Facing climate change complexity: microclimate and distribution range dynamics revealed by long-term monitoring of the kaiser mountain newt (Neurergus kaiseri) in Iran

• Integrating long-term seasonal data clarifies climate change impacts • The strongest signs of climate change are observed in autumn • Rising summer/autumn LST spans 4,315 km² and 2,762 km² of suitable habitat • Vegetation health anomalies cover more suitable habitat than temperature anomalies • Topographic parameters govern microclimates in population cores across seasons Environmental pressures demand effective habitat conservation through systematic, long-term monitoring of key ecological indicators. In this study, seasonal averages of Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) were derived from MODIS data (2003–2024) and categorized into winter, spring, summer, and autumn. LST data were downscaled using Geographically Weighted Regression (GWR) with NDVI, while the Vegetation Health Index (VHI)—a drought indicator—was analyzed. Long-term seasonal trends and seasonal anomalies were identified using the Mann-Kendall test (95% significance) and median absolute deviation, with the Elbow method quantifying anomaly extents. Seasonal cycles were further evaluated by comparing standardized Z-score values, and long-term microclimate patterns were characterized through Principal Component Analysis (PCA) of time-series VHI, LST, and topographic variables. Additionally, Random Forest Regression (RFR) assessed the influence of environmental factors on microclimate fluctuations. Findings reveal that increasing summer and autumn LST trends affected 4,314.78 and 2,761.12 km² of suitable habitat, with 58 and 33 population cores exhibiting rising trends, respectively. Fall is warming up, and southern demographics are experiencing rising trends in LST and VHI. Notably, LST and VHI anomalies covered smaller habitat areas than overall LST trends, with the sharpest long-term seasonal shifts observed from winter to autumn. RFR results indicate that these microclimates are primarily driven by topography-related factors, including the Compound Topographic Index and openness measures. Our integrated analysis of vegetation and temperature trends offers a robust framework for conservation planning by pinpointing habitats most vulnerable to climate-induced changes.