Beaufort Camera Trap Project
This project connects local organizations and individuals interested in documenting reptiles, amphibians, and small mammals throughout the Beaufort and Port Royal Sound areas. Using the AHDriFT camera-trap system, participants collect images and species identifications from multiple locations using a standardized format. This coordinated approach produces comparable data that can help improve our understanding of local species distributions, activity patterns, and populations.
Camera Trap Design Protocol
A standard protocol is needed to connect multiple organizations and camera-trap efforts across the region, ensuring that collected data are comparable and can contribute meaningfully to the larger project. These protocols were designed with simplicity in mind so that as many partners as possible can participate.
Camera Trap Photo ID Portal
To ensure accurate data entry and streamline the identification of large batches of camera-trap photographs, we developed the Camera Trap ID Portal. This tool supports efficient data collection and sharing by using integrated AI to identify blank images, enable batch photo identification, and document species determinations. These verified identifications will help build a training dataset for future machine-learning tools that can automate species identification.
Join the Project!
The project grows stronger with each additional contributor, partner, and stakeholder. Organizations and individuals interested in documenting reptiles, amphibians, and small mammals on their property are encouraged to join the project, follow the standardized protocols, and use the available tools.
Participation simplifies camera-trap data collection and management while allowing contributors to support a larger regional monitoring effort. If you would like more information or are unsure whether the project is a good fit for your property or organization, please contact us. We are happy to provide consultation and assist with camera-trap setup.
All contributing organizations will be recognized and acknowledged in publications produced from the shared dataset.