2.4.1 Data Collection Techniques
Light Traps: These are used for nocturnal flying insects; organisms are attracted to a light source and captured in a container for counting and identification.
Capture-Mark-Recapture: This technique involves capturing a sample of a population, marking them harmlessly, and releasing them to mix back into the wild.
Data Utility: By capturing a second sample later and counting the number of marked individuals, researchers can estimate the total population size without needing to count every single individual.
Questionnaires: These use predefined questions to collect data from large groups. They are efficient for gathering quantitative data (e.g., using Likert scales) but rely heavily on participant honesty.
Interviews: These involve direct, in-depth conversations. They excel at gathering qualitative data and allow for follow-up questions to explore complex environmental attitudes.
Trade-offs: While questionnaires offer breadth and anonymity, interviews offer depth and clarity, though they are more susceptible to interviewer bias.
| Feature | Questionnaire | Interview |
|---|---|---|
| Data Type | Primarily Quantitative | Primarily Qualitative |
| Sample Size | Large (High efficiency) | Small (Time-intensive) |
| Cost | Low | High |
| Bias Risk | Low (Standardized) | High (Interviewer influence) |
Organism Type: Use quadrats for plants/sessile animals; use traps or capture-mark-recapture for mobile animals like insects or mammals.
Environmental Context: Use Secchi discs for aquatic clarity; use light traps specifically for nocturnal species.
Identify the Organism: Always check if the organism in the question is motile or non-motile before selecting a sampling technique.
Bias Awareness: When discussing Secchi discs or interviews, always mention researcher bias (e.g., differences in eyesight or leading questions) as a potential source of error.
Units and Precision: Ensure you specify the size of the quadrat (e.g., ) as this affects the density calculations.
Reliability: Mention that repeating samples (increasing the number of quadrats or interviewees) increases the reliability of the data by reducing the impact of anomalies.