Here are a few of the standout observations that helped me develop my sampling scheme:
- Glossy buckthorn patches vary in density and I wanted to avoid over-representing plants in areas of high density or under-representing plants in areas of low density
- For the purposes of my study I want to follow individuals, therefore areas that are bare of Glossy buckthorn are not good places to look for plants to follow (yeah - this one is obvious)
- Glossy buckthorn grows in varying ecological conditions (under story, gaps, riparian zones, uplands, etc.) and my samples should come from as many of these conditions as is feasible
- The ecological conditions mentioned above are generally clumped. That is, the ecological conditions I'm interested in usually make up their own patch (e.g. a large patch of understory or wetland)
With these observations and thoughts in mind, I decide that the most important aspect of my sampling scheme should be that I select individual plants to follow across different plant densities and in different ecological conditions. So here's the final sampling scheme I chose:
- Ecological conditions - I selected a specific combination of ecological conditions (e.g. upland-understory forest) and using a GPS outlined the borders of a Glossy buckthorn infestation within the bounds of the borders of the ecological condition.
- Individual plants - Once I had the border of an infestation, I selected 15 to 20 random points within these bounds where I set up 2x2m plots, or quadrats. Within these plots, I tagged and measured all Glossy buckthorn plants that were taller than 10cm. Plants smaller than 10cm were considered saplings, which were sampled differently.
This type of sample scheme is essentially a stratified-random design, stratified across ecological condition and random within that condition. So far I think it has worked out pretty well.
|Random 2x2m plot in a large forest gap. Orange flags mark the corners of the quadrat.|