Periodic Reporting for period 2 - CLIMB (Calibrating and Improving Mechanistic models of Biodiversity)
Okres sprawozdawczy: 2021-10-01 do 2022-09-30
The ever more apparent impact of human actions on biodiversity in recent decades has led to an international scientific and political effort to measure, preserve and manage biodiversity (e.g. IPBES, EU’s Horizon 2020 program). To achieve this objective, we need a better understanding of the ecological processes that determine natural communities and we need appropriate modelling tools to make accurate predictions about the distribution and dynamic of biodiversity in the face of anthropogenic pressures.
To do so, scientists currently favour correlative statistical methods (e.g. direct relating of species abundance along climatic gradients) instead of process-based approaches that take explicitly into account how climatic gradients influence species demography and interactions and how the latter determine species abundances. The reason for this preference is not so much the conceptual superiority of correlative methods but rather because they couple easily with the available biodiversity data while process-based approaches, which are in principle better suited to estimate local diversity and biodiversity dynamics are currently more challenging to link to the available biodiversity data. Indeed, to calibrate process-based models, we need to measure experimentally a large quantity of demographic features and biotic interactions among the target species. As this is both complicated and costly, this approach has often been limited to simplified experimental settings, thus strongly limiting their use for ambitious biodiversity modelling projects.
This project (CLIMB) proposes a way forward to overcome the limitations of process-based approaches: it proposes that the demographic features and biotic interactions species could be approximated by functional traits. Functional traits are quantifiable traits of organisms that are easily measurable (for instance plant height or plant leaf area) and they have been shown to be directly or indirectly linked to species demography and biotic interactions.
More precisely, CLIMB aims to develop and test a general statistical procedure that will infer the demographic features of species from known functional trait data to best predict the observed structure of biodiversity. This approach will shed light on some of the oldest questions of ecology, but also explore a new route to connect process-based biodiversity models with functional trait data and thus provide much-needed tools for answering pressing issues in biodiversity policy.
To do so, scientists currently favour correlative statistical methods (e.g. direct relating of species abundance along climatic gradients) instead of process-based approaches that take explicitly into account how climatic gradients influence species demography and interactions and how the latter determine species abundances. The reason for this preference is not so much the conceptual superiority of correlative methods but rather because they couple easily with the available biodiversity data while process-based approaches, which are in principle better suited to estimate local diversity and biodiversity dynamics are currently more challenging to link to the available biodiversity data. Indeed, to calibrate process-based models, we need to measure experimentally a large quantity of demographic features and biotic interactions among the target species. As this is both complicated and costly, this approach has often been limited to simplified experimental settings, thus strongly limiting their use for ambitious biodiversity modelling projects.
This project (CLIMB) proposes a way forward to overcome the limitations of process-based approaches: it proposes that the demographic features and biotic interactions species could be approximated by functional traits. Functional traits are quantifiable traits of organisms that are easily measurable (for instance plant height or plant leaf area) and they have been shown to be directly or indirectly linked to species demography and biotic interactions.
More precisely, CLIMB aims to develop and test a general statistical procedure that will infer the demographic features of species from known functional trait data to best predict the observed structure of biodiversity. This approach will shed light on some of the oldest questions of ecology, but also explore a new route to connect process-based biodiversity models with functional trait data and thus provide much-needed tools for answering pressing issues in biodiversity policy.
Until now, I have produced the following results for the CLIMB project.
First, I have a theoretical study about competition among plants. In more details, I have formulated a mathematical model to link competition to functional traits. I have studied that model in detail and link it to community-level properties of plant communities. For instance, the diversity of community and their stability.
Second, I have organized and realized a data collection field campaign to collect functional traits of thirty seven tussock grassland species. I have focused on leaf and root functional traits. This data will help me to understand the dynamic of tussock grasslands at different elevation following land abandonment.
Third, I have realized statistical developments to better operate my methodology in a Bayesian framework. Across two published articles, this has shown how our new method offers a technical solution to bridge the divide between trait data and process-based models in species-rich ecosystems.
Fourth, I have started to pre-analyze two grasslands datasets as I plan to analyze them in the near future with my new statistical framework. In the meantime, the analysis of the grassland of the Bavarian Mountains have led to a collaboration with S. Rosbakh. In this pre-study, I analyzed how plant seed characteristics and germination traits influence the structure of mountain grassland communities along an elevation gradient.
First, I have a theoretical study about competition among plants. In more details, I have formulated a mathematical model to link competition to functional traits. I have studied that model in detail and link it to community-level properties of plant communities. For instance, the diversity of community and their stability.
Second, I have organized and realized a data collection field campaign to collect functional traits of thirty seven tussock grassland species. I have focused on leaf and root functional traits. This data will help me to understand the dynamic of tussock grasslands at different elevation following land abandonment.
Third, I have realized statistical developments to better operate my methodology in a Bayesian framework. Across two published articles, this has shown how our new method offers a technical solution to bridge the divide between trait data and process-based models in species-rich ecosystems.
Fourth, I have started to pre-analyze two grasslands datasets as I plan to analyze them in the near future with my new statistical framework. In the meantime, the analysis of the grassland of the Bavarian Mountains have led to a collaboration with S. Rosbakh. In this pre-study, I analyzed how plant seed characteristics and germination traits influence the structure of mountain grassland communities along an elevation gradient.
With this project, we have proven the value of functional trait data to calibrate process-based community models to study high-diversity plant communities. A large part of this project until now has been used to solidify the theoretical and statistical foundations of this new modeling approach.
Future research for the end of this project will aim at finishing and publishing the analysis of the grassland datasets that were constituted to produce the first application of the new method technics to study ecological processes.
This use of functional trait data and the statistical tools that I built around it are completely novel in ecology research. The CLIMB project has now defined a whole new line of research in community modeling that stands at the intersection of theoretical community ecology, functional ecology and biodiversity modeling. Most importantly, it shows that with functional trait data and the right statistical tools, one can operate complex process-based community models to study real-life species-rich ecosystems. In the end, our framework approaches the same function as correlative biodiversity models while also offering a deeper understanding of the ecological processes that structure natural communities.
Future research for the end of this project will aim at finishing and publishing the analysis of the grassland datasets that were constituted to produce the first application of the new method technics to study ecological processes.
This use of functional trait data and the statistical tools that I built around it are completely novel in ecology research. The CLIMB project has now defined a whole new line of research in community modeling that stands at the intersection of theoretical community ecology, functional ecology and biodiversity modeling. Most importantly, it shows that with functional trait data and the right statistical tools, one can operate complex process-based community models to study real-life species-rich ecosystems. In the end, our framework approaches the same function as correlative biodiversity models while also offering a deeper understanding of the ecological processes that structure natural communities.