Amazon forests are global epicentres of biological diversity, being home to 15,000 different tree species. These have a large number of functional traits which underpin plant ecological strategies, positioning them along the fundamental axes of growth, survival and reproduction1. In recent years, more functional trait data have become available for Amazon tree species, including leaf, seed, and wood traits, and physiological traits associated with resistance to heat and drought climatic stressors. This growing database of plant functional traits has the potential to yield substantial new insights into the structure and function of Amazonian forest communities, but has been little explored.
Although patterns of taxonomic diversity across the Amazon are documented2, the functional richness and diversity of its forests is poorly known. A recent study along an elevational gradient in Peru found that functional richness and diversity are important predictors of carbon cycling. However, the role of functional diversity in controlling forest processes over large spatial scales has not been evaluated. Community-level functional diversity may also enhance the resilience of forests to climate stress. In temperate forests, for example, community diversity in hydraulic traits has been shown to buffer forests against drought3. While functional diversity enhances tropical forest resilience in model simulations4, data-based investigations into the strength of this regulatory effect are lacking.
Meanwhile, complementary approaches, including long-term measurements of forests on the ground with permanent plots and from space with remote sensing are generating ever richer pictures of forest variability in time and space. If combined and integrated with functional trait and diversity perspectives and potentially integrated via modelling, these now offer potential for more powerful insights into forest function and their sensitivity to climate change. The future for tropical ecology and global change science is likely to lie at the interface of different techniques and approaches like these.
This project represents an exciting opportunity to advance our knowledge of how functional diversity is patterned across Amazonia and how this interacts with ecosystem functioning and resilience at a large scale. The volume of data available and the breadth of expertise in the supervisory team make the project very flexible, with the specific direction ultimately determined by the interests of the student. The studentship could involve a combination of the following:
- Quantifying large-scale patterns in community functional diversity for Amazon forests. This will involve reconciling datasets of species-level functional traits with data on forest community composition to map single and multi-trait diversity metrics at a biome scale. This would allow a first appreciation of the basin-wide inter-relationships between different groups of traits – e.g. leaf morphological traits vs. wood traits vs. hydraulic traits.
- Examining the ability of functional diversity vs. other descriptors of diversity (e.g. taxonomic diversity), as well as remotely sensed estimates of leaf area, hydraulic stress or photosynthetic function, as predictors of ecosystem processes in the Amazon (e.g. wood production, biomass storage).
- Exploring the role of functional diversity in conferring resilience to drought. This may involve the use of an individual-based forest simulator5 now updated to include a mechanistic description of water transport through plants, parameterised with data from Amazon forests.
Potential for High Impact Work
The questions addressed are fundamental within tropical forest ecology and key for understanding the impacts of global environmental change on forest function and composition. Each of the objectives described above has the potential to yield high-impact publications which could significantly shape the field. The project is designed to include opportunities to integrate remote sensing and individual modelling approaches with our rich long-term forest dynamic and trait datasets.
Training and Supervision
The student will work with Oliver Phillips, David Galbraith and Emanuel Gloor in the School of Geography. This project builds on data being collated via a NERC grant (ARBOLES: A trait based understanding of LATAM forest biodiversity and resilience) led by the PIs. ARBOLES involves multiple partners in the UK and South America and other projects led by the supervisors, including the RAINFOR (Amazon Forest Inventory Network) collaboration, and new experimental work. It thus offers the student the possibility to build research ties with a wide range of scientists at different career stages.
You will have access to large datasets of forest traits and dynamics from across the Amazon. Training will include management and analysis of large datasets and individual-based ecosystem modelling. There may also be the opportunity for new Amazon fieldwork. You will join the Ecology and Global Change cluster, a dynamic and world-leading research group focusing on tropical forest ecology and response to global change.
We welcome motivated students with a keen interested in tropical forest ecology. Given the analytical focus, those with strong quantitative skills are especially encouraged to apply.
- Diaz S, et al. 2015. The global spectrum of plant form and function. Nature 529:167-171.
- ter Steege H, et al. 2013. Hyperdominance in the Amazonian tree flora. Science 342:1243092.
- Anderegg W., et al. 2018. Hydraulic diversity regulates ecosystem resilience during drought. Nature561:538-541.
- Levine N, et al. 2016. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. PNAS 113:793-797.
- Fyllas et al. 2014. Analysing Amazonian productivity using a new individual and trait-based model. Geoscientific Model Development 7:1251-1269.