Rahmah N. Al-Qthanin

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress.

Recirculating Aquaculture Systems (RAS) have emerged as a sustainable approach to intensive fish production. In our previous work, we demonstrated the efficacy of a Deep Deterministic Policy Gradient (DDPG) reinforcement learning approach for optimizing feeding rates and maintaining water quality in RAS. However, the “black box” nature of deep reinforcement learning algorithms often impedes their adoption by fish farmers who lack specialized knowledge in artificial intelligence. This study addresses this gap by developing and evaluating methodologies to enhance the interpretability and explainability of DDPG-based control systems specifically for aquaculture practitioners. We employ decision tree approximation techniques to transform complex neural network policies into transparent rule-based systems while preserving performance. Our approach achieves 92.7% policy fidelity with only a 5.8% performance drop compared to the original DDPG controller. The resulting decision rules were validated with fish farmers, with 87% reporting improved understanding of system recommendations and 78% expressing increased trust in the technology. Feature importance analysis revealed dissolved oxygen, ammonia levels, and biomass as the primary decision factors. This work demonstrates how advanced artificial intelligence techniques can be made accessible to end-users through interpretable approximations, potentially accelerating the adoption of intelligent control systems in aquaculture and contributing to more sustainable food production practices.

Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest fires in the Ghalahmah Mountains, Saudi Arabia, using remote sensing data and fire impact models to assess fire severity, environmental drivers, and post-fire vegetation recovery. The research integrates Landsat 8, Sentinel-2, and DEM data to analyze the spatial extent and severity of a 2020 fire event using the Relativized Burn Ratio (RBR). Results reveal that high-severity burns covered 49.9% of the affected area, with pre-fire vegetation density (NDVI) and moisture (NDWI) identified as key drivers of fire severity through correlation analysis and Random Forest regression. Post-fire vegetation recovery, assessed using NDVI trends from 2021 to 2024, demonstrated varying recovery rates across vegetation types. Medium NDVI areas (0.2–0.3) recovered fastest, with 134.46 hectares exceeding pre-fire conditions by 2024, while high NDVI areas (>0.3) exhibited slower recovery, with 26.55 hectares still recovering. These findings underscore the resilience of grasslands and shrubs compared to dense woody vegetation, which remains vulnerable to high-severity fires. The study advances fire ecology research by combining multi-source remote sensing data and machine learning techniques to provide a comprehensive understanding of fire impacts and recovery processes in semi-arid mountainous regions. The results suggest valuable insights for sustainable land management and conservation, emphasizing the need for targeted fuel management and protection of ecologically sensitive areas. This research contributes to the broader understanding of fire ecology and supports efforts to post-fire management.

This study presents a thorough revision of the Malvaceae s.l. (sensu lato) family within the Flora of the Toor Al-Baha District, Lahij Governorate, South Yemen. The documented taxa were subjected to morphological revision alongside an analysis of life forms and chorological affinities. Thirty taxa are categorized into 12 genera within four subfamilies. Each species is presented with its current classification, accepted names, synonyms (when applicable), vernacular names (when applicable), photographs, and distribution to aid in identifying and recognizing these species. The documented mallow species comprises 80% perennials and 20% annuals. Chamaephytes constituted the predominant life forms, comprising 50% of the total taxa surveyed. A phytogeographical analysis revealed that chorotypes with Sudano-Zambezian affinities are predominant, comprising 83.34% of the recorded mallows. The two subfamilies, Grewioideae and Sterculioideae, are distinguished by the lack of an epicalyx and the presence of an androgynophore, unlike the subfamilies Dombeyoideae and Malvoideae. The highest richness genera included Grewia, with seven taxa, and Hibiscus, Abutilon, and Corchorus, each comprising four species. The research presented nine artificial keys for wild mallows in the Lahij Governorate, comprising three for subfamilies and six for genera.

Assessing species at the regional level for their conservation is a vital first step in identifying and prioritizing species for both ex situ and in situ conservation actions. The complex coastal geomorphology of the Farasan Archipelago gives rise to promontories and bays that fragment the coastal flora. Climate change studies, combined with a case study of anthropogenic land use changes such as urbanization, tourism, and fishing, highlight the threat to the fragmented plant populations. In this study, the regional IUCN categories and criteria have been used to assess the conservation status of twelve targeted taxa of the Farasan Archipelago based on the data collected during field surveys and a literature review. According to our results, six species have been categorized as endangered (EN), four species as vulnerable (VU), and two species as near threatened (NT). Compared to an earlier assessment at the global level, Avicennia marina and Rhizophora mucronata have been re-categorized with a high degree of threat and ten species have been assessed for the first time. An effective action plan for the protection of the coastal zone and inland area biodiversity of the Archipelago is crucial to reduce threats to the islands’ plants.

The Plicosepalus genus includes hemiparasitic mistletoe and belongs to the Loranthaceae family, and it has several medicinal uses. In the present study, we sequenced the complete plastomes of two species, Plicosepalus acaciae and Plicosepalus curviflorus, and compared them with the plastomes of photosynthetic species (hemiparasites) and nonphotosynthetic species (holoparasites) in the order Santalales. The complete chloroplast genomes of P. acaciae and P. curviflorus are circular molecules with lengths of 120,181 bp and 121,086 bp, respectively, containing 106 and 108 genes and 63 protein-coding genes, including 25 tRNA and 4 rRNA genes for each species. We observed a reduction in the genome size of P. acaciae and P. curviflorus and the loss of certain genes, although this reduction was less than that in the hemiparasite and holoparasitic cp genomes of the Santalales order. Phylogenetic analysis supported the taxonomic state of P. acaciae and P. curviflorus as members of the family Loranthaceae and tribe Lorantheae; however, the taxonomic status of certain tribes of Loranthaceae must be reconsidered and the species that belong to it must be verified. Furthermore, available chloroplast genome data of parasitic plants could help to strengthen efforts in weed management and encourage biotechnology research to improve host resistance.

Wildfires significantly alter soil properties and result in vegetation shifts; therefore, rapid reforestation activities are needed in the forests affected by wildfires. The decreased nutrient in the soil is the obvious effect of wildfires; however, little is known about the reforestation of Juniper (Juniperus procera) forests with application of NPK fertilizers. Juniper forests are common in Asir and Taif regions of Saudi Arabia and vulnerable to wildfires; thus, reforestation is needed after the onset of fires. This study assessed the impact of different doses of organic NPK fertilizer (0, 5 and 10 g/L) on growth and nutrient accumulation of Juniper trees grown on fire-damaged and intact soils. Data relating to tree height, number of leaves per plant, fresh and dry biomass accumulation in shoot and root, chlorophyll contents and uptake of N, P, K, and Na were recorded. Individual and interactive effects of soil types and fertilizer doses significantly altered all measured traits with minor exceptions. Overall, higher values of the measured traits were recorded for intact soil and 10 g/L fertilize dose. The increasing fertilizer doses improved the growth and nutrient acquisition and application of 10 g/L fertilizer on intact soil recorded the highest values of growth traits. Juniper trees grown on fire-damaged soil accumulated higher amount of nitrogen than intact soil. Similarly, the trees grown on intact soil accumulated lower amount of Na and maintained comparable K/Na ratio to intact soil. It is concluded that supplying 10 g/L fertilizer could improve the establishment of Juniper trees on fire-damaged soil. Therefore, organic fertilizer can be used to improve the reforestation of wildfire-affected Juniper forests in the Asir province.