Environmental drivers of vascular and non‐vascular epiphyte abundance in tropical pre‐montane cloud forests in Northern Peru

Abstract

AbstractQuestionsWhat is the role of microclimate relative to easily obtainable measures of forest structure in explaining epiphyte abundance? Do these roles differ between epiphytic plant groups?LocationTropical pre‐montane cloud forests, Alto Mayo watershed, northern Peru.MethodsWe recorded vascular epiphyte abundance, epiphytic bryophyte cover and forest structural features in 36 plots (20 m × 20 m), and measured air temperature and humidity in a subset of 17 plots. We modelled bryophyte cover, total vascular epiphyte abundance and the abundances of the main vascular epiphyte groups separately (bromeliads, aroids, ferns), as a function of forest structure and microclimate using spatial autoregressive models. Three forest structural variables (basal area, tree height and canopy openness) and two microclimatic variables (minimum humidity and maximum temperature) were considered. We constructed all possible combinations of maximum two‐variable models from the five explanatory variables and carried out AIC‐based model selection and variable importance tests with these as input models.ResultsCanopy openness was the most important variable explaining the abundance of the main epiphytic plant groups. It was also strongly correlated with stand microclimate. Therefore, predictions of epiphyte abundance did not improve with the inclusion of microclimatic data in the models. There were some differences among the epiphytic plant groups in their response to microclimate and forest structural features.ConclusionsForest stand microclimate, reflected through canopy openness in particular, was a main determinant of the distributions of all epiphytic plant groups. This implies that easily measurable forest structural variables alone can be used as good predictors of epiphyte abundance. Taxon‐specific differences in responses to microclimate imply that these taxa may also differ in their sensitivity to predicted future changes in temperature and rainfall.