Ecology of Iranian Forest

Introduction and Objectives

Deforestation poses a serious threat to ecosystems by reducing biodiversity, disrupting hydrological cycles, and exacerbating climate change. In addition to undermining natural resources, it adversely impacts local economies and ecotourism. The Hyrcanian forests, among the most valuable temperate biomes, harbor numerous endemic species. However, anthropogenic pressures such as urban expansion, unsustainable agriculture, and unregulated exploitation have led to severe degradation of these forests. The Peymot Forest region in Noor County, Mazandaran Province, is no exception and is currently facing a significant decline in forest cover.

Material and Methods

To identify key drivers and spatial patterns of deforestation in the study area, 107 deforested plots were recorded. Deforested sites were coded as “1” and non-deforested sites as “0.” In this research, deforestation is defined as the permanent or long-term conversion of forested areas into non-forested land due to anthropogenic activities or natural disturbances, where forest recovery within a reasonable timeframe is improbable. Fourteen environmental and anthropogenic predictor variables—including slope aspect, slope gradient, elevation, landform shape, profile curvature, slope length, wind exposure, minimum temperature, mean temperature, maximum temperature, precipitation, distance to roads, distance to settlements, and proximity to agricultural land—were extracted from multiple sources and integrated into two statistical modeling frameworks: Generalized Linear Model (GLM) and Generalized Additive Model (GAM). GLM, an extension of classical linear regression adapted for binary outcomes, and GAM, an advanced nonparametric extension of GLM that effectively captures nonlinear relationships, were employed for spatial modeling. The GLM framework began with variable selection based on ecological knowledge and multicollinearity analysis, followed by stepwise model refinement using likelihood ratio tests to eliminate non-significant predictors. In the GAM approach, appropriate smoothing functions were selected, and optimal degrees of freedom were determined for each smoother. Model performance was enhanced through the estimation of smoothing parameters and significance testing for each smooth term. Model validation was conducted using the Akaike Information Criterion (AIC) and the Area Under the Curve (AUC) of the Receiver Operating Characteristic. Deforestation risk maps were generated for both models independently, and final susceptibility maps were classified into four risk categories (low, moderate, high, very high) using natural breaks.

Results

Results from the GLM indicated that elevation, wind exposure, distance to roads, distance to settlements, and mean temperature were statistically significant predictors of deforestation. The final model achieved a notable reduction in deviance (from 207.94 to 81.68) and an AIC value of 93.68, reflecting strong model fit and predictive capacity. According to the GAM results, the most influential factors included elevation, wind exposure, distance to roads, distance to settlements, and proximity to agricultural land—with human-related variables showing the highest contribution to deforestation probability. Specifically, increased distances from settlements (25.6%), agricultural land (22.1%), and roads (18.3%) were associated with decreased deforestation likelihood. The GAM outperformed the GLM, achieving an AIC of 21.07 and an AUC of 0.947. Final susceptibility maps from both models revealed that a substantial portion of the study area falls within the "very high" risk category, indicating critical vulnerability in the near future.

Conclusion

The findings underscore the applicability of deforestation risk modeling using GLM and GAM in supporting sustainable forest management. The outputs can serve as a decision-support tool for regional planning and targeted resource allocation. Moreover, advanced modeling frameworks such as GAM allow for more accurate estimation of nonlinear and complex interactions among predictors. The resulting deforestation zoning maps offer a scientific foundation for implementing proactive strategies, including legal restrictions in high-risk areas, community-based awareness programs, and the promotion of sustainable land-use practices. Integration of local data with robust global models enhances predictive performance and improves the effectiveness of policy interventions. Ultimately, this study not only contributes practical and theoretical insights into deforestation dynamics but also provides a transferable methodology for application in other ecologically sensitive regions.