Optimising biodiversity and carbon recovery in neotropical forests

There is an urgent need to understand and optimise the recovery of biodiversity and carbon stocks in natural ecosystems.

In this project, I will work with leading scientists, a postdoc and a tech company partner to integrate the latest developments in remote sensing and biodiversity assessments for estimating current levels of biodiversity and carbon and for predicting the recovery potential of candidate restoration sites.

We will generate high-resolution carbon and biodiversity data from selected sites across 300,000 hectares in the mega-diverse but highly fragmented rainforest biomes of southeastern Brazil and southwestern Costa Rica, including various categories and stages of land use and ecosystem restoration.

The resulting data will be analysed within an interpretable artificial intelligence framework to: (i) identify fast, robust and cost-effective ways of measuring carbon and biodiversity at scale; (ii) test the relative importance of stochastic vs deterministic factors in predicting carbon sequestration and biodiversity recovery; and (iii) identify the best combination of restoration sites and recovery methodology to maximise biodiversity and carbon storage under a constrained budget.

The methodology developed here will facilitate hypothesis- and discovery-driven research in complex ecosystems while contributing to the delivery of international biodiversity targets and climate agreements.