Quantifying distance of edge influence: a comparison of methods and a new randomization method

Abstract

Despite many studies on edge influence in forests, there is no common method for estimating distance of edge influence (DEI, = edge width). We introduce a new randomization method (RTEI) for estimating DEI that tests the significance of edge influence compared to the reference forest. Using artificial datasets we compared DEI as estimated by nine different methods and examined effects of sampling design and the nature of the edge response. DEI estimates varied widely among methods; parametric, randomization and curve-fitting analyses produced the lowest, intermediate and greatest values, respectively. Sampling design and the nature of the edge response affected estimates of DEI differently among methods. RTEI was the only method that was generally invariable to sampling design while being sensitive to variation in the reference ecosystem but not at the edge. A standard method of quantifying DEI is important for comparing edge responses among different studies for conservation research.