Modeling and Mapping Forest Floor Distributions of Common Bryophytes Using a LiDAR-Derived Depth-to-Water Index

Abstract

This article describes how the cartographic depth-to-water (DTW) index in combination with other variables can be used to quantify, model and map the distribution of common forest floor bryophytes, at 1 m resolution. This was done by way of a case study, using 12 terrain and climate representative locations across New Brunswick, Canada. The presence/absence by moss species was determined at each location along upland-to-wetland transects within >10-m spaced 1-m 2 forest floor plots. It was found that Bazzania trilobata, Dicranum polysetum, Polytrichum commune, Hylocomium splendens, and P leurozium schreberi had greater probabilities of occurrence in well-drained forested areas, whereas Sphagnum fuscum and Sphagnum girgensohnii dominated in low-lying wet areas. The presence/absence of each species was quantified by way of logistic regression analyses, using DTW, slope, canopy closure, forest litter depth, ecosite type (8 classes), nutrient regime (4 classes, poor to rich); vegetation type (deciduous, coniferous, mixed, and shrubs), and macro- and micro-topography (upland, wetland; mounds, pits) as predictor variables. Among these, log 10 DTW and forest litter depth were the most consistent predictor variables, followed by mound versus pit. For the mapping pu rpose, only log 10 DTW and already mapped classifications for upland versus wetland and vegetation type were used to predict the probability of occurrences for the most frequent moss species, namely, D. polysetum, P. schreberi and Sphagnum spp. The overall accuracy for doing this ranged from 67 % to 83%, with false positives and negatives amounting to 18 % to 42%. The overall classification accuracy exceeded the probability by chance alone at 76.8%, with the significance level reached at 75.3%. The average level of probability by chance alone was 60.3%.