Effects of climate change and predictionsEdexcel A-Level Biology A: Revision notes

Extrapolation extends a trend in data beyond the measured range to make a prediction, for example continuing a graph of rising temperature into the future. It assumes that the trend and the causes behind it stay the same.

Climate scientists build models that use data on greenhouse gas concentrations, temperature, ocean currents, ice cover and solar output to predict future climate. Different emission scenarios give different predictions.

Limitations of models:

• the climate is very complex, with many interacting factors and feedback loops
• future human emissions and actions cannot be known
• data may be incomplete or cover only a short time
• different models give different predictions

Predictions are best treated as probabilities, which improve as models are tested against real data and refined.

Climate change alters rainfall patterns (amount, timing and reliability) and seasonal cycles (earlier springs, longer growing seasons, milder winters).

Each species has a tolerance range of temperature and water availability. As the climate changes, areas can fall outside that range and others fall into it, so the distribution of species changes: ranges move towards the poles and up mountains, and species that cannot move or adapt decline. Changes in plant distribution then affect the animals that feed on the plants.

Changed rainfall alters water availability for plants, affecting growth, flowering and survival, and so affecting entire food webs.

Warmer temperatures speed up enzyme-controlled reactions in many organisms, so development is faster and life-cycle events happen earlier, such as budburst, flowering, egg hatching, insect emergence and breeding.

Because species respond differently, the timing of linked species can become mismatched. For example, caterpillars that hatch 14 days earlier while their predators, great tits, breed only 4 days earlier leave the chicks short of food at the critical time. The result can be lower survival and falling populations.

The effect of temperature on development can be studied directly, for example in the rate of seedling growth or the hatching of brine shrimp eggs.

As temperature rises towards the optimum, molecules have more kinetic energy, so there are more frequent collisions with enough energy between enzyme and substrate, and the rate of reaction increases.

Above the optimum, the hydrogen bonds and other weak bonds that maintain the tertiary structure break. The active site changes shape, the substrate no longer fits, and the enzyme is denatured, so the rate falls rapidly.

Impact: the metabolic rate of ectothermic animals rises with temperature; photosynthesis and growth in plants are affected as enzymes such as rubisco work at different rates; and microorganisms grow and decompose material faster in warmth, but heat that is too high kills them. Climate change therefore alters growth, development and survival.

Q10 shows how much the rate of a reaction changes for a 10 °C rise in temperature:

Q10 = rate at (T + 10) °C ÷ rate at T °C

For an interval other than 10 °C, Q10 = (R₂ ÷ R₁)^(10 ÷ (T₂ − T₁)).

Method: use water baths at five or more temperatures; keep the pH constant with a buffer and keep the concentrations and volumes of enzyme and substrate constant; allow each solution to reach the temperature before mixing; measure the initial rate (for example volume of oxygen from catalase, or time for starch to disappear with amylase). Initial rate is the gradient of the tangent at time zero because later the substrate is used up and the enzyme may denature. Repeat and calculate means.

Worked example: the rate rises from 0.012 to 0.030 cm³ s⁻¹ between 20 °C and 30 °C, so Q10 = 0.030 ÷ 0.012 = 2.5. A Q10 of about 2 is typical, meaning the rate doubles for every 10 °C rise below the optimum.

Aim: to investigate how temperature affects development, for example the growth of seedlings (cress or mustard) or the hatching of brine shrimp eggs.

Method: set up samples at several temperatures using incubators or thermostatically controlled water baths. Keep other variables constant: the number and source of seeds or eggs, the volume and salt concentration of the water (brine shrimp), light, and water supply (seedlings). Measure the dependent variable at set times, such as the mean seedling length or mass, or the number or percentage of shrimp that hatch each hour. Use many seeds or eggs at each temperature and calculate means.

Results: development rate rises to an optimum and then falls as enzymes denature. Ethics: use eggs or seeds only in numbers needed and dispose of living organisms humanely.