TITLE:
Modeling and Mapping Forest Floor Distributions of Common Bryophytes Using a LiDAR-Derived Depth-to-Water Index
AUTHORS:
Monique Goguen, Paul A. Arp
KEYWORDS:
Bryophytes, Wet Areas, Macro- and Micro-Topography, Forest Floor, Forest Litter, Mound And Pit, Canopy Closure, Digital Elevation Modeling, Logistic Regression
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.8 No.4,
March
31,
2017
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-m2 forest floor plots. It was found that Bazzania
trilobata, Dicranum polysetum, Polytrichum commune, Hylocomium splendens, and Pleurozium 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, log10DTW and forest litter depth were the most
consistent predictor variables, followed by mound versus pit. For the mapping
purpose,
only log10DTW 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%.