Global biogeography of ecological networks in forest ecosystems


ANR : 2017-2020


Wilfried THUILLER - CNRS LECA, Grenoble - France

Scientific Partnership

  • EDB, CNRS - Toulouse - France
  • ENS, Paris - France
  • LAMA, CNRS - Chambéry - France

For UMR EcoFog :

Presentation of the project

A major challenge for ecologists is to understand and predict the ecological consequences of climate change, land use change and disturbances. To meet this challenge, we need to account not only for environmental change effects on species performances and ranges but also for effects on species interactions. The alterations of species interactions are likely to create cascading effects that can result in non-linear responses, potentially leading to critical and irreversible transitions of ecosystems at short time scales but also over large spatial scales. The assumption that species interactions are only important at small spatial scales has indeed generated considerable debate. Until recently the prevailing idea was that biotic assembly processes (e.g. interspecific competition and trophic interactions) were only important at small spatial scales. Conceptual work and microcosms experiments early challenged this assumption, which has been strengthened by recent studies empirically demonstrating the importance of biotic interactions up to continental scales. It has been argued that determining the direction and magnitude of global change impacts on species interactions remains one of the greatest challenges for forecasting community and ecosystem dynamics.
The main objective of the GlobNets project is thus to decipher multi-trophic assemblages at biogeographic scales and to understand their responses to spatial segregation, environmental gradients and/or human activities. To do so, GlobNets builds on new mathematical developments and environmental DNA metabarcoding. We will collect an unprecedented multi-trophic assemblage dataset of soil-plant biodiversity that covers the three super-kingdoms of life (Eukaryota, Bacteria and Archaea) across multiple forest plots along gradients of climate and land-use pressure in thirteen distinct forest sites around the globe (tropical, temperate and boreal forests). GlobNets will address the following objectives:

  1. Develop publicly available multi-scale, multi-trophic and standardized data comprising sampled sites from major forest biomes of the world that contain information on species and functional group co- occurrences from the whole tree of life. In each sampled site, samples are replicated along environmental or disturbance gradients.
  2. Develop new mathematical and statistical tools for the analyses of multi-trophic community data from eDNA that allow for unbiased within, between and overall community diversity estimates (i.e. α, β and γ components) and for an approximation of interaction probabilities within and across trophic levels.
  3. Based on I and II, map and describe the distribution of forest soil and plant diversity across biomes and test for which trophic levels the latitudinal diversity gradient hypothesis holds.
  4. Analyse the response of forest soil and plant diversity to large-scale climate and regional-scale environmental and disturbance gradients, detect co-variation between trophic levels as well as between above and belowground compartments
  5. Based on a suitable sub-set of the dataset, provide a decomposition of diversity into α, β, and γ components and test long-standing ecological hypotheses related to disturbance and stress gradients across climatic regions, specific abiotic drivers and trophic levels.
  6. Based on the methods developed in II, conduct the first global biogeographical description of soil-based co-occurrence networks for a major ecosystem (i.e. forest) including members from the whole tree of life (i.e. Eukaryota, Bacteria and Archaea).
  7. Based on a suitable subset of the data (i.e. including those interaction partners that are identified with enough certainty), investigate how strongly network complexity and modularity are influenced by large-scale climatic filters and regional-scale environmental and disturbance filters. Finally, provide a biogeographical description of network robustness based on simulations of cascading species extinctions

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