Akademik Veri Yönetim Sistemi
ECOLOGY AND EVOLUTION, cilt.7, sa.4, ss.1112-1124, 2017 (SCI-Expanded)
Species distribution modeling was used to determine factors among the large predictor candidate data set that affect the distribution of Muscari latifolium, an endemic bulbous plant species of Turkey, to quantify the relative importance of each factor and make a potential spatial distribution map of M.latifolium. Models were built using the Boosted Regression Trees method based on 35 presence and 70 absence records obtained through field sampling in the Gonen Dam watershed area of the Kazda Mountains in West Anatolia. Large candidate variables of monthly and seasonal climate, fine-scale land surface, and geologic and biotic variables were simplified using a BRT simplifying procedure. Analyses performed on these resources, direct and indirect variables showed that there were 14 main factors that influence the species ' distribution. Five of the 14 most important variables influencing the distribution of the species are bedrock type, Quercus cerris density, precipitation during the wettest month, Pinus nigra density, and northness. These variables account for approximately 60% of the relative importance for determining the distribution of the species. Prediction performance was assessed by 10 random subsample data sets and gave a maximum the area under a receiver operating characteristic curve (AUC) value of 0.93 and an average AUC value of 0.8. This study provides a significant contribution to the knowledge of the habitat requirements and ecological characteristics of this species. The distribution of this species is explained by a combination of biotic and abiotic factors. Hence, using biotic interaction and fine-scale land surface variables in species distribution models improved the accuracy and precision of the model. The knowledge of the relationships between distribution patterns and environmental factors and biotic interaction of M.latifolium can help develop a management and conservation strategy for this species.