Active Plants, Animals & Ecology Food & Agriculture

Brazil-UKRI: The recovery of the adaptive capacity of Pre-Columbian tree crops to environmental changes

In plain English

AI plain-English summary

Brazil nut trees growing on ancient Amazonian archaeological sites still carry genetic traces of the people who cultivated them centuries ago, and researchers plan to read those genomes to find out how the trees recovered their ability to adapt after domestication ended. Forest restoration efforts often fail because they ignore two things: how human disturbance reshapes a species’ genetic diversity, and what happens to ecosystems over the long term—decades or centuries—after people leave. This project fills that gap by building a 2,000-year timeline of abandoned Pre-Columbian settlements, known as Terras Pretas Amazônicas, where descendants of ancient Brazil nut trees still grow. By sequencing the trees’ whole genomes and their associated soil microbiomes along that timeline, the team will identify which genetic functions boost adaptive potential. If successful, the research will pinpoint individual trees whose genomes carry enhanced resilience. Propagules from those trees could then be used in tropical forest restoration and agriculture, making replanted forests more resistant to ongoing global changes. The work is fundamental science with a direct practical route: better seeds for a more resilient Amazon.

View original technical description
Abstract Multiple large-scale forest restoration strategies are emerging globally to counteract ecosystems degradation and biodiversity loss. However, these strategies often remain insufficient to offset the loss caused by anthropogenic development. At least two reasons could explain this incomplete performance: i) we ignore how human disturbance affects species genetic variability and their potential to evolve and adapt to the ongoing global changes; ii) there is a major gap in the knowledge about long-term (>100 years) ecosystem dynamics after human disturbance ends. In this project, we propose to investigate the adaptative potential of the Brazil nut and other Amazonian tree crops associated with Brazil nut areas, after anthropic disturbance cessation. We will sample plant leaf and cambium tissue and roots on Pre-Columbian archaeological sites, today known as Terras Pretas Amazônicas (TPA), where the descendants of ancient Brazilian nut trees still grow today. With selected TPA sites sequentially abandoned that have never been reoccupied, we will build a 2,000-year chronosequence. This chronosequence will allow us understand how the Brazilian nut trees and associated Amazonian tree crops recover their adaptive potential after they are released from domestication after Pre-Columbian peoples sequentially abandoned their lands to finally collapse around the XV century with the Spanish invasion. Our team that includes experts in forest restoration, domestication, and genomics will explore changes in the whole genome of the Brazilian nut tree and associated tree crops, as well as its associated soil microbiome, along the chronosequence. The results will help find genomes with increased genetic variability and thus adaptive potential, by identifying specific functions related to an enhanced adaptive potential. Propagules from individuals with these functions can then be used in tropical forest restoration, and agriculture, increasing the resilience and resistance of forests to ongoing global changes.

View the original record at the funder ↗

Researchers

David Moreno Mateos (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Hook a Worm to Catch a Man: Tracking Historical and Recent Human Settlement, Land use & Migration in Neotropical Rainforests using Ecosystem Engineers
Comparative Legacies of Human Land Use in the Brazilian Atlantic Forest
BIOmes of Brasil - Resilience, rEcovery, and Diversity: BIO-RED
Niche evolution of South American trees and its consequences
Biodiversity and ecosystem functioning in degraded and recovering Amazonian and Atlantic forests

Original classification

Unknown

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.