Predicting Patterns of Plant Diversity and Endemism in the Tropics Using Remote Sensing Data: A Study Case from the Brazilian Atlantic Forest
作者： Andrea Paz , Marcelo Reginato , Fabián A. Michelangeli , Renato Goldenberg , Mayara K. Caddah
DOI: 10.1007/978-3-030-33157-3_11
关键词:
摘要: We combine remote sensing (RS) measurements of temperature and precipitation with phylogenetic distribution data from three plant clades different life forms, i.e., shrubs treelets (tribe Miconieae, Melastomes), epiphytes (Ronnbergia-Wittmackia alliance, Bromeliaceae), lianas (“Fridericia Allies” clade, Bignoniaceae), to predict the biodiversity in a tropical hot spot: Brazilian Atlantic Forest. assess (i) how well RS-derived climate estimates spatial species richness (SR), diversity (PD), endemism (PE) (ii) they compare predictions based on interpolated weather station information. find that environmental descriptors derived RS sources can SR PD, performing as or better than station-based data. Yet performance is lower for high number small ranges. argue this approach provide an alternative remotely monitor megadiverse groups biomes which identification through are not yet feasible available.
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