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  • Mid-Term Report Summary - GEM-TRAIT (GEM-TRAIT: The Global Ecosystems Monitoring and Trait Study: a novel approach to quantifying the role of biodiversity in the functioning and future of tropical forests.)

GEM-TRAIT Report Summary

Project ID: 321121
Funded under: FP7-IDEAS-ERC
Country: United Kingdom

Mid-Term Report Summary - GEM-TRAIT (GEM-TRAIT: The Global Ecosystems Monitoring and Trait Study: a novel approach to quantifying the role of biodiversity in the functioning and future of tropical forests.)

Perhaps the most important questions in ecology and ecosystems science today focus on how communities of organisms respond to global environmental change and local anthropogenic pressure, and how such changes in community composition affect ecosystem properties and services. How does the biodiversity of these ecosystems affect their resilience to change, and how does the biodiversity respond to such change? This question is difficult to tackle because in most ecosystems we do not have the data to describe these relationships, and because of this lack of data we have also not sufficiently advanced our theory and models.
The GEM TRAITS project seeks to understand this relationship between tree diversity and ecosystem function in tropical forests worldwide, by collecting an original dataset and by making advances in theory and modelling. In the first phase of the project we have concentrated on consolidating a global network of tropical forest monitoring sites (the Global Ecosystem Monitoring network, GEM) in 20 field research sites across the tropics, across the Americas (Bolivia, Peru, Brazil, Belize), Africa (Ghana, Gabon, Ethiopia) and Asia (Malaysia). At each of these sites our local collaborators and research assistants measure ecosystem productivity at monthly and/or seasonal timesteps. Our original plan was to ensure continuous monitoring from May 2013 until December 2015. However, over the period mid-2015 to mid-2016 we are experiencing the strongest El Niño event for many decades, with high temperatures and string droughts being experienced by most of our sites. This provides us with a unique opportunity to assess the impacts of this event on the carbon cycle of the forests (and thereby also understand the relationships between tree diversity and ecosystem resilience to climate extreme events). Hence we are continuing monitoring until the end of 2016.
As well as determining ecosystem productivity at each of these sites, a key component of GEM-TRAIT was to determine the distribution of productivity and carbon cycling across the dominant tree species in each plot. This is necessary to relate the tree species diversity to the overall productivity. To do this we have been conducting intensive plant traits campaigns at a number of our tropical forest research sites. This involves an intensive effort by a field team of 5-25 people, spending many months in the field. Tree climbers collect tree branches, which are then measured for leaf respiration and photosynthesis and (in some sites) branch hydraulics, and the leaves are then processed for determining leaf physical traits, leaf chemistry and vein structure and anatomy. Since 2013, we have conducted such intensive plant traits collection campaigns in 65 plots in eight locations in four tropical continents, sampling 50 ha of forest, 3846 trees and more than 1180 tropical tree species. The countries sampled are Peru, Brazil, Ghana, Malaysia and Australia. This represents a phenomenal dataset, globally unique, and we anticipate will contribute major new insights to our scientific understanding in the next few years.

Our next step has been to integrate these two types of data and multiple field sites into a single specifically-designed database, which enables efficient and well-organised data analysis. This database is ready with our first Peru elevation gradient dataset (our first and most advanced dataset), and this year we are beginning to import data from the other field campaigns. By the end of this project we anticipate making the database available for the wider scientific community.
We have also been advancing on an exciting, remote sensing aspect of our project, where we explore the extent to which canopy leaf traits can be measured from airborne hyperspectral remote sensing. Coupled with the relationship between plant traits and ecosystem function that is the focus of this project, this will enable us to potentially map ecosystem processes at landscape and regional scale. We started this work in Peru, where airborne data where collected over our plots by our collaborator Dr Greg Asner. There we have demonstrated that it is possible to monitor leaf traits from the air, and are beginning to determine ecosystem productivity from these data. Dr Asner in currently collecting similar data from our plots in Malaysia, and we will initiate a similar study there. Finally, in our sites in Ghana we have recently explore the potential of drone-borne hyperspectral remote sensing, which may be a lost-cost alternative.
The modelling and theory component of the project is also advancing. We have applied our plant traits model, the Traits Forest Simulator, to our Peru dataset and shown how knowledge of traits alone (without knowing anything about climate, soils and forest structure) can enable us to predict ecosystem productivity. In the second phase of this project we will apply TFS to our other tropical gradients, in particular the forest-savanna transition in Ghana and the logging disturbance gradient in Malaysia. This modelling work is also informing our theory development. We are working on a theory paper based on our Peru dataset, and will seek to expand and apply it to our global dataset by the end of this project.
Overall, the project is going extremely well. We have collected a large dataset in our first phase (larger than we originally planned), have been able to take advantage of the research opportunity that the El Niño has provided (something that was not in the original proposal but that is likely to lead to high impact papers), and are making good progress on databasing, remote sensing analyses, modelling and theory development.

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United Kingdom
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