Alina Daati leads Intro to Conservation Tech for Wildlife Protection
Stephanie O'Donnell
Charlotte Searle leads Intro to Camera Trapping
Stephanie O'Donnell
Meredith Palmer leads The Future of Conservation Technology
Stephanie O'Donnell
Esther Githinji leads I am a Woman in Conservation Technology
Stephanie O'Donnell
Vainess Laizer leads Data Collection Apps
Stephanie O'Donnell
George Lohay leads Conservation Genetics
Stephanie O'Donnell
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
Human-wildlife conflict mitigation: Electric fencing, networked sensors, deterrent systems
Cross-cutting tools: GIS and remote sensing, artificial intelligence, and introductory coding and electronics
Core materials are developed by world leading conservation technology experts
Multiple years of programming have allowed us to refine and improve our training materials
Annual participant feedback helps guide refinement of content and development of new topics
Host institutions and local partners provide valuable input on the most relevant training needs
Educational systems vary significantly, even across countries in the same region. For example, certain types of trainings or activities - such as active learning approaches - may be more difficult for students from countries where education is centered on rote memorization. Understanding local learning preferences and adapting teaching methods accordingly can support deeper engagement.
Certain technologies or methodologies, such as drones or cloud-based data storage, may be prohibited or prohibitively expensive in some regions. Partnering with local conservation technology experts ensures that we focus on accessible, actionable technologies for our participants.
Asking local trainers develop their own materials often exceeds their time and capacity
Using standardized materials ensures consistency and reduces variability in the type and depth of content delivered.
Identifying impactful mentors, trainers, and allies
Students and mentors at WiCT Tanzania
Stephanie O'Donnell
Instructor and mentor Vainess Laizer supports WiCT participants
Stephanie O'Donnell
Mentor and instructor Alina Daati teaches Intro to Conservation Technology
Stephanie O'Donnell
Instructor and ally Benson Benjamin teaches wildlife tracking
Stephanie O'Donnell
Mentor Ashura leads a session on human-wildlife mitigation
Stephanie O'Donnell
Tanzanian government official and mentor Janemary Ntalwila leads a session on HWC
Dany Samwell
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.
Keen interest from female leaders to foster the next generation of conservationists, including willingness to engage honestly in vulnerable conversations and provide career advice
Growing interest from allies to support development of women in their field and organizations
Funding to support attendance and honorarium for high-quality mentors and allies
We have established a code of conduct and set clear expectations up-front on how mentors and allies should engage with students during and after the program
Mentors and allies with a background in training as well as expertise in conservation tech are preferred
Wherever possible, we seek a combination of mid-career and established mentors, who can speak to participants about different stages of the conservation career journey
Male allies need to be carefully selected to create a supportive, safe environment
We maintain and cultivate female-only spaces at the workshop where male allies and trainers are not allowed
Participants engage in classroom activities at RISE Grumeti
Dany Samwell
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.
Local partners with aligned visions in education, upskilling, and empowerment
On-the-ground support from women within the host and collaborating organizations
Networks of experienced local educators and trainers in the conservation technology space
Host institutions with strong ties to local conservation, research, and government networks are best positioned to identify and recruit experienced female professionals to serve as trainers and mentors.
Institutions that already manage other training programs often have existing infrastructure and logistical systems in place, making them well-equipped to support student cohorts.
Sites where a wide range of conservation technologies are actively in use offer students valuable, hands-on exposure to tools in real-world settings.
A shared commitment to the program’s vision, particularly around gender equity and empowerment, is essential to creating a safe, supportive environment where women can build community, grow professionally, and develop leadership skills.
This building block leverages Declas, an open-source AI tool, to automate vulture monitoring. By analyzing images or videos, it detects and classifies species with high accuracy. The system eliminates manual counting, enabling scalable, cost-effective wildlife tracking. Users—researchers, rangers, or conservationists—simply upload visual data, and the tool generates real-time insights for informed decision-making. Built on YOLOv11 (Ultralytics) and trained on crowdsourced data.
A simple and intuitive user interface to ensure accessibility for non-technical users.
Documentation and training resources for users to understand and effectively utilize the application.
Community feedback to continually enhance the tool’s usability and features.
Usability is key; overly complex interfaces deter users.
Offering technical support and clear documentation ensures broader adoption.
Integration challenges included aligning the AI model’s output with user-friendly visualization tools; iterative testing was essential to resolve this.
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.
Deployment of drones and camera traps in strategic locations within reserves for optimal coverage.
Collaboration with local conservation teams for field logistics and data collection.
Consistent testing and refinement of the model based on field results to address discrepancies.
Having local partnerships ensures smoother field operations and enhances data collection efficiency.
A major challenge was dealing with low-quality or insufficient data; addressing this required setting up more camera traps in diverse locations.
The building block aims to automate vulture monitoring by developing a model to detect and classify four vulture species (Gyps africanus, Gyps coprotheres, Gyps rueppelli, Torgos tracheliotos) from visual data, reducing manual effort, speeding up analysis, and ensuring consistency. It leverages Google Colab Pro+ to run Python code and train the model on large image datasets, utilizing the Ultralytics package with YOLOv11 for vulture classification. Images are stored on a 2 TB Google Drive, sourced from the iNaturalist database via the rinat R package and supplemented by data from the Southern African Wildlife College and Endangered Wildlife Trust. The Computer Vision Annotation Tool (CVAT) team plan enables collaborative image annotation, allowing multiple users to label and export images with annotations for training and validation.
A high-quality, annotated dataset with diverse images representing the target species in different environments and conditions.
Access to computational resources (Google Colab Pro+)for training and validating the AI model.
Collaboration with conservationists to validate the model’s results in field conditions.
Ensure the dataset is representative of real-world conditions to avoid bias in detection (e.g., lighting, angles, habitats).
Regular updates to the model with new data improve accuracy and adaptability.
Challenges include misclassifications due to overlapping species traits; having experts validate initial results is essential.
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.
A simple and intuitive user interface to ensure accessibility for non-technical users.
Documentation and training resources for users to understand and effectively utilize the application.
Community feedback to continually enhance the tool’s usability and features.
Usability is key; overly complex interfaces deter users.
Offering technical support and clear documentation ensures broader adoption.
Integration challenges included aligning the AI model’s output with user-friendly visualization tools; iterative testing was essential to resolve this.
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.
Deployment of drones and camera traps in strategic locations within reserves for optimal coverage.
Collaboration with local conservation teams for field logistics and data collection.
Consistent testing and refinement of the model based on field results to address discrepancies.
Having local partnerships ensures smoother field operations and enhances data collection efficiency.
A major challenge was dealing with low-quality or insufficient data; addressing this required setting up more camera traps in diverse locations.
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.
A high-quality, annotated dataset with diverse images representing the target species in different environments and conditions.
Access to computational resources (Google Colab Pro+) for training and validating the AI model.
Collaboration with conservationists to validate the model’s results in field conditions.
Ensure the dataset is representative of real-world conditions to avoid bias in detection (e.g., lighting, angles, habitats).
Regular updates to the model with new data improve accuracy and adaptability.
Challenges include misclassifications due to overlapping species traits; having experts validate initial results is essential.
