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<p>Understanding and managing the impacts of climate change on ecologically and economically important plant species requires integrated modelling approaches. In this study, we developed an environmental modelling and decision-support framework for assessing the current and future habitat suitability of <i>Nepeta persica</i> Boiss. in Fars Province, Iran. The framework combines bivariate models (FR, WofE, IofE) and machine learning algorithms (GLM, GAM, ANN, MaxEnt, XGBoost, ENET) with fuzzy Multi-Criteria Decision Analysis (AHP, TOPSIS, VIKOR), enabling both quantitative habitat forecasting and structured decision support. Results indicated that temperature and elevation are the dominant drivers shaping species distribution. Projections under SSP245 and SSP585 scenarios suggest up to 30% contraction of suitable habitats by 2100, accompanied by an eastward and upslope shift. These outcomes provide critical insights for sustainable management, highlighting climatically buffered highlands as potential refugia for conservation and climate-resilient cultivation. By linking model-based ecological forecasting with participatory decision analysis, this research contributes to the development of adaptive management strategies aligned with the Sustainable Development Goals (SDGs 2, 13, and 15), supporting both biodiversity conservation and rural livelihood resilience under global change.</p>

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An integrated environmental modelling and decision-support framework for climate-resilient management of Nepeta persica boiss. under climate change

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Emran Dastres,
  • Ali Sonboli,
  • Ghazal Shafiee Sarvestani,
  • Hamidreza Rabiei-Dastjerdi,
  • Hassan Esmaeili,
  • Mahdis Amiri,
  • Mohammad Hossein Mirjalili

摘要

Understanding and managing the impacts of climate change on ecologically and economically important plant species requires integrated modelling approaches. In this study, we developed an environmental modelling and decision-support framework for assessing the current and future habitat suitability of Nepeta persica Boiss. in Fars Province, Iran. The framework combines bivariate models (FR, WofE, IofE) and machine learning algorithms (GLM, GAM, ANN, MaxEnt, XGBoost, ENET) with fuzzy Multi-Criteria Decision Analysis (AHP, TOPSIS, VIKOR), enabling both quantitative habitat forecasting and structured decision support. Results indicated that temperature and elevation are the dominant drivers shaping species distribution. Projections under SSP245 and SSP585 scenarios suggest up to 30% contraction of suitable habitats by 2100, accompanied by an eastward and upslope shift. These outcomes provide critical insights for sustainable management, highlighting climatically buffered highlands as potential refugia for conservation and climate-resilient cultivation. By linking model-based ecological forecasting with participatory decision analysis, this research contributes to the development of adaptive management strategies aligned with the Sustainable Development Goals (SDGs 2, 13, and 15), supporting both biodiversity conservation and rural livelihood resilience under global change.