Evaluation and Modelling of Disturbances and Threats in Wad AlBashir Tropical Dry Forest, Elgadaref State, Sudan

Abstract

Tropical dry forests of Sudan provide multiple goods and services that are critical to sustaining societies and ecosystems; however, several emerging risks pose serious implications on their survival. Thus, this study aimed to assess, map and model the severity of anthropogenic, biotic and abiotic forest disturbances and threats in Wad Al-Bashir Forest
(WAF), so as to restrict their impacts and hence combat and mitigate the adverse effects of drought and desertification. The ground-based-survey was used to gather the relevant data using a systematic sampling technique (800*800 m) where 54 circular sample plots were visited using a GPS device. In each sample plot, the effects of humans, pests, diseases,
fire and wind have been recorded.Data gathered for this work, were ranked based on three criteria: threats presence, intensity and insistence; and subsequently analyzed through the interpolation tool in ArcMap
software (Version 10.4.1) and eventually depicted on maps according to their adverse impacts as:severe, slight, mild and none, respectively. The results showed many forest disturbances and threats that varied in magnitude and spatial distribution. The rampant anthropogenic disturbances were farming activities followed by illegal tree cutting and
uncontrolled grazing. The common biotic disturbances were invasive species (Acacia nubica), climbers and pests (Sinoxylon senegalensis) respectively. The most existing abiotic threats were: wind and fires sequentially. Conclusively, forest disturbances and threats exist in WAF with different types and magnitude in spite of the continued forest
authorities’ efforts, which calls for extra conservation efforts. Thus, protecting this forest is crucial as human demand for forest products and services increases. This study, recommends a more sophisticated representation of forest disturbances and threats in the Sudan forested areas that considers their spatial, temporal and severity variabilities for long-term forest monitoring system.