Modeling forest and rangeland ecosystem responses to drought across Hyrcanian bioclimatic zones of Iran using GLM and LAI analysis
Abstract Drought substantially affects ecosystem structure and function, shaping vegetation dynamics and influencing long-term environmental sustainability. This study examines drought effects on forest and rangeland ecosystems across three bioclimatic zones in the Hyrcanian region of Iran. MODIS-de...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09629-0 |
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| Summary: | Abstract Drought substantially affects ecosystem structure and function, shaping vegetation dynamics and influencing long-term environmental sustainability. This study examines drought effects on forest and rangeland ecosystems across three bioclimatic zones in the Hyrcanian region of Iran. MODIS-derived Leaf Area Index (LAI) data (2001–2022) and Standardized Precipitation Index (SPI) were used with a generalized linear model (GLM) to assess vegetation responses. The findings indicate that rangeland ecosystems, especially in Zones II and III, are susceptible to drought, with SPI accounting for over 80% of the observed LAI variability in these regions. Forests better withstand dry conditions, with SPI explaining about half of the changes in LAI Zone III, with high elevation and snow-dominated precipitation, is drought-sensitive. Zone I near the Caspian Sea has higher humidity and more stable conditions. Zone II, with a semi-humid cold climate, exhibits the largest LAI fluctuations due to its strong dependence on moisture. Elevation, vegetation type, and climate critically influence drought responses. Targeted land management, including water optimization and conservation, is essential. Future research should integrate additional factors such as soil moisture, land cover change, and anthropogenic pressures such as deforestation, overgrazing, and environmental degradation alongside predictive modeling to enhance ecological sustainability. |
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| ISSN: | 2045-2322 |