https://ecopersia.modares.ac.ir/ ECOPERSIA en daily 1 Sat, 01 Aug 2026 00:00:00 +0330 Sat, 01 Aug 2026 00:00:00 +0330 https://ecopersia.modares.ac.ir/article_29178.html Aims: This study aimed to assess the honey bee (Apis mellifera) as a bioindicator of heavy metal contamination in District Kasur, Punjab, Pakistan, for ecological monitoring and food safety. Materials & Methods: Fresh honey samples were collected from beehives during the summer season in the morning to minimize external metal contamination. Samples were collected from five sites representing different pollution levels, including a forest area located far from tanneries (Changa Manga Jungle, 88 km from the control site and 56 km from Kasur tanneries), a low-pollution area (Village Ganda Singh Border, 75 km from the control site and 16 km from Kasur tanneries), a slightly polluted area (Raiwind Road, 42 km from the control site and 15 km from Kasur tanneries), a moderately polluted area (Village Sehjra, 84 km from the control site and 27 km from Kasur tanneries), and a control site (Lahore Jallo Park, 62 km from Kasur tanneries). We collected 12 individual samples from each site, for a total of 60 experimental samples. We used a Completely Randomized Design (CRD) with five treatments. Heavy metal concentrations were quantified in mg.kg⁻¹ using a Flame Atomic Absorption Spectrophotometer (FAAS). Findings: Results showed that the highest Cu content was recorded in samples collected from Village Sehjra (11.46 mg.kg⁻¹); on the other hand, the lowest amount was determined in the forest area (0.09 mg.kg⁻¹). Fe expressed the highest contents overall, reaching 15.23 mg.kg⁻¹ in the control site (Jallo Park, Lahore), whereas higher amounts were also observed in Village Sehjra (14.12 mg.kg⁻¹). Somewhat, high Fe concentration at the control site may reflect background geological characteristics and urban environmental influences rather than direct industrial emissions. Conclusion: These findings highlight the impact of environmental pollution on heavy-metal bioaccumulation in honey. The results show that honey bees serve as a good bioindicator for assessing environmental quality and ensuring food safety. https://ecopersia.modares.ac.ir/article_29362.html Aims: This study evaluated the effects of five Zeolite application rates on soil physical and chemical properties, irrigation water requirements, water use efficiency (WUE), and bermudagrass (Cynodon dactylon L.) growth under sports field conditions in southern Iraq.Materials & Methods: A field experiment was conducted during the winter season of 2026 at Basrah Sports City using five Zeolite rates (0, 5, 10, 15, and 20 t ha⁻¹) in a randomized complete block design (RCBD). We scheduled irrigation using a POGO TurfPro soil-moisture sensor calibrated against the standard gravimetric method to maintain soil moisture at field capacity. We measured soil electrical conductivity (EC), bulk density (BD), applied irrigation water (AIW), plant height (PH), root dry biomass (RDB), relative water content (RWC), shoot dry biomass (SDB), and WUE.Findings: Zeolite significantly increased EC from 0.35 to 0.46 dS.m⁻¹, while values remained non-saline. At 20 t.ha-¹ Zeolite application, AIW decreased by 7.2%, whereas PH increased from 17.50 to 21.25 cm (21.42%). RDB increased from 120.53 to 242.45 g.m⁻² (101.15%), RWC increased from 72.60% to 82.37% (13.46%), and SDB increased from 139.95 to 163.73 g.m⁻2 (16.98%). WUE reached 0.373 g.L⁻¹, a 26.02% improvement over the control.Conclusion: Zeolite amendment shows promise for improving soil-water management and irrigation efficiency in sandy sports-field soils, particularly when combined with soil-moisture monitoring, while supporting bermudagrass growth in southern Iraq. However, long-term studies are needed to determine optimal application rates and effects on turf quality, costs, and year-round performance. https://ecopersia.modares.ac.ir/article_29394.html Aims: This study used machine learning models, Random Forest (RF) and Boosted Regression Tree (BRT), to predict avalanche-susceptible areas avalanche-susceptible areas in the Central Alborz watershed, Alborz Province, Iran. Materials & Methods: We established a polygon-based framework based on three main avalanche zones: starting, track, and runout. Three training scenarios were considered for avalanche susceptibility modeling using RF and BRT: (1) the runout zone alone, (2) the starting and track zones, and (3) all three zones combined. We randomly split the avalanche inventory into 70% training and 30% validation datasets. We initially considered 21 topographic, geomorphological, and meteorological factors. We first conducted multicollinearity and Pearson correlation analyses to identify redundant predictors. We then applied Recursive Feature Elimination (RFE) with the Random Forest (RF) algorithm and out-of-bag (OOB) permutation importance to select the optimal predictor subset, retaining 13 factors for model development. We trained models under the three scenarios and evaluated them using the Receiver Operating Characteristic (ROC) curve, precision, sensitivity, F1 score, and Matthews Correlation Coefficient (MCC).Findings: The BRT model trained with the starting and track zones showed the best overall performance across most evaluation metrics (AUC = 0.95, precision = 0.93, F1-score = 0.91, and accuracy = 0.91).Overall, training both BRT and RF models using the starting and track zones substantially improved snow avalanche susceptibility prediction compared with the other scenarios. For the starting and track, runout, and all-zone scenarios, the RF model achieved AUC ≥ 0.90, while the corresponding BRT reached AUC ≥ 0.92 for all three scenarios.Conclusion: The results showed that the BRT and RF models achieved their highest predictive performance under the starting and track zones scenario, with AUC values of 0.95 and 0.93, respectively. These findings indicate that incorporating the spatial characteristics of avalanche zones can improve susceptibility assessment and provide useful information for avalanche hazard management and disaster risk reduction in mountain watersheds. https://ecopersia.modares.ac.ir/article_29393.html Aims: Desertification threatens the balance of dryland ecosystems across Nigeria’s Sudan Savannah zone. This study applies the Mediterranean Desertification and Land-Use (MEDALUS) model to assess the spatio-temporal dynamics of desertification sensitivity in Jigawa and Yobe States over 25 years (2000-2025) at five-year intervals.Materials & Methods: We integrated Landsat-derived Land-Use/Land-Cover (LU/LC) change detection with the MEDALUS quality index framework, an approach rarely applied at this temporal scale in the region. Soil (SQI), Vegetation (VQI), Management (MQI), and Climate (CQI) Quality Indices were combined to derive the Desertification Sensitivity Index (DSI); Mann-Kendall trend and Pearson/Spearman correlation analyses examined LU/LC-DSI relationships.Findings: LU/LC classification accuracy exceeded 94% (Kappa >0.89) across all epochs. Over 54% of the study area was classified under High desertification sensitivity at every epoch, peaking with a Very High DSI share of 7.02% in 2015 - driven by soil quality decline and bareland expansion - before moderating to 2.98% by 2025. LU/LC recorded a net farmland loss of 5,571.71 km² and bareland expansion of 1,492.70 km² (2015-2025). These changes are consistent with anthropogenic pressure being associated with DSI intensification, rather than establishing it as the principal causal driver, alongside the climatic influences discussed below.Conclusion: The findings highlight a persistently elevated but conditionally reversible desertification trajectory - the post-2015 amelioration being contingent on sustained land management and favorable climatic conditions rather than assured- emphasizing the need for land restoration policy and soil conservation strategies aligned with Nigeria’s Land Degradation Neutrality commitments and the African Union’s Great Green Wall Initiative.