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Functional diversity in plant communities: the role of environmental filters

Final Report Summary - DIVERSITRAITS (Functional diversity in plant communities: the role of environmental filters)

Project context and objectives

To answer many of the major questions in comparative botany, ecology and global change biology it is necessary to extrapolate across enormous geographic, temporal and taxonomic scales. Yet much ecological knowledge is still based on observations conducted within a local area or even a few hundred square meters. Understanding ecological patterns and how plants respond to global warming and human alteration of landscapes and ecosystems necessitates a holistic approach. Such an approach must be conducted at a scale that is commensurate with the breadth of the questions being asked. Further, it requires identification, retrieval and integration of diverse data from a global confederation of collaborating scientists across a broad range of disciplines.

The aim of the project is to understand the change in trait diversity (also called functional diversity) along major macro-ecological gradients and how it can explain the patterns of species diversity. The programme is divided into two parts:

- an eco-informatics part, where the goal is to gather botanical information and trait data at macro-ecological scales and analyse it in a biogeographical perspective;
- a theoretical part where the aim is to provide a theoretical framework to understand the variation of functional diversity along environmental gradients and predict the responses of organisms, communities and ecosystems to environmental changes via the use of plant functional traits.

Work performed

We assembled a large trait database for around 45 000 species and 25 core plant functional traits from existing worldwide datasets and a literature survey. The trait database, named DiversiTraits, is one of the largest integrated trait databases in the world. We also closely worked with the TRY initiative to merge the DiversiTraits and TRY databases. Ultimately, this will result in a database containing hundreds of traits for more than 70 000 species. As part of the BIEN initiative (a working group funded by the National Center for Ecological Analysis and Synthesis), we have brought together leading collectors and botanical survey and inventory data, informaticians and ecologists doing synthetic research across scales to integrate, for the first time, the most significant datasets of vegetation data spanning North and South America. This effort incorporated the most significant database resources for plant plot information and taxonomies.

Our efforts encompassed 20 million records of species occurrences. The result is the largest assembly of data on plant diversity and distribution yet created. Two years of effort was needed to incorporate heterogenous data from herbariums of different countries. We developed an ecoinformatics tool for this, called the Taxonomic Name Resolution Service (TNRS) to resolve taxonomic issues (misspellings, synonymy issues, etc.). In addition, we developed automatic procedures to correct geo-referenced data (note that half of the data has been corrected due to taxonomic or geo-reference errors). Currently, half of the species present in the botanical BIEN database has associated plant traits.

We have produced maps of functional traits by combining the species occurrence and trait information. This allows analysis of the variation of key plant functional traits (specific leaf area, leaf phosphorus content, leaf nitrogen content, seed mass, plant height, wood density) along latitudinal and climatic gradients. We analysed (woody and non-woody) plot-level data by combining species abundances and trait data and discovered that communities in tropical areas are functionally richer than those in temperate areas.

Main results

A central paradigm in ecology is that changes in species composition and dominance are linked to species traits that influence species performance and response in different environments. Recently, this paradigm has been extended in an effort to 'scale up' from traits to variation in ecosystem processes. However, a quantitative and predictive theory to scale from traits to communities and ecosystems has yet to emerge. Indeed, despite recent progress, it is not clear if species trait values contain more information about the forces that structure ecological communities than the species composition does. In addition, progress in trait-based ecology has been based on using species trait means and largely ignoring individual variation and the frequency of trait occurrence. To overcome these limits, we have reviewed and merged several lines of work to outline a general integrated theoretical framework termed 'trait driver theory' (TDT).

A unique strength of a TDT approach is that it is based upon individuals instead of taxa, and builds upon the evolutionary theory of population responses to changing environments. A central assumption is an optimum trait-to-phenotype mapping via organismal growth rates and the distribution of traits within the community. As a result, TDT makes mechanistic connections between phenotypes, organism performance, and the differing biotic and abiotic drivers that influence the shape and dynamics of community-level trait distributions. It uniquely predicts how the shape of the community trait distribution, as defined by the central moments, drives ecosystem functioning. Further, via TDT, differing ecological hypotheses can now be extended to make predictions for community structure and the functioning of ecosystems that contrast with predictions from prominent ecological theory based on species richness. Using a 140-year-old ecological experiment we assessed these differing predictions. We showed that empirical data is more consistent with TDT predictions, thus indicating that TDT offers a more general framework for beginning to develop a quantitative and predictive science of biodiversity and ecosystems.