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E. Seda Arslan

Researcher at Süleyman Demirel University

Publications -  17
Citations -  150

E. Seda Arslan is an academic researcher from Süleyman Demirel University. The author has contributed to research in topics: Climate change & Environmental science. The author has an hindex of 4, co-authored 12 publications receiving 53 citations.

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MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier

TL;DR: In this article, the current and potential future distribution of Quercus libani Olivier (Lebanon Oak), a tree species in Turkey, and to predict the changes in its geographical distribution under different climate change scenarios was modeled under the Representative Concentration Pathways (RCP) RCP 4.5 and RCP 8.4.
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Distribution of rose hip (Rosa canina L.) under current and future climate conditions

TL;DR: In this paper, the potential distribution areas of the species Rosa canina L. (rose hip) and the possible future changes in its distribution under given climate change scenarios were identified using MaxEnt.
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MaxEnt modelling of the potential distribution areas of cultural ecosystem services using social media data and GIS

TL;DR: In this article, the authors used photographs on social media to spatially model the potential distribution of user preferences for cultural ecosystem services (CES) in the province of Isparta in Turkey's Mediterranean region.
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Habitat suitability mapping of stone pine (Pinus pinea L.) under the effects of climate change

TL;DR: In this article, the authors examined the current and future potential geographical distribution of the stone pine (Pinus pinea L.), a species of considerable ecological and economic importance, in the light of aridity predictions in the Mediterranean region.
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Effects of climate change on the ecological niche of common hornbeam (Carpinus betulus L.)

TL;DR: In this article, the current and future geographical distribution of common hornbeam under different climate scenarios was modeled by applying machine learning techniques and the differences between the predicted current and potential distribution areas of the species in terms of area and location by means of change analysis.