To build technical capacity across diverse conservation contexts, we have created a modular portfolio of standardized training materials that teach foundational competencies in conservation technology. These materials are organized into themed modules, such as wildlife monitoring, wildlife protection, and human-wildlife conflict, and are designed to be flexible and adaptable based on regional needs.

In collaboration with local host institutions and regionally recruited trainers, we tailor the curriculum to align with local ecological conditions, institutional priorities, regulatory frameworks, and learning styles. For example, because drone use is permitted in Kenya but restricted in Tanzania, modules are adjusted accordingly to ensure all content is actionable within the participant's home context. This approach ensures the training is both locally relevant and practically applicable, maximizing its long-term impact.

Examples of our core training portfolio include:

• Wildlife monitoring: Camera traps, biologgers, acoustic sensors, GPS tracking
• Wildlife protection: SMART, EarthRanger, infrared cameras, radios, K9 units, drones
• Human-wildlife conflict mitigation: Electric fencing, networked sensors, deterrent systems
• Cross-cutting tools: GIS and remote sensing, artificial intelligence, and introductory coding and electronics

Our standardized training curriculum is delivered by female experts (academics, practitioners, and government professionals) working in conservation and conservation technology within the local region. These women serve not only as instructors, but also mentors and collaborators. By centering local female role models, we help participants envision pathways for their own careers while strengthening their ties to regional research and conservation communities. We strive to foster an inclusive environment for honest dialogue around challenges of being a woman in conservation technology and encourage lasting mentorship relationships beyond the formal training period.

However, the gender gap we seek to address can make it difficult to identify and recruit female trainers in certain technical fields. In response, we have defined three distinct roles to broaden the support system for participants:

• Mentors: Local female role models who lead sessions and provide ongoing mentorship.
• Allies: Male trainers and facilitators who actively support our commitment to gender equity and inclusive training spaces.
• Trainers: Members of the international organizing team who provide additional instruction and logistical support.

Together, these individuals play a critical role in delivering content, fostering participant growth, and modeling diverse forms of leadership across the conservation technology landscape.

Host institutions are selected based on their capacity to support both classroom and field-based instruction, and on their engagement with active conservation challenges where technology plays a meaningful role. For instance, the RISE Grumeti Fund in Tanzania is an ideal training site, offering educational facilities, student accommodations, and running active, tech-enabled initiatives such as anti-poaching and rhino protection programs.

Furthermore, we prioritize institutions that share our commitment to advancing education for women and early-career conservationists, have strong ties to local conservation and research communities, and demonstrate leadership in integrating technology into conservation practice. These partnerships are essential to ensuring our program is both sustainable and deeply embedded in the communities it aims to serve.

Strong partnerships with local NGOs and educational departments have been critical to the success of the Arribada Clubs. These partnerships enable the customization of the curriculum to reflect community-specific conservation priorities, such as sea turtle protection in Príncipe or biodiversity monitoring in Kenya. Collaborative planning ensures that the clubs address local needs and have a lasting impact.

The Arribada Club provides hands-on STEM education tailored to conservation needs. Delivered through after-school programs in underserved communities, the curriculum incorporates local conservation challenges into lessons, fostering a deep connection between students and their environment. Students gain practical experience with tools like GPS, microcomputers, and bioacoustic monitoring, learning how these technologies support biodiversity conservation. This education empowers local youth with technical skills essential for both personal and community growth while fostering future conservation leaders.

The influence of natural and anthropogenic drivers on vegetation dynamics was explored using a Generalized Additive Model (GAM). This model evaluated non-linear relationships between vegetation changes and key factors:

• Spartina alterniflora was primarily influenced by marine environmental variables such as salinity and wave height.
• Phragmites australis and Suaeda salsa were affected by precipitation, anthropogenic pressures (e.g., aquaculture), and interspecies competition.

Understanding these drivers supports adaptive ecosystem management and invasive species control.

GBF Alignment: Supports GBF Targets 6 and 8.
Contribution: Predictive models improve on reactive conservation, offering measurable driver insights.

Spatiotemporal analysis was conducted to reveal long-term distribution patterns of wetland vegetation within the protected area from 1990 to 2022.

• Figure 1A illustrates changes in vegetation spatial patterns over time.
• Figure 1B presents percentage vegetation cover along the sea–land gradient.

Analytical tools such as landscape pattern indices, migration models, and expansion–contraction dynamics were used to quantify ecological changes.

Key Findings

• Spartina alterniflora exhibited high spatial aggregation but showed a declining trend over time.
• Phragmites australis and Suaeda salsa displayed greater fragmentation and increasing spatial coverage.
• Vegetation migration exhibited significant heterogeneity and a clear banded distribution along the land–sea gradient.

GBF Alignment: Aligns with GBF Target 2.
Contribution: Measurable outcomes enhance restoration planning, filling gaps in uniform management approaches.

A comprehensive geospatial database was developed, integrating vegetation cover data derived from remote sensing with key environmental, climatic, and anthropogenic variables. Included metrics encompassed soil salinity, sea surface temperature, seawater salinity, and locations of aquaculture ponds, providing a robust analytical foundation.

GBF Alignment: Supports GBF Target 21.
Contribution: Integrates diverse data layers for holistic analysis, adding value to fragmented conservation datasets.

Vegetation index time series were smoothed using Gaussian fitting to reduce noise and extract key phenological features. A random forest deep learning algorithm was applied to classify wetland vegetation into three dominant types: Spartina alterniflora, Phragmites australis, and Suaeda salsa. Classification accuracy from 1990 to 2022 was validated through field surveys.

GBF Alignment: Contributes to GBF Target 6.
Contribution: Reduces invasive species impact by accurately identifying Spartina alterniflora for targeted control, addressing a key biodiversity threat.

The purpose of this building block is to automate the traditionally manual process of wildlife monitoring. The model works by analyzing visual data, detecting the presence of vultures, and classifying them into species with high accuracy. This reduces human effort, accelerates data analysis, and ensures consistency in monitoring.