Exploring and analysing the key drivers of vegetation spatial and temporal distribution is of practical significance for monitoring the expansion of coastal wetland vegetation, species diversity conservation and sustainable management of coastal wetlands.

• Spartina alterniflora was influenced by marine factors (e.g., salinity and wave height).
• Phragmites australis and Suaeda salsa were driven by rainfall, anthropogenic activities, and interspecific competition.

Understanding these factors supports better management of invasive species and promotes biodiversity conservation.

Using spatial-temporal data, the long-term distribution characteristics of wetland vegetation were analyzed. Tools such as the landscape pattern index, migration modeling, and expansion/decline dynamics identified distinct trends:

• Spartina alterniflora patches showed high aggregation and a decreasing trend.
• Phragmites australis and Suaeda salsa patches displayed lower aggregation, higher fragmentation, and increasing trends.
• Vegetation migration patterns revealed significant spatial and temporal heterogeneity, with vegetation distributed in bands along the terrestrial gradient.

A comprehensive database was created, integrating remote sensing-derived vegetation cover with key environmental, climate, and human activity data. This includes metrics such as soil salinity, sea surface temperature, seawater salinity, and aquaculture pond locations, forming a robust foundation for further analyses.

A Gaussian function was applied to vegetation index time series to extract candidate features, while a deep learning algorithm identified three major vegetation types (Spartina alterniflora, Phragmites australis, and Suaeda salsa). Field validation confirmed the model's accuracy, enabling precise vegetation classification from 1990 to 2022.

Given Lebanon’s unique climate and sample conditions, we tailored our field and lab methodologies to optimize DNA extraction from local animal scats. This customization involved developing protocols specific to the Mediterranean environment and sample quality, ensuring accurate results and maximizing the data yield from each sample.

This collaboration has been crucial for knowledge exchange and the adaptation of advanced metabarcoding techniques to Lebanon. The Smithsonian team provided best practices on implementing DNA-based methods in ecological studies and offered expert advice on selecting the most suitable tools and instruments for metabarcoding analysis. This partnership has strengthened the scientific foundation of our project and ensured that our approach aligns with international standards.

The financial viability of solar solutions with reduced operational costs made them highly attractive to farmers. The installation of solar panels and pumps in Tajikistan currently has a payback period of approximately 8 to 10 years, given the existing energy tariffs. However, through the project, which covers about half of the farmers' expenses, this payback period could be reduced by half for those with access to the power grid. On the other hand, farmers without grid access often do not engage in gardening or agriculture. Some farmers are using diesel generators, which significantly increases their costs and contributes to atmospheric pollution. In such cases, the installation of solar solutions could serve as an effective alternative.

This building block emphasises community participation in monitoring, utilising citizen science and accessible data platforms to ensure local knowledge informs adaptive management and contributes to the long-term success of mangrove restoration.

The building blocks of NoArk's solution are interconnected to create a comprehensive, efficient system for conservation and environmental management. Bio-acoustic and chemical sensors collect critical ecological data, while Edge AI processing ensures rapid, on-site analysis, enabling immediate detection and response. These components are supported by LoRaWAN connectivity, which facilitates reliable, long-range communication in remote areas. The processed data is centralized on the PAMS dashboard, where it is visualized and analyzed for actionable insights, fostering better decision-making.

This system is strengthened by hyperlocal climate data, which enhances precision in risk assessments and planning. Finally, community and stakeholder engagement ensures the data and tools are effectively utilized, promoting collaboration and adaptability. Together, these elements form an integrated solution that empowers conservation efforts, addresses ecological threats, and supports sustainable development.

Through the Tunas Scholarship program and conservation education initiatives, HARPA builds long-term community support for conservation. This approach connects conservation with education and local development, ensuring sustainable impact through community involvement.