Understanding biodiversity and environmental change at the scale and speed now required depends on computational methods capable of extracting signal from noisy, heterogeneous ecological data. Researchers in these areas develop statistical models, Bayesian inference frameworks, dynamic and mechanistic simulations, species distribution and niche models, and network-based analyses of ecological communities. Machine learning and deep learning play a central role, from computer vision and bioacoustic classifiers that turn sensor and citizen-science streams into species-level observations, to models that link remote sensing and environmental DNA with population and ecosystem dynamics. Together these approaches make it possible to quantify patterns and drivers of biodiversity change, project ecosystem responses to climate and land-use pressures, and generate predictions robust enough to inform conservation practice and environmental policy.
People
| Name | Computational ecology and biodiversity research |
|---|
| Julia Day | Using stochastic modeling and simulation methods to interpret complex patterns of molecular genetic variation within natural populations. |
| Lucy van Dorp | Developing computational genomic methods to reconstruct the evolutionary history and transmission pathways of bacterial and viral pathogens.
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| Rory Gibb | Using computational data science and predictive modeling to map how environmental change shifts the ecology of reservoir hosts and the resulting risk of zoonotic disease outbreaks. |
| Kate Jones | Using large-scale ecological and epidemiological datasets to model global patterns of biodiversity change and zoonotic disease risk. |
| Joanne Littlefair | Developing large-scale genomic and bioinformatic tools, including airborne and aquatic environmental DNA (eDNA) methods, to rapidly detect species and track biodiversity change. |
| Dan Maynard | Applies theoretical and computational network models to predict ecological community dynamics, stability, and responses to environmental change. |
| Gemma Murray | Using evolutionary modeling and bioinformatic analysis of genome sequences to track how bacterial populations adapt and spread antimicrobial resistance in response to environmental pressure. |
| David Murrell | Applying theoretical mathematical modeling and computational ecology to understand the spatial dynamics of populations and the mechanics of species coexistence in diverse landscapes. |
| Tim Newbold | Employing large-scale statistical modeling of global databases to quantify how land-use change and climate change interact to restructure ecological communities. |
| Richard Pearson | Developing species distribution models and macroecological tools to predict biodiversity responses to climate and environmental change. |
| Alex Pigot | Building macroecological and climate-driven models to predict species distributions, extinction risk and tipping points under global change. |
| Sean Stankowski | Using genomic data and computational evolutionary methods to understand speciation and adaptation across space and time. |
| Gail Taylor | Using machine learning to understand plant genotype-to phenotype relationships in crops and trees, including remotely sensed data, molecular and morphophysiological traits and the leaf microbiome. |
Modules
