Advantage and Disadvantage of Global and Local Climate Datasets on Modeling Species Distribution at Continental and Landscape Scales

Species distribution model based on global and local climate datasets were hypothesized to have advantages on projecting distribution range at continental and landscape scales, respectively. Random Forest (RF) and principle components analysis (PCA) aimed to project potential distribution range and to construct climate space of Bretschneidera sinensis in continental East Asia (CEA) and northern Taiwan (NTWN) based on the WorldClim and local climate datasets. Geographical extent of the endangered species at continental scale was available to be projected by RF based on the WorldClim dataset, whereas isolation and fragmentation of natural habitat had not been presented by the projection map in CEA. At landscape scale, projection map of RF in NTWN based on the WorldClim dataset presented gridded distribution far from empirical distribution pattern, while that based on local climate dataset presented a distribution pattern relevant to elevation and topography. PCA had revealed climate differentiation between continental and island populations. Evidently, local climate dataset is essential for identifying ecological adaptation of island population at geographical margin of the endangered species. Meteorological data interpolated and altitudinal adjusted by empirical elevation lapse rate calculated for each watershed had captured climate heterogeneity in mountainous area, whereas it generated huge number of gridded cells that is not available to expand this method to continental region. Global climate dataset has the advantage on modeling geographical extent of plant species at continental scale, while local climate dataset used for modelling species distribution enables conservationists to delineate reliable conservation areas in fragmented natural habitats at landscape scale.