Sampling averages and ecological graph skills
Ecology • Organisation of an ecosystem
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Key concepts
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Mean, median and mode for organism abundance
Mean represents the arithmetic average of sample counts and provides the most common basis for estimating abundance over an area. The mean is calculated by summing all counts and dividing by the number of samples; when quadrat size is smaller than 1 m2, conversion to per m2 requires multiplying by the appropriate area factor .\nMedian describes the middle value when sample counts are ordered. Median reduces the influence of extreme values (outliers) and provides a robust central measure when distributions are skewed.\nMode identifies the most frequently occurring count in the sample set. Mode indicates the most common abundance class and helps identify modal peaks in categorical or grouped data.
Calculating arithmetic means and converting quadrat counts
Arithmetic mean calculation follows a fixed procedure: sum all sample counts then divide by the number of samples. For quadrats smaller than 1 m2, scale the mean to per m2 by using the ratio of 1 m2 to the quadrat area (for example, multiply counts from a 0.5 m × 0.5 m quadrat by 4) .\nPopulation estimation uses the scaled mean and the total study area: abundance estimate = mean per m2 × total area in m2. Larger and more random samples increase precision and reduce sampling error; ecologists commonly sample a limited percentage of a site to balance effort and representativeness .
Graph types and plotting ecological data
Scatter graphs plot two quantitative variables as points and reveal correlations; a line of best fit or curve summarises the trend. Bar graphs compare discrete categories and histograms display continuous frequency distributions where bars touch for adjacent ranges .\nAxes require correct labels and units, consistent scales and clear keys for multiple data series. Data points plot first; then an appropriate line of best fit or smoothed curve shows the general pattern. Anomalous results should be identified and justified in an evaluation of data quality .
Interpreting predator–prey cycles
Predator–prey cycles show linked oscillations in population size where increases in prey allow predator numbers to rise, and then rising predator numbers cause prey numbers to fall; predator peaks lag behind prey peaks because predator reproduction and population response take time .\nGraphs of predator–prey systems require attention to phase lag, amplitude of oscillations and long-term stability. Stable ecosystems show bounded oscillations rather than unlimited growth or collapse, and interpretation must consider other limiting factors such as food availability, disease and abiotic changes.
Sampling techniques, transects and Required Practical method
Random sampling places quadrats at coordinates selected by random number tables or generators so that every potential sample plot has an equal chance of selection; this reduces sampling bias . Systematic sampling uses a transect line and places quadrats at regular intervals to reveal changes across an abiotic gradient; abiotic variables (light, moisture, pH) are recorded at each quadrat to correlate distribution with environmental change .\nRequired practical procedures specify calculating quadrat area, placing 10 or more random quadrats, recording counts or percentage cover, calculating means, scaling to per m2 and estimating total abundance across the site. Repeating samples and combining class data improves validity; an evaluation considers random and systematic error, anomalous results and possible improvements to method and equipment .
Key notes
Important points to keep in mind