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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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• 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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• 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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• LEAPs and bounds

How better spatial data reduces nature risk for business

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• IUCN Red list R client.

Everything you need on the IUCN Red list is now accessible from R, outputted as tibble to facilitate data query, analysis, and visualization.

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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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• Population genetics with eDNA

Using SNP markers with an Amphibian species, we were able to identify a total of 17,617 nuclear single nuclear polymorphisms shared across individual, pond eDNA (4 populations) and tank eDNA samples (where tadpoles of the four ponds were housed), enabling us to detect genetic structuring across sampling locations (previously demenstrated with microsatellites and tissue samples), consistent with individual-based estimates. Collecting only the matrix (here water) allows describing the existing population structure...that could be a nice progress for conservation biology...

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• Siguiendo Ballenas: rastreo satelital desde el espacio revela sus rutas migratorias // Tracking right whales from outer space reveals their migratory routes

Un ambicioso proyecto internacional está usando transmisores satelitales para monitorear los viajes migratorios de ballenas francas australes, permitiendo entender sus sorprendentes patrones de desplazamiento en tiempo real.  //  An ambitious international project is using satellite tags to monitor the migratory journeys of southern right whales, allowing researchers to understand their surprising movement patterns in real time.

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• Opportunity to participate in study on what counts as evidence in conservation

Please help us by participating in a MSc research project on what you consider to be 'evidence' in your work in conservation.