How advanced computational tools are revolutionizing our ability to monitor, understand, and safeguard biological diversity
Our planet's biodiversity—the dazzling variety of life from genes to ecosystems—is a fundamental principle of life, underpinning the resilience of nature and human well-being. Yet, this intricate web is unraveling at an alarming rate. Scientists warn we are in the midst of a biodiversity crisis, with species vanishing before we even have a chance to document them 4 .
The challenge is monumental; how can we protect what we don't fully know or understand?
Enter computational science. In an era defined by data, a new, digital ally has emerged. Advanced computational tools are now revolutionizing our ability to monitor, understand, and safeguard biological diversity. From artificial intelligence (AI) that identifies species from photos to "digital twins" that simulate entire ecosystems, computational science is placing the power to protect nature firmly in our hands, offering a beacon of hope in the race against time 2 9 .
Species are vanishing at an unprecedented rate, with many disappearing before we can even document them.
Advanced computational tools are transforming how we monitor, understand, and protect biodiversity.
Computational biology is transforming our relationship with the natural world by turning massive, complex data into actionable knowledge. Several key concepts are central to this transformation.
Analyzing volumes of data beyond human capacity to identify species, map distributions, and discover new species.
Creating virtual replicas of ecosystems to simulate outcomes under different scenarios.
Addressing critical gaps in what we know about species distributions and ecosystem roles.
At the heart of this revolution is artificial intelligence. AI, particularly machine learning, can analyze volumes of data far beyond human capacity. In palaeontology, AI algorithms are automating the analysis of fossil data, extracting morphological traits, and modeling evolutionary dynamics across deep time 2 .
For living species, AI-powered tools like BioCLIP are being used to detect species traits from images, dramatically accelerating species identification and discovery 5 . Researchers from McGill University note that AI's potential is vast, with the capability to map species distributions more accurately than ever before and even infer complex species interactions, like food webs, that are difficult to observe directly 5 .
AI can identify species from images with over 95% accuracy for many taxonomic groups.
One of the most exciting frontiers is the creation of "digital twins" for ecology. A digital twin is a virtual representation of a real-world entity, continuously updated with real-time data. Ecologists are now using this technology to simulate everything from bird migrations to entire predator-prey relationships.
Forecasts the migration of common cranes across Europe using migration data, real-time sightings, and environmental factors.
Models interactions between vegetation, rabbits, and the endangered Iberian lynx to guide reintroduction efforts.
Why are these tools so needed? Scientists have identified seven "global biodiversity knowledge shortfalls"—critical gaps in what we know about species, from their geographical distributions to their roles in the ecosystem 5 . A major review found that AI is currently only being used to address two of these seven shortfalls, meaning we have only begun to scratch the surface of its potential to illuminate the dark corners of our planetary biodiversity 5 .
To understand how computational science works in practice, let's take an in-depth look at a specific, large-scale initiative from Japan: the Biome mobile app.
Launched in 2019, Biome was designed to make wildlife surveying an easy and fun activity. The core procedure can be broken down into a few key steps:
Users photograph organisms using their smartphone's GPS-enabled camera, automatically recording time and location.
The app implements AI algorithms to generate a list of potential species for the photographed organism.
Users earn points and levels for submitting records and helping others, creating an engaging, game-like experience.
All verified observations are compiled into a massive, growing database with over 6 million observations—more than four times the number of records gathered in Japan during the same period by the Global Biodiversity Information Facility from all other sources 8 .
The researchers then investigated the quality and utility of this community-sourced data.
First, they assessed species identification accuracy, which was found to be exceptionally high for certain groups like birds, reptiles, mammals, and amphibians (exceeding 95%), though lower for more challenging taxa like seed plants and fish 8 .
More importantly, they used Species Distribution Models (SDMs)—statistical tools that estimate a species' geographic range—to test the value of the Biome data. They modeled the distributions of 132 terrestrial plants and animals across Japan under two scenarios: one using only traditional scientific survey data, and another blending traditional data with Biome's community-sourced data 8 .
The results were striking. The blended models were significantly more accurate. For endangered species, the traditional data required over 2,000 records to produce a highly accurate model (Boyce index ≥ 0.9). In contrast, blending the two data sources reduced this requirement to just around 300 records 8 .
The success of the Biome data lies in its ability to correct for a major bias in traditional data. Scientific surveys are often biased towards natural, remote areas, while community-sourced data from apps like Biome provides uniform coverage across both urban and natural landscapes. This gives a much more complete picture of where species can and do occur, leading to superior distribution models 8 .
The experiment with the Biome app relied on a suite of modern tools. The following details some of the key "research reagents"—the essential digital materials and solutions—that are powering the new era of computational biodiversity science.
Analyze complex datasets (images, sounds, satellite data) to identify species, map distributions, and discover new species.
Collect genetic material from soil or water to detect species presence without direct observation, revolutionizing monitoring.
Engage the public to contribute vast amounts of observational data, drastically expanding the scale and scope of data collection.
Create virtual replicas of ecosystems to simulate outcomes under different scenarios, enabling predictive conservation and management.
Provide large-scale, continuous data on habitat change, land use, and vegetation health from space.
Process and analyze massive datasets to uncover patterns, trends, and insights that would be impossible to detect manually.
The fusion of biodiversity science and computational power is no longer a futuristic concept; it is a present-day reality that is fundamentally altering our capacity to be stewards of the natural world. By leveraging AI, digital twins, and the collective power of citizen scientists, we are beginning to fill critical knowledge gaps and make conservation more efficient, predictive, and impactful.
The story of the Biome app shows that when we combine technology with human curiosity, we can generate the high-resolution data needed to track progress against global goals like the Kunming-Montreal Global Biodiversity Framework's "30 by 30" target 8 .
As a new Perspectives paper stresses, there is an ethical imperative to ensure equitable access to these powerful AI and digital tools. Disparities in computing infrastructure and expertise risk widening the gap between well-resourced and developing regions 2 .
The future of biodiversity conservation depends not only on advanced algorithms but on inclusive, global collaboration. The principle of life is biodiversity. The tool to uphold it is now in our hands. Through the thoughtful and shared application of computational science, we can aspire not just to slow the loss of life's variety, but to foster a future where both humanity and nature thrive together.
Ensuring equitable access to computational tools across all regions.
Using computational tools to safeguard ecosystems and species.
Fostering a future where humanity and nature thrive together.
References will be listed here in the final publication.