Modeling and mapping potential distribution of Crimean juniper (Juniperus excelsa Bieb.) using correlative approaches

Ozkan K., Senturk O., Mert A., Negiz M. G.

JOURNAL OF ENVIRONMENTAL BIOLOGY, vol.36, no.1, pp.9-15, 2015 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 1
  • Publication Date: 2015
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.9-15
  • Süleyman Demirel University Affiliated: Yes


Modeling and mapping potential distribution of living organisms has become an important component of conservation planning and ecosystem management in recent years. Various correlative and mechanistic methods can be applied to build predictive distributions of living organisms in terrestrial and marine ecosystems. Correlative methods used to predict species' potential distribution have been described as either group discrimination techniques or profile techniques. We attempted to determine whether group discrimination techniques could perform as well as profile techniques for predicting species potential distributions, using elevation (ELVN), parent material (ROCK), slope (SLOP), radiation index (RI) and topographic position index (TPI)) as explanatory variables. We compared potential distribution predictions made for Crimean juniper (Juniperus excelsa Bieb.) in the Yukari gokdere forest district of the Mediterranean region, Turkey, applying four group discrimination techniques (discriminate analysis (DA), logistic regression analysis (LR), generalized addictive model (GAM) and classification tree technique (CT)) and two profile techniques (a maximum entropy approach to species distribution modeling (MAXENT), the genetic algorithm for rule-set prediction (GARP)). Visual assessments of the potential distribution probability of the applied models for Crimean juniper were performed by using geographical information systems (GIS). Receiver-operating characteristic (ROC) curves were used to objectively assess model performance. The results suggested that group discrimination techniques are better than profile techniques and, among the group discrimination techniques, GAM indicated the best performance.