Biodiversity Co-benefits of Large Scale Restoration in the Brazilian Amazon
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences
Abstract
Tropical forest restoration is critical for mitigating climate change and reversing biodiversity loss. However, monitoring biodiversity recovery remains a significant challenge due to the complexity of ecosystems and limitations of traditional survey methods. This research investigates biodiversity recovery in large-scale tropical forest restoration projects in the Brazilian Amazon, examining how taxa and ecological communities respond to reforestation over time. It will assess the influence of landscape structure, environmental variables, and restoration practices on recovery trajectories using both traditional field methods and innovative monitoring technologies.
The first chapter will outline a monitoring framework for biodiversity recovery, integrating methods such as acoustic monitoring, environmental DNA (eDNA), camera traps, and satellite imagery. The second chapter will explore baseline biodiversity patterns in degraded Amazon landscapes, identifying key drivers-both anthropogenic and natural-that shape these patterns. In the third chapter, I will examine recovery trajectories, focusing on community integrity, functional richness, and vegetation structure across restoration sites. The fourth chapter will simulate recovery potential by creating synthetic "positive control" sites to represent the climax community a restored site could achieve, aiding in the development of methods to set restoration success targets.
By integrating these diverse data sources, the project will assess biodiversity responses across restoration sites, primary forests, and degraded landscapes. The outcomes will contribute to evidence-based restoration strategies and explore scalable, repeatable, and cost-effective approaches to monitoring biodiversity recovery.
This research will offer critical insights into large-scale forest restoration, equipping conservation practitioners and policymakers with tools to guide effective biodiversity recovery efforts.
The first chapter will outline a monitoring framework for biodiversity recovery, integrating methods such as acoustic monitoring, environmental DNA (eDNA), camera traps, and satellite imagery. The second chapter will explore baseline biodiversity patterns in degraded Amazon landscapes, identifying key drivers-both anthropogenic and natural-that shape these patterns. In the third chapter, I will examine recovery trajectories, focusing on community integrity, functional richness, and vegetation structure across restoration sites. The fourth chapter will simulate recovery potential by creating synthetic "positive control" sites to represent the climax community a restored site could achieve, aiding in the development of methods to set restoration success targets.
By integrating these diverse data sources, the project will assess biodiversity responses across restoration sites, primary forests, and degraded landscapes. The outcomes will contribute to evidence-based restoration strategies and explore scalable, repeatable, and cost-effective approaches to monitoring biodiversity recovery.
This research will offer critical insights into large-scale forest restoration, equipping conservation practitioners and policymakers with tools to guide effective biodiversity recovery efforts.
People |
ORCID iD |
| Rhys Preston-Allen (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/S007415/1 | 30/09/2019 | 29/09/2028 | |||
| 2892586 | Studentship | NE/S007415/1 | 30/09/2023 | 30/03/2027 | Rhys Preston-Allen |