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Informative plot sizes in presence‐absence sampling of forest floor vegetation
Author(s) -
Ståhl Göran,
Ekström Magnus,
Dahlgren Jonas,
Esseen PerAnders,
Grafström Anton,
Jonsson BengtGunnar
Publication year - 2017
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12749
Subject(s) - plot (graphics) , estimator , sampling (signal processing) , statistics , poisson distribution , variance (accounting) , forest plot , vegetation (pathology) , range (aeronautics) , mathematics , sample size determination , sampling design , econometrics , ecology , environmental science , computer science , medicine , population , meta analysis , materials science , accounting , demography , filter (signal processing) , pathology , sociology , business , composite material , computer vision , biology
Summary 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.