Soil health reduction following the conversion of primary vegetation covers in a semi-arid environment.

A degraded forest is the outcome of a degradation process that has adverse effects on ecosystem functions and services. This phenomenon results in alterations of soil physicochemical and biological properties, which serve as valuable indicators for assessing soil health that has been recognized as a crucial component of soil quality. For several decades, the conversion of forested areas into rangeland has been documented in specific regions of the world. There is a widespread lack of global understanding regarding the lasting consequences of land degradation on soil health indicators. The present study aims to investigate the impact of forest degradation on soil health indicators in a mountainous semi-arid region located in northern Iran. The study area was predominantly forested, but due to human activities over the past 30 years, it has been transformed into three distinct land uses: forest, forest-rangeland ecotones and rangeland. In each of these land covers, a total of 20 litter (O-horizon) and 20 soil (from two depths of 0-15 and 15-30 cm) samples were collected in the summer (August 2022) season. According to our results, the highest litter thickness, P and Mg were in forest ecosystem, the lowest in rangeland ecosystem. The findings indicated that following the conversion of forest to rangeland, there was a decrease in soil aggregate stability, porosity, soil organic matter, POC, PON, NH4+, NO3- and nutrient levels, while soil bulk density increased. The forest ecosystem showed notably higher C and N stocks (45 and 5.21 Mg ha-1) in comparison to the rangeland (38 and 3.32 Mg ha-1) ecosystem. In addition, P, K, Ca, and Mg exhibited elevated levels within the total root of the forest ecosystem (2.12, 1.23, 0.71, and 0.38 %, respectively), whereas the lower values (1.29, 1.01, 0.43, and 0.23 %, respectively) were found in the rangeland ecosystem. Following the shift of land cover from forest to rangeland, soil fauna, microflora populations, soil enzymes and microbial activities decreased (about 1-2 times higher in the forestland). This research emphasizes the urgent need to advance sustainable management practices to prevent further degradation and promote the implementation of restoration or rehabilitation techniques in degraded forests. Despite being conducted in a semi-arid region situated in northern Iran, the findings of this study have considerable value for the sustainable management of soil and land conservation in various other semi-arid regions around the world.

Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning.

Snowstorms are disturbance agents that have received relatively little research attention rather than significant disturbances that they pose to forest ecosystems. In this study, we modeled the interactions between snowstorms and different characteristics of a forest stand in northern Iran and spatially visualized the susceptibility of the stand to damage caused by snowstorms using the random forest (RF) and logistic regression (LR) methods. After a severe snowstorm in November 2021 that caused stem breakage and uprooting of individual trees, the location of 185 damaged trees was identified via field surveys and used for generating an inventory map of snowstorm damage. The thematic maps of fourteen explanatory variables representing the characteristics of damaged trees and the study forest were produced. The models were trained with 70% of the damaged trees and validated with the remaining 30% based on the area under the receiver operating characteristic curve (AUC). The results indicated the better performance of RF compared to LR in both training (0.934 vs. 0.896) and validation (0.925 vs. 0.894) phases. The RF model identified slope, aspect, and wind effect as the variables with the greatest impacts on the forest stand sustainability to snowstorm damage. Approximately 30% of the study area was categorized as high and very high susceptible to snowstorms. Our results can enable forest managers to tailor more informed adaptive forest management plans in readiness for snowstorm seasons and recovery from their damage.