Predictive Understanding of Global Biodiversity Dynamics (PUGBD)
Main funder
Funder's project number: 336212
Funds granted by main funder (€)
- 798 750,00
Funding program
Project timetable
Project start date: 01/09/2021
Project end date: 31/08/2026
Summary
Recent research efforts have resulted in major progress in the theoretical foundations of community ecology, the build-up of vast
databases on species, traits and phylogenetic relationships, and the development of statistical approaches that allow linking ecological
data to ecological theory more directly than was possible before. In spite of these fundamental developments, we still have a very poor
understanding of global biodiversity, both of its current distribution, and of the drivers behind it. The reason for this is a combination of
two factors: extant data are partial and biased, and at the same time the processes underlying biodiversity dynamics are highly complex.
The overarching aim of this proposal is to turn community ecology from a descriptive into a predictive science. In particular, we aim to
achieve a greatly revised understanding of global biodiversity, including its current distribution, the drivers of its dynamics, and how it
can be expected to change due to the ongoing global changes. The research programme covers the entire spectrum from sampling
globally relevant ecological data to developing and applying bioinformatics and statistical methods for generating ecological
knowledge. First, we will generate unprecedented time-series data for fungi, arthropods, birds and mammals, at spatial scales from
100m up to a global scale. Second, we will resolve the challenge of how to reliably identify taxa from massive amounts of new data
originating from automated survey methods, in particular DNA barcoding, autonomous audio recording and camera-trapping. Third, we
will use joint species distribution modelling to turn the new global biodiversity data into a predictive understanding of global
biodiversity dynamics, including the identification of underlying environmental drivers, and the prediction of how global biodiversity
dynamics are expected to respond to environmental change. Fourth, we will use both theoretical and experimental approaches to
critically evaluate the limitations of observational data on species abundances (combined with environmental covariates, species traits,
and phylogenetic relationships) for inferring the processes underlying biodiversity dynamics. From the applied point of view, this
project has the potential to revolutionize biomonitoring by demonstrating that it is feasible to move from indicator approaches to
comprehensive global monitoring of entire species communities.
databases on species, traits and phylogenetic relationships, and the development of statistical approaches that allow linking ecological
data to ecological theory more directly than was possible before. In spite of these fundamental developments, we still have a very poor
understanding of global biodiversity, both of its current distribution, and of the drivers behind it. The reason for this is a combination of
two factors: extant data are partial and biased, and at the same time the processes underlying biodiversity dynamics are highly complex.
The overarching aim of this proposal is to turn community ecology from a descriptive into a predictive science. In particular, we aim to
achieve a greatly revised understanding of global biodiversity, including its current distribution, the drivers of its dynamics, and how it
can be expected to change due to the ongoing global changes. The research programme covers the entire spectrum from sampling
globally relevant ecological data to developing and applying bioinformatics and statistical methods for generating ecological
knowledge. First, we will generate unprecedented time-series data for fungi, arthropods, birds and mammals, at spatial scales from
100m up to a global scale. Second, we will resolve the challenge of how to reliably identify taxa from massive amounts of new data
originating from automated survey methods, in particular DNA barcoding, autonomous audio recording and camera-trapping. Third, we
will use joint species distribution modelling to turn the new global biodiversity data into a predictive understanding of global
biodiversity dynamics, including the identification of underlying environmental drivers, and the prediction of how global biodiversity
dynamics are expected to respond to environmental change. Fourth, we will use both theoretical and experimental approaches to
critically evaluate the limitations of observational data on species abundances (combined with environmental covariates, species traits,
and phylogenetic relationships) for inferring the processes underlying biodiversity dynamics. From the applied point of view, this
project has the potential to revolutionize biomonitoring by demonstrating that it is feasible to move from indicator approaches to
comprehensive global monitoring of entire species communities.
Principal Investigator
Primary responsible unit
Related publications and other outputs
1 of 2
- Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting (2024) Rahman, Anis Ur; et al.; A1; OA
- Airborne DNA reveals predictable spatial and seasonal dynamics of fungi (2024) Abrego, Nerea; et al.; A1; OA
- A Mobile Application–Based Citizen Science Product to Compile Bird Observations (2024) Nokelainen, Ossi; et al.; A1; OA
- Beyond species richness : Forest structure and edaphic conditions have similar importance but different effects on multi-taxon biodiversity (2024) Kepfer-Rojas, Sebastian; et al.; A1; OA
- Experimental evidence that root‐associated fungi improve plant growth at high altitude (2024) Burg, Skylar; et al.; A1; OA
- Fungal trait‐environment relationships in wood‐inhabiting communities of boreal forest patches (2024) Dawson, Samantha K.; et al.; A1; OA
- Global Spore Sampling Project : A global, standardized dataset of airborne fungal DNA (2024) Ovaskainen, Otso; et al.; A1; OA
- Natural deadwood hosts more diverse pioneering wood‐inhabiting fungal communities than restored deadwood (2024) Saine, Sonja; et al.; A1; OA
- Novel community data in ecology : properties and prospects (2024) Hartig, Florian; et al.; A2; OA
- Recommendations for quantitative uncertainty consideration in ecology and evolution (2024) Simmonds, Emily G.; et al.; A2; OA
