Modelling Species Distribution

Agriculture and biodiversity management require precise prediction of species' occurrence and other environmental variables. Gaussian processes provide a statistical framework to incorporate spatial and temporal correlations in species abundance data, but traditional methods are computationally intensive and limited.

Quantum computing-enhanced Gaussian process regression could speed up these calculations, enabling more accurate, fine-scale predictions of species occurrence and abundance under changing environmental conditions.

This approach can support improved biodiversity management and conservation within agricultural ecosystems.