A Machine Learning Algorithm‐Based Approach (MaxEnt) for Predicting Habitat Suitability of Formica rufa
Yazarlar (3)
Prof. Dr. Gonca Ece ÖZCAN Kastamonu Üniversitesi, Türkiye
Eda Ünel
Kastamonu Üniversitesi, Türkiye
Kastamonu Üniversitesi, Türkiye
Prof. Dr. Fatih SİVRİKAYA Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Journal of Applied Entomology (Q1) | ||
| Dergi ISSN | 0931-2048 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2025 |
| Cilt / Sayı / Sayfa | 149 / 4 / 558–572 | DOI | 10.1111/jen.13403 |
| Makale Linki | https://doi.org/10.1111/jen.13403 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
|
||
| Özet |
| Machine learning techniques are quite effective for simulating species habitat appropriateness. Species distribution models are statistical algorithms founded on the ecological niche idea. These models estimate the association between existing species records and the environmental and spatial characteristics of the habitat. From 2022 to 2023, a field survey was conducted in the Kastamonu Forest Enterprise, resulting in the identification of 267 active Formica rufa nests. The habitat preferences of F.rufa were assessed based on factors such as stand characteristics, topography and climatic variables. MaxEnt, a prevalent machine learning technique for predicting species habitat suitability, was employed in the habitat suitability modelling of F. rufa. 30 distinct variables were employed in the modelling process. Receiver Operating Characteristic (ROC) analysis examined model accuracy. AUC was 0.941 for training … |
| Anahtar Kelimeler |
| bioclimatic variables | Forest stand | Jackknife | nest | ROC | topography |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 2 |
| Google Scholar | 5 |