1. Not knowing your species

Understanding the study species is essential for considering biases and interpreting data.

2. Not knowing exactly why you are surveying

Think exactly what the question is and what data are required to answer it. How will the data be presented and analysed?

3. Counting in one or a few large areas rather than a large number of small ones

A single count gives no measure of the natural variation and it is then hard to see how significant any changes are. This also applies to quadrats.

4. Not giving precise information as to where sampling occurred

Give the precise date and location. ‘Site A, behind the large tree’ or ‘near to the road’ may be sufficient now but might mean nothing later. Use of the GPS is the easy solution to this.

5. Only sampling sites where the species is abundant

It seems obvious to concentrate upon sites where the species is known to occur. However, without knowing the density where it is scarce, it is impossible to determine the total population size.

6. Changing the methods in monitoring

Unless there is a careful comparison of the different methods, changing the methods prevents comparisons between the years.

7. Pretending that the samples taken within a site are replicates

For example, if the project involves comparing logged and unlogged forest, but you have just collected a number of samples in one area of each, then there is only one replicate of each treatment and it is impossible to obtain statistics.