Abstract
Research that has been conducted documenting species richness patterns on tropical mountains has resulted in conflicting observations: monotonic declines with increasing elevation, monotonic increases with increasing elevation, and a mid-elevation "bulge." Currently, it is unclear if these differences are due to environmental differences among study areas, the taxonomic groups or ecological groups (e.g., growth form) sampled, or the scale of study along elevation gradients. Because of the difficulty in sampling and identifying canopy-dwelling plants, the number of inventories quantifying tropical epiphytes is relatively limited and recent. In this study, we provide a detailed qualitative and quantitative assessment of the vascular epiphyte flora and its spatial distribution on Volcán Maderas, Isla de Ometepe, Nicaragua, including weather and environmental measurements along the entire elevation gradient of the volcano. We sampled epiphytes in five distinct forest types associated with increasing elevation as follows: dry forest, humid forest, wet forest, cloud forest, and elfin forest. Five weather stations were placed along the elevation gradient for us to relate observed patterns to environmental conditions. A mid-elevation peak in species richness was detected for all vascular epiphytes at approximately 1000 m in elevation (cloud forest), yet epiphyte abundance increased with increasing elevation. In total we identified 206 taxa of vascular epiphytes belonging to 26 families and 73 genera. The most species-rich family was the Orchidaceae with 55 species for the entire elevation gradient, followed by Bromeliaceae (29 species), Araceae (23), Polypodiaceae (25), Dryopteridaceae (16), and Piperaceae (11), with all other families represented by fewer than 10 species each. We found that richness patterns differ phylogenetically across epiphyte groups, possibly due to different adaptive strategies, and species for the most part appear to be narrowly distributed within specific habitat zones along the elevation gradient. Variables associated with moisture, precipitation, humidity, mist, or cloud cover are key to understanding the observed patterns.