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Research article - Peer-reviewed, 2017

Informative plot sizes in presence-absence sampling of forest floor vegetation

Stahl, Goeran; Ekstrom, Magnus; Dahlgren, Jonas; Esseen, Per-Anders; Grafstrom, Anton; Jonsson, Bengt-Gunnar


Plant communities are attracting increased interest in connection with forest and landscape inventories due to society's interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here, we investigate the use of presence-absence sampling with the aim to monitor state and change in plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible.Plant occurrences were modelled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new.We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square metre the optimum plot size is 1.6 square metres. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory.Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rare plants using presence-absence sampling in monitoring programmes.


density; intensity; optimum plot size; plant monitoring; point pattern; Poisson model; sample plots; vegetation change; vegetation survey

Published in

Methods in Ecology and Evolution
2017, Volume: 8, number: 10, pages: 1284-1291