Effects of cluster plot design parameters on landscape fragmentation estimates: A case study using data from the Swedish national forest inventory

Forest fragmentation is commonly characterized using indices derived from analyses of classified land cover maps. An alternative is to use data obtained from sampling, such as those from a national forest inventory (NFI). The main objective of the current study is fill knowledge gaps on the performance of sample-based forest fragmentation metrics calculated with different cluster plot designs and under different forest conditions. A set of NFI cluster plot designs, each with different geometric properties, was created from Swedish NFI data. Each member of the set was used to calculate the fragmentation metrics mean patch size (MPS) and perimeter-area ratio (PA). Impacts of plot design parameters on metric estimates and their precision were assessed. Important differences in metric values were observed both within and between regions under different plot design scenarios; within regions, ranges of PA and MPS values were large, and confidence intervals for the minimum and maximum metric values did not overlap. Weighted least squares regression significance testing results suggest that subplot separation distance was an impactful design factor whereas number of subplots and cluster shape were less important. However, cluster plots with more and widely-separated subplots yielded estimates that were more precise (lower relative sampling errors) than smaller, more compact clusters. We suggest that care should be taken when interpreting the physical meaning of the metrics under study.