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• AI for Conservation Office Hours: 2025 Review

Read about the advice provided by AI specialists in AI Conservation Office Hours 2025 earlier this year and reflect on how this helped projects so far.

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• AI for Conservation Office Hours: 2025 Review

Read about the advice provided by AI specialists in AI Conservation Office Hours 2025 earlier this year and reflect on how this helped projects so far.

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• Can CBIs promote coexistence? A Case Study from Northern Tanzania

Can conservation-based incentives promote the willingness of local communities to coexist with wildlife? A case of Burunge Wildlife Management Area, Northern Tanzania

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• Application of computer vision for off-highway vehicle route detection: A case study in Mojave desert tortoise habitat

Driving off-highway vehicles (OHVs), which contributes to habitat degradation and fragmentation, is a common recreational activity in the United States and other parts of the world, particularly in desert environments with fragile ecosystems. Although habitat degradation and mortality from the expansion of OHV networks are thought to have major impacts on desert species, comprehensive maps of OHV route networks and their changes are poorly understood. To better understand how OHV route networks have evolved in the Mojave Desert ecoregion, we developed a computer vision approach to estimate OHV route location and density across the range of the Mojave desert tortoise (Gopherus agassizii). We defined OHV routes as non-paved, linear features, including designated routes and washes in the presence of non-paved routes. Using contemporary (n = 1499) and historical (n = 1148) aerial images, we trained and validated three convolutional neural network (CNN) models. We cross-examined each model on sets of independently curated data and selected the highest performing model to generate predictions across the tortoise's range. When evaluated against a ‘hybrid’ test set (n = 1807 images), the final hybrid model achieved an accuracy of 77%. We then applied our model to remotely sensed imagery from across the tortoise's range and generated spatial layers of OHV route density for the 1970s, 1980s, 2010s, and 2020s. We examined OHV route density within tortoise conservation areas (TCA) and recovery units (RU) within the range of the species. Results showed an increase in the OHV route density in both TCAs (8.45%) and RUs (7.85%) from 1980 to 2020. Ordinal logistic regression indicated a strong correlation (OR = 1.01, P < 0.001) between model outputs and ground-truthed OHV maps from the study region. Our computer vision approach and mapped results can inform conservation strategies and management aimed at mitigating the adverse impacts of OHV activity on sensitive ecosystems.

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• Nature Tech for Biodiversity Sector Map launched!

Conservation International is proud to announce the launch of the Nature Tech for Biodiversity Sector Map, developed in partnership with the Nature Tech Collective! 

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• BoutScout – Beyond AI for Images, Detecting Avian Behaviour with Sensors

In this case, you’ll explore how the BoutScout project is improving avian behavioural research through deep learning—without relying on images or video. By combining dataloggers, open-source hardware, and a powerful BiLSTM deep learning model trained on temperature data, the team has reduced the time needed to analyse weeks or even months of incubation behaviour to just seconds. This has enabled the discovery of new patterns in how tropical birds incubate their eggs across different elevations and climates. With tools soon to be released on PyPI, including a no-code platform for behavioural analysis, this work offers a fresh, scalable approach for conservationists and researchers working on breeding data at the tropics.

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• A new underwater camera trap for freshwater wildlife monitoring

Proud of sharing our paper introducing our underwater camera trap, a solution for automating the production of underwater images and videos of amphibians, reptiles, invertebrates, mammals...

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• BoutScout – Beyond AI for Images, Detecting Avian Behaviour with Sensors

In this case, you’ll explore how the BoutScout project is improving avian behavioural research through deep learning—without relying on images or video. By combining dataloggers, open-source hardware, and a powerful BiLSTM deep learning model trained on temperature data, the team has reduced the time needed to analyse weeks or even months of incubation behaviour to just seconds. This has enabled the discovery of new patterns in how tropical birds incubate their eggs across different elevations and climates. With tools soon to be released on PyPI, including a no-code platform for behavioural analysis, this work offers a fresh, scalable approach for conservationists and researchers working on breeding data at the tropics.