min in darkness.
5. Set the experimental variable to the first value in the series (e.g., 200 µmol m⁻² s⁻¹).
6. Start the gas syringe and record the change in gas volume every minute for 10 min.
7. Return the chamber to the initial conditions, then repeat steps 3–6 for each subsequent value.
8. Carry out three independent replicates for every treatment.
9. Randomise the order of discs (or chambers) for each series to avoid systematic bias.
10. Calculate the mean rate of O₂ evolution (mL min⁻¹) and the standard deviation (SD) for each treatment.

Control‑Checklist (to be completed before each series)

• Verify CO₂ concentration with a calibrated gas‑meter (or NaHCO₃‑HCl titration) before starting a light‑intensity series.
• Check light intensity with a quantum sensor; record the exact µmol m⁻² s⁻¹.
• Confirm chamber temperature with a digital thermometer; allow at least 2 min for stabilisation after any change.
• Ensure water volume is exactly 10 mL and that no air bubbles are trapped under the leaf disc.
• Record ambient humidity; if it varies >5 % between trials, note it as a possible source of error.

Data Presentation

Example Table – Light Intensity

Graphical Representation

• Plot rate of photosynthesis (y‑axis) against the variable tested (x‑axis).
• Fit the appropriate trend line and report the fitted parameters:

  • Light intensity – Michaelis–Menten‑type curve:
    

    r = (r_max·I) / (K_I + I)
    

    where I = photon flux density, K_I = half‑saturation constant.

  • CO₂ concentration – Michaelis–Menten equation for Rubisco:
    

    r = (r_max·[CO₂]) / (K_m + [CO₂])

  • Temperature – Gaussian/quadratic approximation:
    

    r = r_opt·e^{-(T‑T_opt)² / (2σ²)} (or a second‑order polynomial for simplicity).

• Statistical analysis:

  • Calculate mean ± SD for each treatment.
  • Perform a one‑way ANOVA (or t‑tests) to test for significant differences; use p < 0.05** as the significance level.
  • Report the result as, e.g., “The increase from 400 to 600 µmol m⁻² s⁻¹ was significant (p = 0.02).”

Theoretical Explanation

1. Light intensity
   

   Photons drive the light‑dependent reactions (PSII → H₂O → O₂ + e⁻; electron transport → ATP & NADPH). At low intensities the rate is directly proportional to photon flux:

   r_light = ϕ I

   where ϕ = quantum efficiency. When all photosystems are saturated, the curve plateaus at r_max (light‑saturated rate).

2. CO₂ concentration
   

   CO₂ is the substrate for Rubisco. Rubisco follows Michaelis–Menten kinetics:

   r_CO₂ = \frac{r{\max}\,[\text{CO}2]}{K{m}+[\text{CO}2]}

   At low CO₂ the rate rises linearly; at high CO₂ it approaches r_max, indicating that another factor (usually light) becomes limiting.

3. Temperature
   

   Enzyme activity increases with temperature according to the Arrhenius equation until the optimum temperature (T_opt) is reached. Beyond this point, thermal denaturation and increased photo‑respiration reduce activity:

   rT = r{\text{opt}} e^{-\frac{(T-T_{\text{opt}})^2}{2\sigma^{2}}}

   The curve is asymmetric: the decline after T_opt is steeper than the rise before it.

Data Analysis and Interpretation

• Light intensity – Rapid increase up to ≈ 800 µmol m⁻² s⁻¹ shows light limitation; the plateau beyond this point indicates saturation of the photosystems and that CO₂ or temperature becomes the next limiting factor.
• CO₂ concentration – Linear rise at low concentrations demonstrates substrate limitation of Rubisco; leveling‑off at ≈ 0.24 % v/v shows Rubisco operating near V_max. Further CO₂ increase will not raise the rate unless light intensity is also increased.
• Temperature – The curve peaks around 25–30 °C for most C₃ plants, reflecting the optimum for Rubisco and the electron‑transport chain. The sharp decline above 35 °C indicates thermal inhibition (enzyme denaturation, increased photo‑respiration). Low rates at 10–15 °C are due to reduced kinetic energy and slower enzyme turnover.

Conclusions

1. Photosynthetic rate is directly proportional to each variable only within the range where that variable is limiting.
2. Each factor displays a characteristic saturation point; beyond this point the next factor becomes the controlling variable.
3. Understanding these relationships is essential for:

   • Optimising greenhouse lighting and CO₂ enrichment strategies for crop yield.
   • Predicting plant performance under climate‑change scenarios (e.g., rising temperatures, altered atmospheric CO₂).

Evaluation – AO3 Checklist

• Reliability – Use three replicates, randomise treatment order, and calibrate equipment before each series.
• Validity – Keep all non‑tested variables constant; verify that the chosen leaf area is identical for every disc.
• Precision – Report mean ± SD; use ANOVA to confirm that observed differences are statistically significant (p < 0.05).
• Sources of error:

  • Leakage from the gas syringe or chamber.
  • Variation in stomatal aperture caused by unnoticed changes in humidity.
  • Inaccurate CO₂ mixing – mitigate by measuring with a gas‑meter before each run.

• Improvements:

  • Use a closed‑circuit infrared gas analyser (IRGA) for continuous O₂/CO₂ monitoring.
  • Measure chlorophyll fluorescence (F_v/F_m) simultaneously to assess photosystem II efficiency.
  • Include a water‑stress treatment to explore the interaction between stomatal conductance and the three primary factors.

Possible Extensions

• Investigate the effect of water availability (soil moisture, relative humidity) on stomatal conductance and photosynthetic rate.
• Compare C₃ and C₄ species to highlight differences in CO₂ saturation curves and temperature optima.
• Use chlorophyll fluorescence (F_v/F_m) as a non‑destructive proxy for photosystem II efficiency.
• Model the combined effect of two variables (e.g., light × CO₂) using response‑surface methodology.

Safety and Ethical Considerations

• Handle glass gas syringes with care; wear safety glasses and gloves.
• When enriching CO₂, work in a well‑ventilated area or under a fume hood; avoid concentrations >5 % in the laboratory.
• Dispose of plant material and any chemical reagents (e.g., NaHCO₃, HCl) according to school and local regulations.
• Use a single plant species throughout the investigation to minimise ecological impact and maintain consistency.