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Unlock This CourseWhen scientists study ecosystems, they can't count every single organism - that would take forever! Instead, they use clever sampling methods and maths to get reliable estimates. Statistical analysis helps us understand patterns in nature, compare different habitats and track changes over time.
Key Definitions:
Imagine trying to count every daisy in a field - impossible! Statistics let us take samples and make reliable predictions about the whole population. It's like tasting a spoonful of soup to check if the whole pot needs more salt.
Scientists use different techniques to collect data about populations. The method depends on what they're studying and where they're working.
Quadrats are square frames (usually 0.5m × 0.5m or 1m × 1m) thrown randomly into an area to sample plants and small animals. This method works best for organisms that don't move much.
Throw quadrats randomly to avoid bias. Use random number tables or apps to pick coordinates. This gives every part of the habitat an equal chance of being sampled.
Place quadrats at regular intervals along a line. Good for studying how species change across an area, like from a pond edge to dry land.
Divide the habitat into different zones and sample each one separately. Useful when the area has obviously different sections.
Studying Buttercups in School Fields: Students used 20 random quadrats (1m²) across the school field. They found an average of 12 buttercups per quadrat, suggesting about 12 buttercups per square metre across the whole field. With the field being 2000m², they estimated roughly 24,000 buttercups total!
Transects are brilliant for studying how ecosystems change from one area to another - like from a beach into sand dunes, or from a forest edge into open grassland.
A line transect involves laying a measuring tape across the habitat and recording what species touch the line at regular intervals (every metre, for example). A belt transect is wider - you place quadrats at intervals along the line to get more detailed data.
Quick and easy to do. Shows clear patterns of change. Good for covering large distances. Perfect for studying zonation patterns.
Once you've collected your sample data, you need to crunch the numbers to estimate the total population size.
Population density = Total number counted ÷ Total area sampled
Total population = Population density × Total area of habitat
Add up all the individuals found in your quadrats. If you used 10 quadrats and found 5, 8, 3, 7, 9, 4, 6, 8, 5, 7 daisies, your total is 62.
Work out the average per quadrat: 62 ÷ 10 = 6.2 daisies per quadrat. If each quadrat was 1m², that's 6.2 daisies per square metre.
Multiply by the total area. If the field is 500m², the estimated population is 6.2 × 500 = 3,100 daisies.
Raw data doesn't tell us much on its own. We need to analyse it using statistical measures to spot patterns and make comparisons.
These three measures help us understand the 'typical' value in our data set.
Mean: Add all values and divide by how many you have. Most commonly used in ecology.
Median: The middle value when arranged in order. Less affected by extreme values.
Mode: The most common value. Useful for categorical data.
This measures how spread out your data is. A small standard deviation means most values are close to the mean. A large one means the data is more scattered.
Comparing Two Ponds: Pond A had mayfly larvae counts of 8, 9, 7, 8, 8 (mean = 8, standard deviation = 0.7). Pond B had counts of 2, 15, 6, 12, 5 (mean = 8, standard deviation = 5.1). Both have the same mean, but Pond B is much more variable - suggesting it might be less stable.
Scientists love graphs because they make patterns in data jump out at you. Different types of graphs suit different types of ecological data.
Perfect for comparing different species or habitats. The height of each bar shows the value. Great for showing biodiversity data.
Brilliant for showing changes over time or distance. Often used for transect data or population changes over years.
Show relationships between two variables. Might plot temperature against number of insects, for example.
No scientific study is perfect - there are always sources of error that can affect results. The trick is knowing about them and minimising their impact.
This happens when your sample isn't representative of the whole population. Maybe you only sampled the sunny spots, or avoided muddy areas. Use random sampling to reduce this.
Different people might count differently or identify species wrongly. Train all observers and use identification guides.
Populations change throughout the day and year. Sample at the same time of day and note the season.
Measuring tools might be inaccurate. Calibrate equipment and use the same tools throughout the study.
Statistical analysis isn't just academic - it has real-world applications in conservation, farming and environmental monitoring.
Monitoring Butterfly Populations: The UK Butterfly Monitoring Scheme uses transect walks to track butterfly numbers. Volunteers walk the same route weekly, counting butterflies within 5 metres. This 40-year dataset shows that many species are declining, helping conservationists target their efforts where they're needed most.
Good statistical analysis helps us make evidence-based decisions about managing ecosystems. Whether it's deciding where to create nature reserves or working out if pollution is affecting wildlife, statistics provide the proof we need.