Yazarlar
- Kandemir, Ali(11)
- Yildiz, Faruk(8)
- Kılıç, Engin(7)
- Osma, Etem(6)
- Türkoğlu, Halil İbrahim(6)
- Sari, İdris(5)
- Elverici, Mert(4)
- Gökhan, Askan(4)
- Katipoğlu, Okan Mert(4)
- Varol, Tuğçe(4)
- Akar, Özlem(3)
- Akın, Fatih(3)
- Elveren, Müjgan(3)
- Korkmaz, Mustafa(3)
- Sari, Idris(3)
- Yildirim, Nalan(3)
- Abdo, Hazem Ghassan(2)
- Agar, Guleray(2)
- Agoundé, Gafarou(2)
- Akar, A.(2)
- Akar, Alper(2)
- Akar, Ö.(2)
- Alqadhi, Saeed(2)
- Aslay, Meral(2)
- Davron, Dekhkonov(2)
- Hasbenli, Abdullah(2)
- Hilooğlu, Muhip(2)
- Kartal, Veysi(2)
- Kaya, Ozkan(2)
- Kiymaz, Muhammet Onur(2)
- Komiljon, Tojibaev Sh.(2)
- Mallick, Javed(2)
- Polat, Pinar(2)
- Prasad, Pankaj(2)
- Richi, Sahar Mohammed(2)
- Sözen, Emel(2)
- Taş, Mehmet Akif(2)
- Temur, Asatulloev(2)
- Ullah, Fazal(2)
- Umida, Tojiboeva(2)
Yayınlar
- ArticleAçık Erişim2026LandTrendr Algoritması ve Makine Öğrenme Kullanılarak Orman Tahribatı ve İyileşme Süreçlerinin Sınıflandırılması: Milas-Bodrum Örneği
Taş, Mehmet Akif
Bu çalışma, zengin orman varlığına rağmen Türkiye'nin yangına en hassas bölgelerinden olan Muğla'nın Milas-Bodrum yöresindeki orman dinamiğini, 2013-2024 periyodu için uzaktan algılama ve makine öğrenmesi yöntemleriyle analiz etmektedir. Analizde, 49 adet Landsat 8 görüntüsünden oluşan zaman serisi verileri kullanılmıştır. Normalize Edilmiş Yanma Oranı (NBR) indeksine dayalı bu veriler, LandTrendr algoritması ile işlenmiş ve ardından Random Forest makine öğrenmesi modeliyle her yıl için "Sağlıklı Orman", "Bozulma" ve "İyileşme" olmak üzere, ekosistemin durumunu yansıtan üç dinamik sınıfa ayrılmıştır. Kantitatif bulgular, 2014-2021 arasında "Bozulma" oranının %24'e gerilediği bir iyileşme döneminin, 2021'deki büyük yangınla dramatik şekilde kesintiye uğradığını ortaya koymuştur. Hem sağlıklı hem de iyileşmekte olan ormanları etkileyen bu olay, "Bozulma" oranını tek bir yılda %47'ye fırlatmış; ancak 2024 itibarıyla "İyileşme" kategorisi tekrar %29'a ulaşarak yeni bir rejenerasyon döngüsünün başladığını teyit etmiştir. Bu çalışma, geleneksel sınıflandırmaların ötesine geçerek, makine öğrenmesi ve zaman serisi analizinin büyük afetlerin ekosistem etkilerini belgelemek ve iyileşme süreçlerini izlemek için sunduğu güçlü metodolojik çerçeveyi ortaya koymaktadır.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026İnsansız Hava Aracı ve Görüntü İşleme Destekli Yasaklı Bitki Tespiti
Kose, Oguz
Bu çalışmada insansız hava aracı (İHA) ile tarım arazileri üzerinden alınan görüntüler ile YOLOv11 ve YOLOv26 görüntü işleme algoritmaları kullanılarak tarım arazilerinde ekilmesi yasak olan bitkilerin tespiti gerçekleştirilecektir. Çalışma yazılım kiti ve donanım kiti olmak üzere iki aşamadan oluşmaktadır. Yazılım kitinde görüntü işleme algoritmalarının veri seti, eğitimi, testi ve program ara yüzü geliştirilecektir. Kullanılacak algoritmaların aynı veri seti üzerinden eğitimleri yapılacak, performans metrikleri incelenip en iyi performansı veren algoritma yazılım kitinde kullanılacaktır. Ara yüzün geliştirilmesi ve diğer tüm yazılım bileşenleri Python dilinde yapılmıştır. Donanım kiti ise projenin tüm donanımsal öğelerini içermektedir. Tasarlanan yazılım kiti, donanım kitinde bulunan Raspberry pi içerisinde çalışacaktır. İHA ve şarj istasyonu kendisine ayrılan alan içerisinde muhafaza edilecektir. Önerilen çalışma ile tarım arazilerinde ekilmesi yasak olan bitkilerin tespiti yapılarak erken müdahale edilmesinin avantajı sağlanacaktır. Ayrıca yasak bitkilerinin ekiminin tespiti yapılarak kolluk kuvvetlerinin hem personel hem de zaman ihtiyacını minimuma indirmesi planlanmaktadır.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Projection of Light Pollution and Environmental Impact Estimation Based on Population and GDP: The Case of Türkiye
Elveren, Müjgan; Yilmaz, Abdulvahap
Artificial Light at Night (ALAN) prediction can inform the development of strategies to minimise future light pollution and mitigate its adverse health, ecological, and environmental impacts. Population growth and GDP expansion significantly influence ALAN dynamics. This study quantifies changes in light pollution across Türkiye for 2012, 2017, and 2022, and predicts and maps potential changes in ALAN for 2027 and 2037 using the MOLUSCE plugin (MLP-ANN) in QGIS version 2.18, incorporating population and GDP as driving variables. The predicted and observed ALAN maps for 2022 show a satisfactory level of agreement, with an overall kappa coefficient of 0.63 and a general accuracy of 73.16%. The predicted and observed ALAN maps for 2022 exhibit an overall kappa coefficient of 0.63 and a general accuracy of 73.16%. Based on this validation, potential changes in ALAN according to Bortle classes were projected for 2027 and 2037. From 2022 onwards, ALAN intensity in the light suburban zone (20.1–19.1 mag/arcsec²) is projected to increase by 34,792.87 km², while the suburban–urban transition zone (19.1–18 mag/arcsec²) is expected to expand by 2,979.80 km² by 2037. The predicted spatial patterns provide critical insights into future urbanisation trends and light pollution dynamics. The contraction of rural areas and expansion of suburban zones pose notable risks to environmental sustainability. Gains and losses among Bortle classes indicate that ALAN dynamics can be managed through spatial planning and that ALAN projections can serve as early-warning and scenario-based decision-support tools. Integrating ALAN maps into Environmental Impact Assessment (EIA) processes and spatial planning policies can help preserve dark-sky areas and mitigate the impacts of light pollution.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Design of an Endemic and Native Plant Collection Garden Based on the Integration of Conservation, Propagation, and Exhibition Functions: The Case of Erzincan Horticultural Research Institute
Aslay, Meral; Gökhan, Askan
Global biodiversity loss requires integrated ecological conservation and sustainable management approaches. Endemic taxa with restricted distributions and specialized ecological requirements are particularly vulnerable, necessitating both in situ and ex situ conservation strategies. This study presents the design of a native and endemic plant collection garden proposed for the Erzincan Horticultural Research Institute campus, developed through a conservation–propagation–exhibition integration model within a systematic spatial planning framework. The research followed a multi-stage methodology: (i) establishing a literature-based conceptual framework, (ii) conducting ecological analyses of climate, topography, and soil, (iii) applying multi-criteria species selection, and (iv) implementing computer-aided spatial design. Inventory results identified 48 plant taxa, including 19 endemic to Türkiye, with a dominance of geophytes, particularly species from the genus Fritillaria. The garden was structured through functional zoning, incorporating conservation, propagation, exhibition, education, and controlled sales units, aligned with ecological suitability and institutional sustainability. The findings indicate that such collection gardens serve not only as ex situ conservation sites but also as research infrastructures, genetic resource management platforms, and multifunctional landscapes that contribute to biodiversity conservation and regional development.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Landslide Susceptibility Analysis of Kara Dere Basin (Erzincan) Using Analytical Hierarchy Process Method
Kopar, İbrahim; Yanik, Muhammet Enes
Landslides, as one of the primary natural hazards, are significant mass movements that cause considerable casualties and property damage. The objective of this study is to perform a landslide susceptibility analysis of the Kara Dere Basin, located in the northwest of the Erzincan Plain, and to spatially delineate potential risk areas. In this research, the Analytic Hierarchy Process (AHP), a Multi-Criteria Decision-Making (MCDM) method, and Geographic Information Systems (GIS) technology were utilized. Within the scope of the model, eleven different landslide-conditioning parameters were evaluated: slope, precipitation, lithology, NDVI, slope curvature, distance to streams, land use, elevation, great soil groups, aspect, and distance to roads. As a result of the weighting process applied via the AHP method, slope (25.10%) and precipitation (19.20%) were identified as the most influential factors in landslide occurrence. For the validation of the susceptibility map produced, the historical landslide inventory in the study area was randomly partitioned, utilizing 70% as training data and 30% as independent testing data. The validation procedure, performed using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) technique, yielded an AUC value of 0.799, demonstrating that the model possesses a scientifically acceptable and reliable predictive capacity. According to the spatial analysis results, it was determined that 43% of the basin consists of high and very high-risk areas, 34% of moderate-risk areas, and 23% of low and very low-risk areas. The most susceptible regions were identified as the high-gradient terrains in the north and northeast of the basin. Furthermore, while certain sections of the Erzincan-Kelkit highway were observed to be located in high-risk zones, it was verified that the Ahmetli Village disaster housing, constructed following a historical landslide event, is appropriately situated in a low-risk area. It is anticipated that the resulting susceptibility map will serve as a crucial scientific baseline for future regional and spatial planning, as well as disaster risk management strategies.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Evaluation of Diyarbakır Urban Parks within the Framework of Biodiversity- Friendly Criteria in the Context of Resilient and Sustainable Cities
Yılmaz, Hasan; Çelik, Medine; Gökhan, Askan
Objective: This study evaluates six urban parks in the city center of Diyarbakır within the framework of resilient and sustainable urban development using Biodiversity-Friendly criteria. Species richness, distribution evenness, proportions of native and fruit-bearing species, and ecosystem components were analyzed to determine biodiversity levels and Biodiversity-Friendly performance. Materials and Methods: The study covered 15 Temmuz Şehitler Parkı (July 15 Martyrs Park, KP1), Park 75 (Park 75, KP2), Kardeşlik Parkı (Brotherhood Park, KP3), Kent Meydanı (City Square, KP4), Sümer Parkı (Sumer Park, KP5), and Tema Parkı (Tema Park, KP6). Plant species were identified through field surveys, individual counts were conducted, and naturalness status was determined. Species diversity was calculated using the Shannon–Wiener (H′) and Simpson (1–λ) indices via BİÇEB software. Parks were also evaluated using the Biodiversity-Friendly Index, consisting of 10 main and 65 sub-criteria, and comparatively assessed. Results: A total of 173,343 plant individuals were recorded. The highest diversity values were found in KP6 (H′ = 2.93; 1–λ = 0.939) and the lowest in KP3 (H′ = 1.87; 1–λ = 0.796), while evenness was highest in KP5. Biodiversity-Friendly Index scores classified KP6 (85) and KP4 (83) as high-level, KP1 (77) as good, and KP2 (72), KP3 (69), and KP5 (71) as insufficient in mandatory criteria. Native species exceeded 10% in all parks. Conclusion: Biodiversity-friendly suitability depends not only on species richness but also on species evenness, the proportion of native and fruit- bearing species, permeable surfaces, ecological connectivity, and sustainable management approaches. Considering Diyarbakır’s semi-arid climatic conditions, high summer temperatures, and rapid urbanization pressure, the use of native and drought-tolerant species together with the development of permeable and ecologically connected green infrastructure systems is of critical importance. The findings provide a scientific basis for ecosystem-based and climate-sensitive urban park planning in Diyarbakır.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Climatic Variables as Drivers of Pterocarpus erinaceus (Fabaceae): Distribution and the Implications of Climate Change
Sari, İdris; Ismael, Bio; Ullah, Fazal; Agoundé, Gafarou; Yildiz, Faruk
Pterocarpus erinaceus is an ecologically and economically important tree species native to the Sahel region of West Africa, facing severe threats due to overexploitation, habitat degradation, and climate change. This study assesses the impact of key climatic variables on the species' current distribution and projects potential range shifts across Africa under mid-21st century climate scenarios. Using an ensemble modeling approach that combines various algorithms and utilizes 37 comprehensive climatic variables, the analysis revealed significant patterns of highly suitable habitat, concentrated primarily in West Africa. Projections under low and medium emission scenarios for 2040–2060 and 2080–2100 periods predict a reduction in climatically unsuitable areas and a potential expansion of suitable habitats, suggesting that the species may be somewhat resilient to medium-term climate changes. The primary climatic factors limiting the species' distribution were identified as the Mean Temperature of the Driest Quarter and the Climatic Moisture Index. These results underscore the species' ecophysiological dependence on specific temperature and moisture regimes. Crucially, while our projections suggest the species exhibits resilience and potential habitat expansion under medium-term climate change, its realization is contingent upon mitigating persistent anthropogenic pressures. Therefore, to ensure the species' long-term persistence and maintain the ecological integrity of the West African Savanna Biome, conservation strategies must prioritize aggressive, in situ actions focused on sustainable management, controlled harvesting, and the protection of current and future suitable habitats, rather than focusing solely on long-term climate change adaptation measures.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026First Population Assessment Report, Suitable Habitat and Ecological Corridors of the Endangered Kashmir Gray Langur Morphotype (Semnopithecus ajax, Pocock 1928) in Khyber Pakhtunkhwa, Pakistan
Ali, Abid; Rössel-Ramírez, David Walter; Jan, Arif; Sari, Idris; Martín-Taboada, Adrián; Juan-Rueda, Jorge; Djukanovich-Iturbe, Milenka; Zaman, Iftikhar U-; López-Bao, José Vicente; Khan, Muhammad Noman
The Himalayan Kashmir langur (Semnopithecus ajax) is an endangered colobine of the northwestern Himalayas, occurring in India and Pakistan; yet its ecology remains poorly studied. In Pakistan, it is known from Azad Jammu and Kashmir (AJK) and the northern districts of Khyber Pakhtunkhwa (KPK) but populations restricted to KPK has been neglected until the data. In this regard, we conducted 60 line-transect surveys (118.2 km) across 69.5 km² in three KPK districts—Mansehra, Kohistan, and Battagram—to estimate population density and abundance. Further, we modeled habitat suitability and connectivity modeling of langurs across its known range in Pakistan. The current study revealed mean density across the surveyed districts was 4.5 individuals/km² (95% CI: 2.3–6.7), with substantially higher densities in Mansehra (7.4; 95% CI: 3.3–11.5) compared to Kohistan–Battagram (KB) (1.3; 95% CI: 0.1–2.5). The total population in the study area was estimated at 314 individuals (95% CI: 143–485), with ~86% concentrated in Mansehra. Habitat suitability modeling (AUC = 0.96) identified four major suitable niche zones (SNZ) (~2,953 km²) across Pakistan, primarily montane forests between 1,300–3,100 m with > 40% canopy cover, though only ~82 km² (~1%) overlapped with existing protected areas. Connectivity analysis indicated forested corridors linking these niche zones, while fragmentation reduced connectivity in several regions. Our findings demonstrate that the langur persists in Pakistan as a small, fragmented, and forest-dependent population. Conservation of this threatened species will require improved forest protection, management of habitat corridors, and coordinated transboundary conservation efforts across its Himalayan range.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Zerouali, Bilel; Alqadhi, Saeed; Katipoğlu, Okan Mert; Prasad, Pankaj; Arman, Hasan; Albanai, Jasem A; Mallick, Javed
Landslides significantly threaten natural and built environments, necessitating accurate prediction models for effective hazard mitigation. There is an urgent need to further improve the performance of machine learning algorithms in predicting landslide susceptibility by monitoring the impact of optimization algorithms on the performance of these models. This study evaluates the performance of various machine learning classifiers, including k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), for landslide susceptibility mapping. Additionally, Particle Swarm Optimization (PSO) is employed to enhance model performance by optimizing hyperparameters. Mountainous areas in the eastern Mediterranean (the northern Kabir River basin in western Syria) were identified as a result of the high frequency of landslide events over the past two decades. Nineteen factors causing landslides were identified, with no factor excluded, as a result of a multicollinearity test. The results indicate that XGBoost achieves the highest performance among traditional models. When integrated with PSO, the PSO-XGBoost model further improves classification performance, demonstrating its robustness in handling complex spatial patterns. Feature importance analysis using SHAP confirms slope as the dominant factor, followed by TRI, rainfall, Aspect, TWI, and curvature, highlighting the role of topography and hydrology in landslide occurrence. Moderate lithology, NDVI, and LULC contributions and lower importance of Flow Accumulation and Soil Depth suggest complex environmental interactions. Model predictions show varying susceptibility distributions. PSO-MLP assigns the highest very high susceptibility (44.09%), while PSO-XGBoost provides a balanced classification (31.13%). The PSO-XGBoost model demonstrates superior predictive capability, offering reliable landslide susceptibility maps for disaster risk management and land-use planning.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Prediction of soil moisture via feature selection, model optimization, and climate data integration
Katipoğlu, Okan Mert; Kartal, Veysi; Akiner, Muhammed Ernur; Terzioğlu, Zeynep Özge; Kılıç, Zeyneb
Soil moisture is vital for water resource management and agricultural productivity. With the growing availability of reanalysis data and advanced machine learning methods, it is essential to explore reliable, scalable models for long-term estimation. Comparing different algorithms enables a comprehensive assessment of their strengths and limitations in modeling complex soil moisture dynamics under varying climatic and agricultural conditions. In this study, soil moisture data for the period 1950–2022 from six stations in the Konya Closed Basin of Türkiye were estimated using Echo State Networks (ESN), Adaptive Boosting (AdaBoost), Deep Neural Networks (DNN), and Light Gradient Boosting Machine (LGBM). Precipitation (PR), solar radiation (RSDS), wind speed (WS), mean temperature (Tave), maximum temperature (Tmax), minimum temperature (Tmin), and potential evapotranspiration (ET0) values calculated by the Thornthwaite equation were used as input variables of the models, and soil moisture (SM) was determined as the output variable. Model performance was evaluated using statistical metrics and visualizations derived from training, validation, and test datasets. In addition, a feature selection approach was used to identify the most effective parameters for soil moisture estimation. The analysis showed that the DNN model achieved the highest accuracy, with R2 values ranging from 0.87 to 0.97. The second most successful model was AdaBoost or ESN, while the LGBM model showed higher deviations than the others. These findings reveal that artificial intelligence-based models can effectively estimate soil moisture.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Advancing Forest Fire Susceptibility Mapping in the Eastern Mediterranean Using Multi-Algorithm Machine Learning
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Alqadhi, Saeed; Prasad, Pankaj; Katipoğlu, Okan Mert; Nguyen-Huy, Thong; Das, Jayanta; Almohamad, Hussein; Mallick, Javed
Forest fires are among the most severe natural hazards threatening ecological sustainability in the Eastern Mediterranean, with Syria being a prominent example where ongoing conflict has undermined forest management and monitoring systems. Despite numerous global studies on forest fire susceptibility, conflict-affected areas in the Eastern Mediterranean remain underrepresented in the scientific literature due to limited data and complex environmental and social conditions. In particular, few studies have systematically compared multiple machine learning algorithms for predicting forest fire susceptibility in these regions. To address this gap, this study evaluates the performance of six machine learning algorithms—Extreme Gradient Boost (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), and Linear Regression (LR)—using 3,589 fire events and 13 conditioning factors in the Latakia governorate, western Syria. Results indicate excellent predictive performance for all models, with XGBoost achieving the highest AUC of 0.993, followed by RF (0.992), KNN (0.977), MLP (0.975), SVM (0.968), and LR (0.963). These findings provide a robust basis for supporting fire management and mitigation strategies in Syria and offer insights applicable to other conflict-affected regions of the Eastern Mediterranean.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Ecological and environmental assessment of Erzincan Districts within the scope of the biodiversity-friendly city approach
Askan, Gökhan; Yilmaz, Hasan
This study was conducted between 2022 and 2024 to develop strategies for the inventory, conservation, and sustainable management of biological diversity in urban areas of Erzincan province, encompassing six districts and one town. It also seeks to promote the use of natural plant species to create climate-resilient urban ecosystems. Field surveys, drone imaging, and plant species identification were conducted across 70 open green spaces. The Shannon–Wiener diversity index and Simpson diversity index, together with the Biodiversity-Friendly City Criteria, were applied to the Erzincan case. A total of 105 plant taxa, including 36,799 individuals, were identified, with 56% being natural and 44% exotic. Kemaliye had the highest species diversity index and while Çayirli had the lowest. The plants consisted of 54% edible-leaf broadleaf trees, 19% conifers, and the remaining being edible shrubs, small trees, and vines. Biodiversity assessments revealed that Kemaliye had the highest diversity index, and Çayirli the lowest. Districts were analyzed using the “Bio-friendly Settlement” criteria, with all districts, including Çağlayan, Çayirli, Kemah, Kemaliye, Refahiye, Tercan, and Üzümlü, showing high biodiversity characteristics. The average Bio-friendly score for the districts was 90.7 points. The Shannon–Wiener diversity index indicated that Kemaliye exhibited the highest biodiversity with a high evenness value (E = 0.896), whereas Çayirli showed the lowest diversity; this pattern was consistently supported by the Simpson diversity index results. In conclusion, the study stresses the importance of implementing conservation measures to enhance ecological sustainability in Erzincan, which also holds significant potential for agricultural landscapes and tourism. The high Bio-friendly scores of these districts underscore the need for strategies supporting ecological balance and biodiversity preservation.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Future Climate Change and Bioclimatic Suitability of Two Endemic Balkan Water Frogs
Papežík, Petr; Rusinko, Adam; Sari, İdris; Mikulíček, Peter; Brus, Jan; Benovics, Michal; Iannella, Mattia
Aim: To evaluate the impacts of future climate change on the bioclimatic suitability of two endemic water frogs of the southwestern Balkans, focusing on species- and lineage-level responses and implications for conservation. Location: Southwestern Balkans, including Albania, Greece and Montenegro. Methods: We applied an ensemble bioclimatic suitability modelling framework based on multiple algorithms, using selected bioclimatic predictors to estimate current suitability and to project future distributions under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) for 2050 and 2070. Intraspecific patterns were assessed by integrating mitochondrial lineage data to evaluate stability for both species and lineage levels. Results: Both species are predicted to experience an overall decline in bioclimatic suitability under future climate scenarios, indicating broadly similar responses to climate change. However, the magnitude of these changes differs substantially. Pelophylax shqipericus is predicted to undergo more pronounced range contraction and fragmentation, whereas P. epeiroticus shows relatively higher spatial persistence, with suitable areas remaining primarily in lowland coastal regions. Lineage-level analyses reveal uneven patterns of vulnerability, with greater losses generally affecting phylogenetic lineages occupying more restricted or environmentally sensitive parts of the range. Main Conclusions: Regardless of the differences in their realized bioclimatic niches, both species respond in a similar direction to projected climate change. This suggests that shared climatic constraints outweigh niche differentiation in shaping future distributions. However, differences in the magnitude of response highlight species-specific and lineage-specific sensitivity to environmental change. These findings emphasize the importance of incorporating intraspecific diversity into climate vulnerability assessments and conservation planning in biodiversity hotspots.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Functional Ecology of Floral Traits and Plant Pollinator Interactions in Two Endemic Scrophularia L. Species from Erzincan, Türkiye
Kandemir, Ali; Kılıç, Engin; Yildiz, Faruk; Türkoğlu, Halil İbrahim; Yildirim, Nalan
Plant–pollinator interactions are fundamental to reproductive success, yet remain understudied in the Turkish ecological context, particularly for endemic species. This study investigates pollinator diversity, behavior, and environmental influences in two rare Scrophularia L. species endemic to Erzincan, Türkiye: S. fatmae and S. erzincanica. Field observations conducted between 2019 and 2020 documented floral visitors, visitation frequency, and the role of floral morphology in guiding pollinator behavior. Climatic variables such as temperature, humidity, and wind were also analyzed in relation to pollinator activity. The high-altitude S. fatmae was visited exclusively by Bombus niveatus, which proved effective even under cold and windy conditions, whereas S. erzincanica attracted a more diverse community, including Halictus quadricinctus, Halictus sp., and Lasioglossum pauxillum, the efficiency of pollination was strongly linked to floral architecture; clustered flowers and staminodes enhanced pollinator guidance and cross-pollination. Statistical comparisons further highlighted differences in body size and foraging strategies between pollinator groups. Environmental conditions were shown to significantly limit pollinator activity, with high humidity and low temperatures reducing visitation. Additionally, unexpected warming events accelerated phenology in S. fatmae, indicating potential risks from climate change. Overall, this research provides novel insights into the functional ecology of endemic Scrophularia species, demonstrating how floral traits, pollinator behavior, and environmental variables interact to shape reproductive strategies. These findings contribute to biodiversity conservation efforts and underscore the importance of long-term monitoring under changing climatic conditions.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Politik İstikrarın Çevre Üzerindeki Etkisi: Türk Devletleri Teşkilatı Örneği
Akın, Fatih
Bu çalışmada, Türk Devletleri Teşkilatı üyesi ve gözlemcisi olan ülkelere (Azerbaycan, Kazakistan, Kırgızistan, Özbekistan, Türkiye, Macaristan ve Türkmenistan) yönelik 2002-2021 dönemi için politik istikrar ve çevre arasındaki ilişki panel Otoregresif Dağıtılmış Gecikme (ARDL) yöntemi ile incelenmiştir. Çevreyi temsilen ekolojik ayak izi değişkeni kullanılmıştır. Ekonomik büyüme, yenilebilir enerji tüketimi ve politik istikrar açıklayıcı değişkenler olarak modele dâhil edilmiştir. Çalışmadan elde edilen bulgular, politik istikrarda meydana gelen artışın kısa ve uzun dönemde ekolojik ayak izini azalttığını ortaya koymaktadır. Dolayısıyla bulgulardan hareketle politik istikrarın, çevre kalitesini artmasına katkı sağladığı görülmektedir. Diğer önemli bulgular ise, ekonomik büyüme ekolojik ayak izini artırırken, yenilenebilir enerji tüketimi ise ekolojik ayak izini azaltmaktadır. Bu bulgular doğrultusunda, Türk Devletleri Teşkilatı üyesi ve gözlemcisi olan ülkelerde politik istikrarın devamlılık göstermesi ve yenilenebilir enerji tüketiminin arttırılması çevre kalitesinin artırılmasına yardımcı olabilir.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025The Impact of Agricultural Total Factor Productivity and Economic Growth on Environmental Quality: The Case of Agriculturally Successful Countries
Dinçer, Selin; Akin, Fatih
Agricultural productivity supports the increase in agricultural production by ensuring the efficiency of resources used in the agricultural sector, such as water, soil, labour, and capital. In addition, environmental problems such as land expansion and deforestation can be prevented by using efficient agricultural techniques. However, when the literature is analyzed, it is seen that there is no consensus on the impact of agricultural productivity on environmental quality. On the other hand, it is very important to examine the impact of economic growth on the environment. The economic activities of countries that are in line with their growth and development targets affect environmental quality. In this direction, in this study, in the period 1990-2022, in 12 countries (China, USA, Russia, France, Germany, Canada, Australia, India, Brazil, Indonesia, Argentina, and T & uuml;rkiye) that are successful in agriculture, The impact of agricultural Total Factor Productivity (T-TFP) and economic growth on environmental quality is analyzed by the Augmented Mean Group (AMG) method and Dumitrescu & Hurlin causality analysis. The carbon emission variable is used to represent environmental quality. The findings show that an increase in T-TFP increases environmental quality in China, the USA, Russia, France, Canada, Indonesia, and Australia; that is, the Borlaug hypothesis is valid in these countries. In India, Brazil, Argentina, and T & uuml;rkiye, the increase in T-TFP decreases the environmental quality; that is, Jevons' Paradox is valid in these countries. Moreover, in the study, environmental quality decreases as economic growth increases in all countries except Germany. Renewable energy consumption increases environmental quality in China, the USA, Brazil, Australia, and T & uuml;rkiye. Dumitrescu & Hurlin's causality analysis findings show that there is a bidirectional causality relationship between T-TFP, carbon emissions, and renewable energy consumption. The findings reveal that T-TFP, economic growth, and renewable energy consumption are important in environmental quality.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025BIO-FRIENDLY EDUCATIONAL CAMPUSES – EXAMPLE OF ERZİNCAN CITY IN TURKIYE
Askan, G.; Yilmaz, H.
This study emphasizes outcomes such as the identification of biodiversity in urban areas within Türkiye, the development of strategies to ensure its continuity, the enhancement of resilience against climate change by utilizing natural species in terms of plant material, the effective utilization of resource values, and the creation of awareness at local, national, and international scales. Totally, 10 different educational areas were surveyed, counting 13,323 plants comprising 80 different species, of which 35 are part of the natural flora. In particular, we promote biodiversity in cities by encouraging the planting of more native trees and the use of a greater variety of species that can attract wildlife. Among the identified plants, there are 1,043 edible tree species, 1,871 broadleaf species, 2,393 coniferous species, 828 other ornamental plants, shrubs, and bush types totaling 7,126, and 62 climbing plant species. In these areas, plant species diagnostics, naturalness status, and analyses of edible trees and fruit-bearing plants were conducted. Analyses were also performed according to the Shannon-Wiener diversity index, Simpson diversity index, and Biyodost Educational Campus Index for each area. We recommend considering a design concept that caters not only to humans but also appeals to all living beings for completed and planned educational areas. New concepts such as Bıo-Frıendly Educatıonal Area require collaboration across different disciplines in this field. Academic circles should begin to take the lead in developing and implementing this concept. The collaborative work of these disciplines will contribute to the development of innovative approaches in the design, management, and sustainability of bio-friendly areas.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Beauty demands sacrifices: is it possible to overcome both climate change and anthropogenic impact for Pamir-Alay Tulips (Liliaceae)?
Davron, Dekhkonov; Temur, Asatulloev; Umida, Tojiboeva; Sari, Idris; Shoxida, Abdupattaeva; Shuxrat, Abdullayev; Weibang, Sun; Ziyoviddin, Yusupov; Komiljon, Tojibaev Sh.
Tulipa (Liliaceae) is a well-known ornamental plant that supports a horticultural trade worth over a billion euros. Existing research highlights the expanding array of risks faced by Tulipa species, with climate change emerging as a significant and escalating threat. However, the fate of phytogeographically restricted red-flowering species of tulips in the Pamir-Alay mountain system is crucial, as they are also harvested by local people or visitors purely for their ornamental attractiveness. To study this, 41 bioclimatic and ecologic variables were utilized in the investigation. The suitable areas for T. ingens are expected to increase nearly twofold, while for T. affinis and T. carinata, they will remain constant. However, suitable habitats for T. tubergeniana are projected to decrease considerably. Surprisingly, the western Tian-Shan, where these species are not native, was highlighted as climatically suitable and may be an important area for translocation efforts in the future for T. affinis, T. carinata, and T. ingens. We also identified protected areas that could provide suitable habitats for these species. Additionally, we recommend creating living collections of T. tubergeniana, given the significant decrease in suitable habitat. Ultimately, relocating these species to protected regions with projected suitable habitats plays a crucial role in supporting their survival.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm
Saroughi, Mohsen; Katipoğlu, Okan Mert; Kartal, Veysi; Simsek, Oguz; Kilinc, Huseyin Cagan; Pande, Chaitanya Baliram
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood management. Direct measurement of SC is a challenging and expensive task. For these reasons, it was used to estimate the SC values at a station in the Euphrates River. New hybrid models were established by combining the CatBoost regressor (CBR) and artificial neural network (ANN) models with the honey badger optimization algorithm (HBA) and coati optimization algorithm (COA). The performance of the new model was compared with stand-alone model of ANN and CBR, and their accuracy was evaluated. In the setup of the models, 4 different input combinations of lagged sediment and discharge values for up to 3 months were evaluated. It is noteworthy that as the number of input variables, i.e., lagged data input, increases, the prediction accuracy of the models generally increases. HBA and COA algorithms often improve the accuracy of sediment prediction by optimizing the parameters of the single machine learning model. In addition, according to the AIC performance metric, the HBA algorithm is generally slightly better capable of optimization than the COA. The best model outputs were obtained according to the HBA-CBR hybrid approach of scenario 4 (RMSE = 59.78, AIC = 785.03, R2 = 0.32, PBIAS = 0.016, SI = 0.48, and MBE = − 2.05 in test phase), which consists of discharge and sediment with a delay of up to 3 months. The results of the study are valuable for decision-makers and planners in terms of practical reservoir and flood management and protection of coasts and river beds.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024İmar Barışının Mera ve Yayla Alanlarındaki Yapılaşmaya Etkisinin İncelenmesi
Bayata, Halim Ferit; Akar, Alper; Akar, Özlem
Yayla ve meralar genellikle doğal yollarla oluşan bitki topluluklarıdır. Yapılan tahmini hesaplamalara göre, mera ekosistemleri dünya topraklarının yaklaşık %50' sini kapsamakta ve dünyadaki hayvanların yem ihtiyacının büyük bir kısmını karşılamaktadır. Yapılan çalışma ile, 2018 yılında yürürlüğe giren ve halk arasında imar barışı olarak adlandırılın 7143 sayılı yasanın, mera ve yayla alanlarında oluşabilecek kaçak yapılaşmaya olan etkisi araştırılmıştır. Çalışma alanı olarak Trabzon ili Akçaabat ilçesi sınırları içerisinde yer alan mera ve yaylalar seçilmiştir. Çalışmada Google Earth Pro programından elde edilen uydu görüntüleri ile Triplesat uydu görüntüleri kullanılarak zaman içerisinde meydana gelen kontrolsüz yapılaşma tespit edilmiştir. 1. çalışma bölgesinde 33 ve 2. çalışma bölgesinde 50 olmak üzere toplam 83 yeni yapının bu süre içerisinde yapıldığı belirlenmiştir. Elde edilen sonuçlar halen bu alanlarda kaçak yapılaşmanın devam ettiğini, denetlenme sürecinin hızlı ve etkili bir suretle yapılamadığını ve çıkarılan bu af niteliğindeki yasaların kaçak yapılaşmayı teşvik ettiğini göstermektedir.
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