Environment & Disaster Resilience

GIS Mapping of Bamboo Resources in Sta. Rita and Matuguinao, Samar with RF Algorithm and Convolutional Neural Network (CNN) – Phase 2

['F Gomba', 'R Tabiongan', 'N Palomas', 'E Celada']

This research mapped bamboo resources in selected areas of Sta. Rita and Matuguinao, Samar to estimate bamboo area, determine species composition, and support sustainable resource planning. The study integrated Geographic Information Systems (GIS) with machine learning techniques, specifically Random Forest and Convolutional Neural Network algorithms, to improve the accuracy of bamboo resource mapping. Drone imagery, satellite validation, and ground surveys were utilized to analyze plantation distribution and generate detailed spatial data. Results demonstrated that combining GIS and machine learning methods enhances classification accuracy and provides reliable maps for bamboo resource monitoring. The findings support planning, conservation, and sustainable utilization of bamboo resources for ecological management and community development in Samar.

Bridging Knowledge Gaps Through Community Education on Riparian Ecosystem Conservation

['Macha', 'Echapare', 'Velasquez']

Riparian ecosystems are critical ecological zones that support biodiversity, regulate water quality, and provide essential ecosystem services such as flood control, nutrient cycling, and erosion prevention. However, these ecosystems are highly vulnerable to human-induced threats, including deforestation, pollution, agricultural expansion, and urbanization. This study examined community knowledge, attitudes, and practices regarding riparian ecosystem conservation in selected municipalities of Samar. Using qualitative methods such as focus group discussions, key informant interviews, and policy analysis, the research identified gaps in ecological awareness and inconsistencies in conservation practices. Findings revealed positive community attitudes toward conservation but limited policy awareness and environmental education, highlighting the need for strengthened community engagement and sustainable conservation initiatives.

Physio-Chemical Parameters as Predictors to HABs Occurrence and Severity BORDIOS_5

['Bordios', 'Balindo']

This study analyzed 108 months of hydro-climatic data from Irong-Irong Bay and Maqueda Bay (2016–2024) to determine key environmental predictors of harmful algal blooms (HABs). Using correlation analysis, SARIMAX modeling, and time-series forecasting, the research identified rainfall, humidity, temperature, and atmospheric pressure as significant drivers of bloom occurrence and severity. Results revealed a dominant 12-month seasonal cycle and projected increased HAB risks for 2025–2026. The study highlights the value of data-driven early warning systems and climate-informed coastal management. Findings support evidence-based decision-making for fisheries protection, public health safety, and sustainable marine resource management. It is recommended that local agencies strengthen monitoring systems and integrate predictive models into coastal governance programs.

Morphometric and Genetic Characterization of Blue Swimming Crab Portunus pelagicus (Forskal, 1775) Along the Eastern Coasts of Bicol and Eastern Visayas, Philippines

['Balindo']

This study examined the morphometric and genetic variation of the blue swimming crab (Portunus pelagicus) along the eastern coasts of Bicol and Eastern Visayas, Philippines. Using morphological measurements and DNA barcoding techniques, specimens from Albay, Catbalogan, Ormoc, and Sorsogon were analyzed to determine population structure and genetic diversity. Results showed significant variation in body proportions and coloration patterns but minimal genetic divergence, indicating a single species stock. The findings highlight the importance of integrating morphometric and molecular approaches in fisheries assessment and resource management. The study provides scientific evidence to support sustainable crab fisheries management and conservation strategies in coastal communities.

Awareness of Ecosystem Services and Management of Mangroves in Coastal Communities of Samar

['Balindo', 'Brillantes', 'Lagumbay', 'Amparado']

This study assessed the awareness of mangrove ecosystem services and management strategies among coastal communities in Samar, Philippines, and examined their implications for sustainable development and disaster resilience. Using a household survey approach, the study measured awareness of regulating, provisioning, and supporting ecosystem services and evaluated management practices and challenges. Results showed generally high awareness levels, particularly for regulating and provisioning services. However, organized community action and enforcement of mangrove protection policies were limited. Structural Equation Modeling confirmed that awareness significantly influences management practices and perceptions of challenges. The findings highlight the need to strengthen community-based governance and institutional support mechanisms.

Population Parameters of River Mullet “Ludong” (Cestraeus sp.) in Ulot River, Paranas, Samar, Philippines

['Bacnutan', 'Gamba', 'Diocton', 'Evardone', 'Tibar']

This study assessed the population parameters of the river mullet (Cestraeus sp.), locally known as “ludong,” in Ulot River, Paranas, Samar. Using length-frequency data analyzed through the FAO-ICLARM Stock Assessment Tools (FiSAT), the research estimated growth parameters, mortality rates, exploitation rate, recruitment patterns, and length–weight relationships. Results showed moderate growth (K = 0.23 yr⁻¹), high fishing mortality (F = 1.60 yr⁻¹), and an exploitation rate (E = 0.53) exceeding sustainable levels. Recruitment peaks were observed annually, but heavy capture of immature individuals threatens stock stability. Findings highlight urgent need for science-based fisheries management, including size limits, seasonal closures, and conservation measures to ensure long-term sustainability.

Profiling of Bamboo Resources in Samar

['Quebada', 'Labrador', 'Uy', 'Gomba']

This research project established baseline data on bamboo resources in four municipalities of Samar, namely Tarangnan, San Jorge, Gandara, and Pagsanghan. Field inventories were conducted using GPS and GIS technology to determine the distribution, species composition, and standing crops of bamboo planted under the National Greening Program. The study identified major bamboo species such as Kawayan Tinik, Kawayan Kiling, and Giant Bamboo. Results showed varying densities and productivity levels across barangays. The findings provide essential information for planning, management, and sustainable utilization of bamboo resources. The study recommends continuous monitoring, farmer training, and value-adding initiatives to strengthen bamboo-based livelihood and environmental conservation.

Strengthening Awareness of Food Security and Environment through Physics Education and Local Wisdom

['Gabane', 'Gabejan', 'Solayao', 'Oreo']

This study, Project SAFE-ED, assessed the impact of climate change on food security in Western Samar by analyzing historical climate trends and farmers’ lived experiences. Using a mixed-methods approach, the research synthesized 20 years of climate data with qualitative narratives to identify core physics principles embedded in agricultural practices. Findings served as a foundation for developing contextualized instructional materials aimed at improving climate literacy and community resilience. Results showed that rainfall variability and increasing temperature trends significantly affected agricultural productivity and food access. The study recommends integrating indigenous knowledge into science education, strengthening community-based climate adaptation programs, and enhancing collaboration between schools, LGUs, and farmers to promote food security and environmental sustainability.