Estimating lichen α- and β-diversity using satellite data at different spatial resolutions

Understanding biodiversity patterns and its environmental drivers is crucial to meet conservation targets and develop effective monitoring tools. Inconspicuous species such as lichens require special attention since they are ecologically important but sensitive species that are often overlooked in conservation planning. Remote sensing (RS) can be particularly beneficial for these species as in combination with modelling techniques it allows planners to assess and better understand biodiversity patterns. This study aims to model the lichen alpha-diversity (species richness) and beta-diversity (species turnover) biodiversity components using high resolution RS variables across a subarctic region in Northern Quebec (similar to 190.25 km(2)). Two sensors, one commercial (WorldView-3, WV3) and another freely accessible (Sentinel-2, S2), at different resolutions (1.2 m and 10 m, respectively) were tested separately to develop our variables and feed the models. Lichens were sampled in 45 plots across different habitat types, ranging from forested habitats (coniferous, deciduous) to wetlands (bogs, fens) and rocky outcrops. Two sets of uncorrelated variables (Red and NIR; EVI2) from each sensor were parallelly used to build the alpha- and beta-diversity models (8 models in total) through Poisson regressions and generalized dissimilarity modelling (GDM), respectively. Red and NIR variables were useful for modeling the two biodiversity components at both resolutions, providing information on stand canopy closure and structure, respectively. EVI2, especially from WV3, was only informative for assessing beta-diversity, providing similar information than Red. Poisson models explained up to 32 % of the variation in lichen alpha-diversity, with Red, NIR and EVI2, either from WV3 or S2, showing negative relationships with lichen richness. GDMs described well the relationship between beta-diversity and spectral dissimilarity (R-2 from 0.25 to 0.30), except for the S2 EVI2 model (R-2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation. This study contributes to improve our knowledge and to inform on the use of RS to understand biodiversity patterns of inconspicuous species, which we consider to be an essential step to enhance their rep-resentation in conservation planning.