A new approach to diversity indices - modeling and mapping plant biodiversity of Nallihan (A3-Ankara/Turkey) forest ecosystem in frame of geographic information systems

Dogan H., Dogan M.

BIODIVERSITY AND CONSERVATION, vol.15, no.3, pp.855-878, 2006 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 3
  • Publication Date: 2006
  • Doi Number: 10.1007/s10531-004-2937-4
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.855-878
  • Keywords: flora, geographic information systems, mapping, modeling, plant biodiversity, plant ecology, remote sensing, spatial analysis, ELEVATIONAL GRADIENT, SPECIES-DIVERSITY, RICHNESS, RULE


Modeling and mapping possibilities of Shannon-Wiener, Simpson, and number of species (NS) indices were researched using geographic information systems (GIS) and remote sensing (RS) tools in Nallihan forest ecosystem of Turkey. The relationships between the indices and a number of independent variables such as topography, geology, soil, climate, normalized difference vegetation index (NDVI), and land cover were investigated to understand relationships between plant diversity and ecosystem. Georeferenced field data from the established 56 quadrats (50 x 20 m) were used to calculate the indices. Principle component analysis (PCA) and multiple regression were employed for data reduction and model development, respectively. Three diversity maps were produced using the developed models. Residual maps and logical interpretations in ecological point of view were used to test the validity of the models. Elevation and climatic factors formed the most important components that are effective determinants of plant species diversity, but geological formations, soil, land cover and land-use characteristics also influenced plant diversity. Considering the different responses of the models, Shannon-Wiener (SWI) and NS models were found suitable for rare cover types, while Simpson (SIMP) model might be appropriate for single dominant land covers in the study area.