The role of topographic-derived hydrological variables in explaining plant species distributions in Amazonia

Author(s)

Gabriel M. MOULATLET ; Camilo D. RENNÓ ^[2] ; Fernando O. G. FIGUEIREDO ^[3] ; Kalle RUOKOLAINEN ; Lise BANON ^[5] ; Thaise EMILIO ^[6] ; Henrik BALSLEV ^[7] ; Hanna TUOMISTO ^[8]

• Document type: Journal article
• Source: Acta Amazonica; v. 52, n. 3, p. 218-228, 2022-09-12.

Abstract

ABSTRACT In Amazonian terra-firme non inundated forests, local floristic composition and species occurrence are explained by water availability as determined by topographic conditions. Topographic complexity can render these conditions quite variable across the landscape and the effects on plant ecological responses are difficult to document. We used a set of topographically defined hydrological metrics to evaluate community composition and single-species responses of four plant groups [pteridophytes (ferns and lycophytes), Melastomataceae, palms (Arecaceae) and Zingiberales] to topographic conditions in the middle Juruá River region, in western Brazilian Amazonia. The area spans two geological formations (Içá and Solimões) with contrasting topography. River terraces are also found along the main rivers in the area. Local topographic conditions were approximated by height above the nearest drainage (HAND), slope, and Strahler´s drainage order, all obtained from a SRTM digital elevation model (DEM). Data were analyzed using linear and generalized linear mixed models and regression trees. HAND was most successful in explaining floristic composition for all plant groups, except for Melastomataceae, and was more important in the hilly Içá formation than in the Solimões. Individual occurrences of 57% species were predicted by at least one of the topographic variables, suggesting a marked habitat specialization along topographic gradients. For these species, response models using SRTM-DEM-derived variables gave similar results than models using field-measured topography only. Our results suggest that topographical variables estimated from remote sensing can be used to predict local variation in the structure of plant communities in tropical forests. (AU)