Scaling litter fall in complex terrain: A study from the western Cascades Range, Oregon

Non-normally distributed variation in vegetation and topography can make estimates of litter fall based on simple mathematical scaling of randomly placed litter traps inaccurate. It has been shown that litter fall is directly related to aboveground net primary productivity (ANPP), which can be calculated from remote metrics (such as LiDAR returns) and/or measured at a high resolution (based on inventory plots). Extrapolating litter fall to a landscape scale with ANPP as a cross-correlate may increase the reliability of estimates. However, the differences in landscape-scale litter flux estimates due to scaling method have not been quantified. We collected litter from 16 plots on a small (96 ha) watershed in the western Cascades Range of Oregon over 2 years. We related litter fall to ANPP (R2 = 0.65), which was calculated from 133 long term re-measurement plots on a forest currently dominated (70-80%) by Pseudotsuga menziesii and Tsuga heterophylla of approximately 50 years of age and used four methods of scaling, (1) multiplying mean measured litter fall per hectare by watershed area, (2) randomly selecting and summing variates from the probability distribution, (3) kriging field-measured litter and (4) supplementing our field measurements of litter with modeled litter based on the ANPP to litter relationship and then kriging. We found that (1) longer term averages of litter are more robust when compared to measurements and modeled estimates, and (2) the appearance of “hot spots” of elevated litter fall corresponding to undersampled areas of the complex terrain disappeared when the field measurements were supplemented with ANPP. In complex terrain, longer-term experiments supplemented with high-resolution productivity data may increase the reliability and interpretability of litter fall estimates.