For many conservationists, including our participants, the knowledge to effectively use conservation technology is not enough without the funding to access the tools. Recognizing this barrier, we provide each participant with $500 USD in seed funding to help them implement their conservation solutions. Additionally, we offer training in grant writing, pitching, and engaging with funders to enhance their ability to secure future funding.

For our technical training, we prioritize activities that allow students to directly interact with conservation technology tools. By setting up and deploying tools in safe, low-pressure environments, students have the opportunity to make mistakes and learn from those experiences. For example, letting students decide where to place a camera trap based on a lesson, and then evaluating the effectiveness of their decision by reviewing the data collected, is highly valuable. 

We select participants who are at the beginning stages of their careers, such as those who have completed their bachelor’s degrees and are entering the NGO or conservation workforce or embarking on higher education.The goal is to identify participants whose careers would benefit the most from the type and amount of training, funding, mentorship, and support we provide. 

To support our upskilling objectives across different contexts, we have developed a core portfolio of training materials. These materials focus on teaching fundamental competencies and are organized into themed modules (e.g., wildlife protection, human-wildlife conflict). Depending on the local context, we select the most relevant modules and training topics. Our locally recruited mentors and trainers are then encouraged to adapt these materials based on their specific expertise and background.

Our goal is that our core portfolio of standardized training materials are delivered by female experts recruited from the local region, who we further engage in mentoring and leadership activities. By centering these role models throughout our programming, we provide our participants with a vision of their future careers. We strive to foster an inclusive environment for honest dialogue and encourage ongoing mentorship even after the program concludes. However, the very gender gap we aim to address often presents a challenge when it comes to recruiting female educators and role models for our programs. This situation has helped us to differentiate three leadership roles: “mentors” (female role models, who participate in training and mentorship), “allies” (male trainers and facilitators), and “trainers” (support from international organizing team). Participation of each to these types of individuals is critical to develop and support our participants.

This program aims to equip women with practical skills that are actionable within their local context, enabling them to seize opportunities such as funding and career advancement within their specific regions. To achieve this, we collaborate closely with local partners and host institutions to adapt our core training materials, ensuring they align with local challenges, processes, and institutions. By tailoring our trainings to address the unique needs and contexts of the women we support, we maximize the relevance and impact of our programming. 

The Arribada Clubs integrate conservation technology into their STEM curriculum to teach students practical applications for environmental monitoring and problem-solving. Students learn GPS mapping, bioacoustic data analysis, microcomputer programming, and 3D printing to address conservation challenges. They design prototypes, analyze biodiversity data, and create digital libraries of natural specimens using 3D scanning, directly applying their learning to conservation efforts.

This building block democratizes access to cutting-edge technology, enabling scalable and cost-effective wildlife monitoring. Users can upload images or videos, and the application automatically detects and classifies species, providing actionable insights for decision-making.

The framework ensures that the AI model is robust and generalizable across different regions and habitats. Data collected is used to test the model’s ability to recognize vulture species in diverse conditions, providing feedback for further optimization.

This building block democratizes access to cutting-edge technology, enabling scalable and cost-effective wildlife monitoring. Users can upload images or videos, and the application automatically detects and classifies species, providing actionable insights for decision-making.