Mapping LIFE on Earth

Human-driven habitat loss is recognised as the greatest cause of biodiversity loss, but we lack robust, spatially explicit metrics quantifying the impacts of anthropogenic changes in habitat extent on species' extinctions. LIFE is our new metric that uses a persistence score approach that combines ecologies and land-cover data whilst considering the cumulative non-linear impact of past habitat loss on species' probability of extinction. We apply large-scale computing to map ~30k species of terrestrial vertebrates and provide quantitative estimates of the marginal changes in the expected number of extinctions caused by converting remaining natural vegetation to agriculture, and also by restoring farmland to natural habitat. We are also investigating many of the conservation opportunities opened up via its estimates of the impact on extinctions of diverse actions that change land cover, from individual dietary choices through to global protected area development.

LIFE v1

Our efforts through 2023-24 were focussed on building the first version of the LIFE metric and addressing peer review contents, with the research expertly lead by Alison Eyres. The LIFE: A metric for mapping the impact of land-cover change on global extinctions paper appeared in publication at the Royal Society in early 2025, and even covered by Mongabay.

The computational challenges of generating global maps for ~30k species at 1 arc-minute resolution required significant high-performance computing resources and careful attention to dataset versioning and reproducibility. As Patrick Ferris notes, large ecological datasets are inherently difficult to version and reproduce, making our approach of coupling persistence scores with HPC infrastructure particularly important for ensuring scientific reproducibility.

A false colour version of the LIFE dataset over central and south America.

Our research efforts in 2025 are focussed on improving the resolution of the persistence maps, increasing the coverage of species, and performing more analyses to identify newer conservation opportunities. This work is part of broader planetary computing efforts to make global-scale biodiversity data accessible for decision-making.

Computational Challenges and Infrastructure

The LIFE metric represents a huge computational ecology challenge, requiring processing of species occurrence data, habitat maps, and persistence calculations across multiple spatial and temporal scales. Our work highlights the broader challenges in computational conservation, where the scale of ecological data now requires sophisticated computational infrastructure.

Key technical challenges we've addressed include Managing and versioning terabyte-scale biodiversity datasets across time, scaling persistence score calculations across 30,000+ species, ensuring reproducible computational workflows for ecological modeling, and balancing computational efficiency with ecological model accuracy. This computational ecology approach is increasingly vital as conservation decisions require rapid, evidence-based analysis of large-scale environmental data, and I am hosting the 2nd outing of Programming for the Planet in October 2025 to continue this conversation.

Mapping other spatial threats

Unfortunately, individual species are affected by anthropogenic threats beyond simply habitat loss from landuse change, including hunting, agricultural practices and the introduction of invasive species. Emilio Luz-Ricca is conducting his PhD research on this topic, in particular focussing on per-species abundances and threats. Charles Emogor (who completed his PhD in 2024 on threats to pangolins via hunting) is joined the Computer Lab in 2025 as a Schmidt Sciences fellow and is applying machine learning to predicting sources of hunting pressures on wild species.

Area of Habitat Maps and Species Distribution Models

Common Base Maps for Area of Habitats

AoH calculations per species are really important to agree on, and are generated from a combination of range maps, habitat preferences, climatic variables and occurrence data. Michael Dales and I are working with other developers of biodiversity metrics (such as IUCN's STAR team) which also require AoH maps to develop a common base layer that can be maintained communally. This will also make it far easier to pinpoint algorithmic differences between STAR and LIFE rather than simply varying because of differing input data.

You can find the code for our area-of-habitat calculators for 30k terrestrial vertebrates online, and (thanks to a UKRI funded project in 2024) this will be expanded to include plants. These AoH maps are a crucial input to the LIFE metric, forming the foundation layer that allows us to quantify the marginal impact of land-cover changes on species extinctions at global scale. The combination of AoH maps with persistence score calculations represents a breakthrough in making biodiversity impact spatially explicit and computationally tractable through high-performance computing.

Species Distribution Modelling

One use for AoH maps is to turn them into Species Distribution Models, which is a way to predict where species are likely to be found based on environmental variables and occurrence data. Emily Morris worked on a new method that uses a combination of satellite data and machine learning to predict the distribution of species across the globe, with her focus being on proteas. Read more about it in Towards Scalable Deep Species Distribution Modelling using Global Remote Sensing.

Applications of the LIFE Metric

Food Systems and Supply Chains

Agriculturally-driven habitat degradation and destruction is the biggest threat to global biodiversity, and so an exciting line of work that Thomas Ball has been leading is to tie the LIFE metric with food consumption and production data and provenance modelling in order to figure out the impact of what we eat on species extinctions. Our Food impacts on species extinction risks can vary by three orders of magnitude paper, published in Nature Food, reveals that impacts of producing one kilogram of different foods on species extinction risks can vary by more than three orders of magnitude. This work links agricultural production and trade data with the LIFE metric to show that animal products and tropical commodities are generally far more impactful than staple crops and vegetables - insights that can inform everything from national policies to individual dietary choices.

Despite marked differences in per-capita impacts across countries, there are consistent patterns that could be leveraged for mitigating harm to biodiversity. This work connects to broader questions about sustainable food systems and how computational tools can help consumers and policymakers understand the biodiversity consequences of dietary and agricultural choices. We're continuing to work on refining this data and analysis, particularly via higher resolution supply chain datasets and crop yield data.

Diverse Conservation Applications

The practical applications of LIFE span diverse conservation challenges beyond food systems. Our Informing Conservation Problems and Actions Using an Indicator of Extinction Risk paper demonstrates five distinct use cases showing the metric's flexibility:

Near real-time monitoring: Quantifying biodiversity harms in tropical hotspots by integrating LIFE with forest loss data
Food consumption impacts: Assessing variation in extinction impacts mediated by specific foods like apples in the UK
Biodiversity compensation: Testing LIFE's suitability for offsetting through scenarios in Sumatran rainforests
Conservation prioritisation: Comparing benefits of competing area-based conservation projects in the Honduras
Project effectiveness: Evaluating long-term conservation interventions using counterfactual methods in Sierra Leone

These applications are intended to show that LIFE offers actionable insights into a geographically and thematically wide range of conservation challenges, from land-use planning to sustainable consumption. Like all global metrics, the broad applicability of LIFE relies on assumptions and simplifications, and should be used cautiously alongside local knowledge and ground-truthing, especially for restoration, crediting, or fine-scale analysis.