Factors biasing the correlation structure of patch level landscape metrics

• We assessed the correlation structure of 13 patch level landscape metrics with PCA.
• We applied several combinations of landscape types, resolutions and variable sets to reveal the influencing factors on correlations.
• Outcomes indicate the relevance of variable sets and smaller importance on cell size and landscape types (including patch size and configuration).
• Control measurements showed the reliability of the results and revealed the high variability of core area metrics.

Landscape metrics are in varying correlations with each other. Several authors have revealed their correlation structure and determined sets of metrics which can be used in landscape analysis. We assumed that correlation structure is not stable and is biased by several factors, thus, selection based on the correlation can vary by case studies. In this study we dealt with 13 patch level landscape metrics using three landscape types, consisting of 9 subregions with 7 and 14 land cover classes, applying 5 different cell sizes. In each step of the analysis other factors that can bias the results were controlled, or the analyses were carried out separately. In accordance with our aims, we uncovered the factor structure of the metrics in different situations, with the parameters which might possibly bias the results. Results showed that cell size, landscape types and number of land cover classes had a greater or lesser effect on cross-correlations. However, the greatest effect was experienced when variables were changed slightly (i.e. two metrics were replaced with two new ones). A comparison of factor structure was conducted with the coefficient of congruence, rank order based on factor loadings, and biplots. According to our findings, congruence values are not reliable in all cases, while ranks and biplots were not sensitive to the changes in circumstances. Possible outcomes were tested with calculations of 3 test areas (a large landscape from NE-Hungary and two countries). Results can be relevant for landscape ecologists dealing with many variables and multivariate techniques.