To sample or not to sample? That is the question... For the vegetation scientist

Abstract

Lájer (2007) raised the problem of using a non-random sample for statistical testing of plant community data. He argued that this violates basic assumptions of the tests, resulting thus in non-significant results. However, a huge part of present-day knowledge of vegetation science is still based on non-random, preferentially collected data of plant communities. I argue that, given the inherent limits of preferential sampling, a change of approach is now necessary, with the adoption of sampling based on random principles seeming the obvious choice. However, a complete transition to random-based sampling designs in vegetation science is limited by the yet undefined nature of plant communities and by the still diffused opinion that plant communities have a discrete nature. Randomly searching for such entities is almost impossible, given their dependence on scale of observation, plot size and shape, and the need for finding well-defined types. I conclude that the only way to solve this conundrum is to consider and study plant communities as operational units. If the limits of the plant communities are defined operationally, they can be investigated using proper sampling techniques and the collected data analyzed using adequate statistical tools.