Bryophytes represent an essential component of global biodiversity and play a significant
role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial
exploitation of natural resources threatens bryophyte species and the ecological processes
and services they support. However, the consideration of bryophytes in conservation
issues is limited by current knowledge gaps on their distribution and diversity patterns.
This is mainly due to the ineffectiveness of traditional field surveys to acquire
information over large areas. Using remote sensing data in combination with species
distribution models (SDMs), we aim to predict and map diversity patterns (in terms
of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts
and sphagna) in 28,436 km(2) of boreal forests of Quebec (Canada). A bryophyte presence/absence
database was used to develop four response variables: total bryophyte richness, moss
richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental
predictors including climate, topography, soil moisture and drainage as well as vegetation.
Then a final set of predictors was selected individually for each response variable
through a two-step selection procedure. The Random Forest (RF) algorithm was used
to develop spatially explicit regression models and to generate predictive cartography
at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics
were provided. Our models explained a significant fraction of the variation in total
bryophyte and guild level richness, both in the calibration (42 to 52%) and validation
sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI)
and climatic variables (temperature, precipitation, and freeze-thaw events) consistently
appeared among the most important predictors for all bryophyte groups modeled. However,
guild-level models identified differences in important factors determining the richness
of each of the guilds and, therefore, in their predicted richness patterns. For example,
the predictor number of days > 30 degrees C was especially relevant for liverworts,
while drainage class, topographic position index and PALSAR HH-polarized L-band were
identified among the most important predictors for sphagna. These differences have
important implications for management and conservation strategies for bryophytes.
This study provides evidence of the potential of remote sensing for assessing and
making predictions on bryophyte diversity across the landscape.