Main sampling considerations: What - Where - How many - When - How

1. Main sampling considerations: What - Where - How many - When - How What are you going to measure? Temperature? Humidity? Windspeed? Soil temperature? What you measure may depend on access to equipment and the kind of questions you need to ask.

2. Main sampling considerations: What - Where - How many - When - How This is an important question and requires a sampling scheme to ensure the measurements you take are unbiased and repeatable by other people. Random sampling - generally poorly done by people who think random means “wherever I feel like measuring”. Proper random sampling requires use of random number tables to generate true random locations.

3. Main sampling considerations: What - Where - How many - When - How Random sampling - Method: divide area into grid and number the intersections.Use random number tables to determine which intersections to sample. Problem - Being random you may - by coincidence - find none of the sample points include the conifers in the forest.

4. Main sampling considerations: What - Where - How many - When - How Systematic sampling - one of the commonest and simplest methods. Method: divide area into grid and number the intersections. Choose a regular “system” to select the sample points - eg every 3rd intersection. Problem - Might still - by coincidence - fail to pick any samples in smaller subgroups (like conifers). May be influenced by patterns in what you measure - eg firebreaks every 50 metres might consistently be sampled, even though they are NOT representative of the rest of the wood.

5. Main sampling considerations: What - Where - How many - When - How This is an important question and requires a sampling scheme to ensure the measurements you take are unbiased and repeatable by other people. Stratified sampling - a more sophisticated method. Method: work out the natural subgroups in the data and their relative importance (eg conifers .v. broadleaved; margins .v. centre etc). Ensure you have a representative number of systematic samples from each area. Problem - Open to bias - who decides which subgroups are important? Young conifer Old broadleaved Young broadleaved

6. Eg 20 samples 12 broad-leaved 8 conifer 4 young 8 mature 2 young 6 mature Main sampling considerations: What - Where - How many - When - How As many as possible. The representativeness of your result is better if there are more samples. Take sufficient samples so that subgroups within the data (eg conifers .v.broadleaved) can be separated out and still have enough data in each group to allow statistical significance to be worked out. More = Better Q: Do young conifers have a different microclimate from young broadleaved trees? Impossible to answer! Sample sizes too small!

7. Main sampling considerations: What - Where - How many - When - How Most ecosystems will have strong seasonal patterns.The time of year will influence the results you obtain. In summer conifers may produce a cooler microclimate as a result of their dense shade. In winter the dense foliage may be an insulator keeping the microclimate warmer. Daily patterns are equally important. Results in the morning, midday, mid afternoon and night might all demonstrate different microclimate patterns. A well designed sample scheme will take these into account.

8. Main sampling considerations: What - Where - How many - When - How Most studies are compromise. The systems you measure are infinitely variable with a large number of factors. You have limited time and resources. Do you take detailed comprehensive measurements of all factors but end up with a small sample size because it took so long? Or should you go for a large sample size covering all the main areas and ecosystem types - but only measuring one variable - eg temperature? There is not a right or wrong answer but you need to understand that what you measure will determine the questions you can answer.

9. This is the end of the slide show.