Biodiversity conservation in socio-ecologically complex regions requires integrative frameworks that account for multi-scalar pressures, ecological conditions, and policy responses. This study proposes a novel application of the Pressure-State-Response (PSR) model combined with the Analytic Hierarchy Process (AHP) and geospatial analysis to systematically identify conservation priorities across a Mediterranean biodiversity hotspot. Using 24 environmental, social, and economic criteria, refined through field surveys (2022–2024) and interdisciplinary expert input, we demonstrate that anthropogenic pressures such as land use, transportation infrastructure, and forest fires account for 75% of total stress on biodiversity, while natural drivers like climate and geohazards contribute 25%. Ecosystem-level variables (e.g., climate heterogeneity, topography, and vegetation structure) received higher expert-derived weights than species-level indicators, emphasizing the foundational role of habitat integrity. Education emerged as the most influential response mechanism, surpassing legal and institutional measures, highlighting the need for community-driven strategies. Spatial prioritisation identified nearly 44% of the study region as high to very high priority for conservation action. Our findings offer a replicable, policy-relevant tool for transparent conservation planning and resource allocation, applicable not only to Mediterranean landscapes but also to other global biodiversity hotspots facing similar socio-ecological pressures.

Understanding land use and land cover (LULC) dynamics in seismically active regions is crucial for risk‐informed urban planning and sustainable post‐disaster recovery. This study investigates the impact of the Mw 6.8 Elazığ earthquake (24 January 2020) on LULC patterns in eastern Turkey by integrating high‐resolution Sentinel‐2 satellite imagery with geographic information systems (GIS), remote sensing (RS), artificial neural networks (ANNs), and Markov chain modelling. The methodology comprises four phases: establishing a pre‐earthquake baseline (2015–2019), assessing post‐earthquake changes (2015–2023), analysing transition probabilities to identify key LULC drivers, and forecasting land‐use scenarios for 2030 and 2050 under seismic and non‐seismic conditions. Results reveal that seismic activity significantly accelerates urban expansion, shifting development towards geologically stable zones. By 2050, artificial surfaces are projected to occupy 54.70% of the region under seismic influence, compared to 48.87% without it. Agricultural land is more preserved in the seismic scenario (26.54%) than in the non‐seismic case (22.68%), while pasture and meadow areas decline sharply to 6.18%, raising concerns for biodiversity and ecosystem services. These findings emphasise the importance of integrating ecological considerations and seismic risk into land‐use planning frameworks. By combining multicriteria decision‐making with machine learning‐based forecasting, the study offers a replicable and scalable model for balancing urban growth, environmental conservation, and resilience. Framed within interdisciplinary insights from disaster resilience theory, urban governance, and spatial risk modelling, this research contributes to the global discourse on sustainable urban transformation in the face of increasing natural hazards.