Modelling Urban Plant Diversity Along Environmental, Edaphic, and Climatic Gradients

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dc.contributor.authorDogan, Tuba Gul
dc.contributor.authorEroglu, Engin
dc.contributor.authorKucuksille, Ecir Ugur
dc.contributor.authorDogan, Mustafa Isa
dc.contributor.authorGedik, Tarik
dc.date.accessioned2026-07-01T11:39:21Z
dc.date.available2026-07-01T11:39:21Z
dc.date.issued2025
dc.departmentDüzce Üniversitesi
dc.description.abstractUrbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Duzce, Turkiye. A multidimensional suite of environmental predictors-including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)-was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (similar to 0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R-2 approximate to 0.937474; Richness R-2 approximate to 0.855305; Evenness R-2 approximate to 0.631796).
dc.description.sponsorshipTBIdot;TAK 1002-A Project [124O656] -- This study is supported by TUB & Idot;TAK 1002-A Project No: 124O656.
dc.identifier.doi10.3390/d17100706
dc.identifier.issn1424-2818
dc.identifier.issue10
dc.identifier.orcid0000-0001-7372-8295
dc.identifier.orcid0000-0002-1777-8375
dc.identifier.orcid0000-0002-3293-9878
dc.identifier.scopus2-s2.0-105020313424
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/d17100706
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23250
dc.identifier.volume17
dc.identifier.wosWOS:001604426600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiversity-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subjectUrban-Rural Gradient
dc.subjectShannon Diversity
dc.subjectSpecies Richness
dc.subjectMachine Learning Algorithms
dc.titleModelling Urban Plant Diversity Along Environmental, Edaphic, and Climatic Gradients
dc.typeArticle

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