Sample interview questions: How do you account for potential biases in wildlife survey data?
Sample answer:
Accounting for Potential Biases in Wildlife Survey Data
1. Sampling Design Biases:
- Non-random sampling: Ensure sampling sites are randomly selected to avoid over- or underrepresentation of specific habitats or species.
- Temporal bias: Consider seasonality and diurnal/nocturnal patterns in species activity to minimize time-related biases.
- Observer bias: Standardize survey methods, minimize inter-observer variability, and train observers thoroughly.
2. Detection Biases:
- Cryptic species: Account for species that are difficult to detect due to camouflage, elusive behavior, or nocturnal habits. Utilize multiple survey techniques.
- Environmental factors: Consider factors such as vegetation density, weather conditions, and noise levels that can influence detectability.
- Habituation: Monitor changes in species behavior over time as they become accustomed to survey methods.
3. Estimation Biases:
- Small sample size: Ensure sample sizes are adequate for reliable estimates. Consider using models to supplement data.
- Extrapolation errors: Read full answer