Software
Freely available tools developed by the Levi Lab to support quantitative ecology, wildlife management, and biodiversity science.
The Levi Lab develops and releases open-source software tools to support reproducible research in wildlife ecology and conservation. Our tools span web-based simulation platforms, statistical models, and AI-assisted image analysis.
A web-based, user-friendly implementation of biodemographic models for assessing the long-term sustainability of subsistence hunting in tropical forests. Users can parameterize wildlife populations, hunting offtake rates, and landscape configurations to project long-term population trajectories.
Originally developed to support wildlife management planning for indigenous communities in Manu National Park, Peru, and subsequently applied across Amazonia and other tropical systems. The beta version was created by Tasman Thenell.
Levi, T., et al. (2009). Modeling the long-term sustainability of indigenous hunting in Manu National Park, Peru. Journal of Applied Ecology 46: 804–814.
Levi, T., et al. (2011). Spatial tools for modeling the sustainability of subsistence hunting in tropical forests. Ecological Applications 21: 1802–1818.
A simulation tool for modeling how forest exclusion zones around individual trees affect seed predation by host-specific enemies (pathogens and herbivores) and the maintenance of tropical tree diversity.
FEZ was developed to accompany our theoretical work demonstrating that tropical forests can maintain hyperdiversity through enemy-mediated mechanisms (Janzen-Connell effects). The simulator allows users to explore how zone size, enemy specificity, and dispersal interact to determine coexistence outcomes.
Levi, T., Barfield, M., Barrantes, S., et al. (2019). Tropical forests can maintain hyperdiversity because of enemies. Proceedings of the National Academy of Sciences.
An open-source platform for collaborative image labeling and the development and deployment of custom computer vision models for ecological research. Njobvu-AI allows research teams to collaboratively annotate camera trap images and train species detection models without requiring deep machine-learning expertise.
Developed by Cara Appel, Abhilash Subramanian, and collaborators, the platform is designed for wildlife ecologists who need scalable, user-friendly AI tools for processing large volumes of camera trap or passive acoustic data.
Appel, C.L., Subramanian, A., Koning, J.S., et al. (2025). Developing custom computer vision models with Njobvu-AI. Ecological Applications.
Koning, J.S., Subramanian, A., Alotaibi, M., et al. (2024). Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision models. ArXiv.
A highly sensitive, accurate, and affordable SNP genotyping method adapted from GT-seq for noninvasive genetic samples (hair, scat, environmental DNA). The approach uses high-throughput amplicon sequencing to genotype wildlife from field-collected samples with minimal DNA input.
This pipeline dramatically reduces the cost of noninvasive genetics studies, making population-level genetic monitoring feasible for a wider range of species and study systems. It has been applied to carnivores across the Pacific Northwest and Alaska.
Eriksson, C.E., Ruprecht, J., Levi, T. (2021). More affordable and effective noninvasive SNP genotyping using high-throughput amplicon sequencing. Molecular Ecology Resources.
Code & Data Availability
We are committed to open and reproducible science. Code and data associated with our publications are made available through GitHub, Dryad, and other public repositories wherever possible. Look for data availability statements in individual papers.