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Fieldwork can reveal an association between a measured environmental variable and a species’ distribution. Sampling design determines whether the pattern is representative and what causal claims it supports.
What you'll be able to do
Choose a named species and environmental variable such as light intensity, soil moisture or pH. Define the habitat and the biological response: abundance, percentage cover or frequency are different measurements. Use a method suited to the organism rather than trying to count indistinguishable individuals of a spreading clonal plant.
A transect can follow a gradient, while random quadrats can characterise variation over an area. Several independently located transects or sites can strengthen generalisation. Record selection rules before collecting data so conspicuous patches do not receive preferential sampling.
At each sampling position, measure the environmental variable and the biological response in a comparable local area. Use consistent quadrat size, identification and inclusion rules, and appropriate instrument calibration. Record time and relevant conditions because variables such as light and moisture can change during the survey.
Repeat environmental readings where useful and retain raw data. Avoid trampling or otherwise changing later samples as the work proceeds. Multiple points along one small gradient may share conditions and should not automatically be treated as independent evidence about every habitat of that type.
A scatter graph can display paired environmental and abundance data, and an appropriate correlation analysis can assess an association. The test and graph must match the measurement scales and design. A weak association may reflect a genuinely small relationship, measurement noise, limited range or other influences.
A correlation alone does not prove that the measured factor causes the distribution. Soil, competition, grazing and other variables may covary. Explain a plausible biological mechanism, evaluate alternatives and consider a controlled manipulation if a causal claim is required. Statistical significance does not replace evaluation of effect size or ecological importance.
Try each question before opening the worked response. Use precise biological terms, connect each cause to its effect, and check units and assumptions when interpreting data.
Common mistakes to avoid
Key takeaways
Test yourself
Answer a few questions, then review anything you missed.
Start with the quiz above to check the key ideas. You can also choose another topic from the practice library.